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An investigation into the antecedents and outcomes of the sorting on Twitter's algorithm.

“It is not from the benevolence of the butcher, the brewer, or the baker,that we expect our dinner, but from their regard for their own interest.”Each participant in a competitive economy is “led by an invisible hand topromote an end which was no part of his intention.” Adam Smith, 1776.With his treatise, The Wealth of Nations, 1776, Adam Smith initiated the field of economics,and his famous quote provided this field with its central guiding principle. The pioneeringwork of Walras (1874) gave a mathematical formulation for this statement, using his notion ofmarket equilibrium, and opened up the possibility of a formal ratification. Mathematical ratification came with the celebrated Arrow-Debreu Theorem (1954), whichestablished existence of equilibrium in a very general model of the economy; however, an efficient mechanism for finding an equilibrium has remained elusive.The question of algorithmic ratification was taken up in the earnest within theoretical computer sciencea decade ago, and attention soon gravitated on markets under piecewise-linear, concave utility functions.

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The talk will consist of two parts. In the first part, I will present prior work aimed at developing new algorithms for materials science applications. I developed a novel numerical method to optimize the shape of mechanical parts and maximize their strength. This optimization problem was extremely challenging as it involved changing the topology and therefore classical optimization methods do not apply. A similar idea can be applied to model shape memory alloys and plasticity in metals. In this project, the optimization led to the construction of a tree of laminated microstructures. Constraints from mechanical equilibrium were used to construct an adaptive tree and to determine the optimal number of levels. The second part involves more recent work. I will present the implementation of a new heat flux algorithm to understand failures in micro-devices in LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator); LAMMPS is a high performance code which was benchmarked on Blue Gene L with 90% parallel efficiency. Also I am currently extending the capabilities of ParaDiS (Parallel Dislocation Simulator), another high performance code which has run on 135k processors of Blue Gene L. ©2008 Microsoft Corporation. All rights reserved.

In this dissertation, a series of studies are designed to investigate the brain connectivity patterns (both functional connectivity and effective connectivity), comparing subjects with depression history vs. control and subjects with OCD vs. control. Depression and OCD are examples of trans-diagnostic disorders, which may be more likely due to alteration of connectivity patterns (ex., hyper-connectivity in default mode network or other networks) rather than localized brain abnormality.

Wiki - List of Open Problems in Sublinear Algorithms

The purpose of this study was to ascertain whether children in grade 3 who differ in cognitive processing capacity add and subtract differently. The researchers drew upon information from three sources: individual results from a battery of 14 tests, an objective-referenced achievement test measuring a variety of arithmetic skills related to addition and subtraction, and coded strategy data for a set of verbal addition and subtraction problems administered in an interview setting. Eleven children starting third grade in Tasmania, Australia were involved in the study. The data suggested two notions: (1) there is a group of children who have the capacity to reason about quantitative problems, know the basic procedures of addition and subtraction, but see little reason to use those algorithmic procedures to find answers to verbal problems; (2) there is a second group of children whose capacity to reason about quantitative problems is suspect: they do not know the basic procedures to addition and subtraction and have not acquired other skills like modeling or counting which would help them solve verbal problems. It is suggested that educators need to reexamine the relationships between the algorithm and its application. (MNS)

Consider an airlift operation which consists of several routes, each having missions which are subject to given time schedules. The aircraft are manned with aircrews that are required to rest for a certain period of time after each leg of a mission. A mission may be continued whenever a rested aircrew is available at the location. Given the number of missions that are needed to be flown on the different routes, and given the schedule timetable that is associated with those missions, we consider the problems: (1) What is the minimum number of crews that are needed to maintain the operation? (2) How many aircrews are needed to be staged at each location? (3) If the number of available aircrews is less than the minimum needed, which legs of what missions may be delayed so that the minimum required number of aircrews is reduced? We will exclude from the analysis the trivial case where the rest period of the aircrews is always less than the period of time for which the aircrafts are delayed. If this is the case, then it is clear that the minimum number of aircrews is equal to the number of missions and all the aircrews must be staged initially at the home base.

https://www.theverge.com/2020/4/27/21238229/algorithms-supply-chain-model-pandemic-disruption-amazon-walmart Issues with supply chain algorithms may teach lessons about cybersecurity. Matt Keyser, Jim Clausing and Manny Ortiz of the AT&T Chief Security Office discuss the week's top cybersecurity news, and share news on the current trends of malware, spam, and internet anomalies observed on the AT&T Network. Originally recorded on April 28, 2020 AT&T ThreatTraq welcomes your e-mail questions and feedback at [email protected]

Algorithms are increasingly implemented in the criminal justice system to assist and support judges especially in decisions requiring a prognosis (e.g., parole decisions). The aim of these algorithmic support systems is to assist but also to avoid biases and heuristics in the decision process. The aim of the present study is to explore the impact of algorithmic as well as legal human experts’ recommendations on judicial decisions in the context of parole decisions because these judgments require a prognosis of recidivism. Trainee lawyers are presented ten different (pretested) fictional cases. The cases have a high complexity because they contain mixed information (in favor, in disfavor, and irrelevant information) about the defendant. The trainee lawyers’ task is to review all ten cases and to make a parole decision, to assess the risk of recidivism, and to self-asses their confidence in their decision. In three recommendation conditions manipulated between the trainee lawyer, they receive a recommendation on how to decide on the question of parole in form of a risk score. This risk score is allegedly either provided by a supervision group of experienced judges (legal human experts’ recommendation) or by an algorithmic system that presumably bases its recommendation on data of comparable past cases (algorithmic recommendation). For the purpose of the study, the risk score is chosen randomly so that it is perceived as more realistic. We investigate the impact of these recommendations on judicial decisions by comparing the recommendation conditions (legal human experts vs. algorithm) to a control condition in which the trainee lawyers do not receive a risk score as a recommendation (i.e., baseline condition).

The linear programming relaxation of the minimum vertex coloring problem, called the fractional coloring problem, is NP-hard. We describe efficient approximation procedures for both the weighted and unweighted versions of the problem. These fractional coloring procedures are then used for generating upper bounds for the (weighted or unweighted) maximum clique problem in the framework of a branch and bound procedure. Extensive computational testing shows that replacing the standard upper bounding procedures based on various integer coloring heuristics with the somewhat more expensive fractional coloring procedure results in improvements of the bound by up to one fourth in the unweighted and up to one fifth in the weighted case, accompanied by a decrease in the size of the search tree by a factor of almost two.

Cognitive radio is an enabling technology for opportunistic spectrum access. Spectrum sensing is a key feature of a cognitive radio whereby a secondary user can identify and utilize the spectrum that remains unused by the licensed (primary) users. Among the recently proposed algorithms the covariance-based method is a constant false alarm rate (CFAR) detector with a fairly low computational complexity. The low computational complexity reduces the detection time and improves the radio agility. In this paper, we present a framework to analyze the performance of this covariance-based method. We also propose a new spectrum sensing technique based on the sample autocorrelation of the received signal. The performance of this algorithm is also evaluated through analysis and simulation. The results obtained from simulation and analysis are very close and verify the accuracy of the approximation assumptions in our analysis. Furthermore, our results show that our proposed algorithm outperforms others.

In this study, we describe the algebraic computations required to implement the stochastic finite element method for solving problems in which uncertainty is restricted to right hand side data coming from forcing functions or boundary conditions. We show that the solution can be represented in a compact outer product form which leads to efficiencies in both work and sorage, and we demonstrate that block iterative methods for algebraic systems with multiple right hand sides can be used to advantage to compute this solution. Finally, we examine the behavior of these statistical quantities in one setting derived from a model of acoustic scattering.

Experiment in which humans are used to provide feedback to an algorithm used to give recommendations about which social support applications, submitted by immigrant and locals, are approved. The algorithm is varied to be neutral, biased towards immigrants, or biased towards locals. The aim is to test whether these humans in the loop create rather than mitigate bias in algorithms based on their political orientation (democrat voters favoring benefits to immigrants more than republican voters; republican voters favoring benefits to locals more than republican voters).

The bottleneck in many applications that process massive amounts of data is the I/O communications between internal memory and external memory. The bottleneck is accentuated as processors get faster and parallel processors are used. Parallel disk arrays are often used to increase the I/O bandwidth. The goal of this proposal is to deepen our understanding of the limits of I/O systems and to construct external memory algorithms that are provably efficient. The three measures of performance are number of I/Os, disk storage space, and CPU time. Even when the data fit entirely in memory, communication can still be the bottleneck, and the related issues of caching become important. Theoretical work involves development and analysis of provably efficient external memory algorithms and cache-efficient algorithms for a variety of important application areas. We address several batched and on-line problems, involving text databases, prefetching and streaming data from parallel disks, and database selectivity estimation. Our experimental validation uses our TPIE programming environment. Plans for the coming year are to address bottleneck issues in parallel disks, text databases, and XML databases.

This paper examines the correspondence between two approaches to astrometric observational reductions: the approach based on angular observables used for optical observations, and the approach based on the interferometric delay observable used for very long baseline radio interferometry (VLBI) observations. Specifically, of interest here is the group of algorithms that have become standard in accounting for the physical effects traditionally called annual and diurnal aberration and gravitational light bending. These algorithms are important because they must be applied to all wide-angle astrometric measurements, whether ground-or space-based, and regardless of the distance of the objects observed.

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The programming and analysis methods to support the development of a thermal model of the Earth's surface from detailed analysis of day/night registered data sets from the Heat Capacity Mapping Mission satellite are briefly described.

The analysis and design of complex multivariable reliable control systems are considered. High performance and fault tolerant aircraft systems are the objectives. A preliminary feasibility study of the design of a lateral control system for a VTOL aircraft that is to land on a DD963 class destroyer under high sea state conditions is provided. Progress in the following areas is summarized: (1) VTOL control system design studies; (2) robust multivariable control system synthesis; (3) adaptive control systems; (4) failure detection algorithms; and (5) fault tolerant optimal control theory.

The construction of a reliable, shock capturing finite difference method to solve the Euler equations for inviscid, supersonic flow past fighter and missile type configurations is highly desirable. The numerical method must have a firm theoretical foundation and must be robust and efficient. It should be able to treat subsonic pockets in a predominantly supersonic flow. The method must also be easily applicable to the complex topologies of the aerodynamic configuration under consideration. The ongoing approach to this task is described and for steady supersonic flows is presented. This scheme is the basic numerical method. Results of work obtained during previous years are presented.

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

In this report, we demonstrate that bottom-up IPA's, image-processing algorithms, can perform a new visual task to select and locate Regions-Of-Interests (ROIs). This task has been defined on the basis of a theory of top-down human vision, the scanpath theory. Further, using measures, Sp and Ss, the similarity of location and ordering, respectively, developed over the years in studying human perception and the active looking role of eye movements, we could quantify the efficient and efficacious manner that IPAs can imitate human vision in located ROIS. The means to quantitatively evaluate IPA performance has been an important part of our study. In fact, these measures were essential in choosing from the initial wide variety of IPAS, that particular one that best serves for a type of picture and for a required task. It should be emphasized that the selection of efficient IPAs has depended upon their correlation with actual human chosen ROIs for the same type of picture and for the same required task accomplishment.

We present a convergence theory for pattern search methods for solving bound constrained nonlinear programs. The analysis relies on the abstract structure of pattern search methods and an understanding of how the pattern interacts with the bound constraints. This analysis makes it possible to develop pattern search methods for bound constrained problems while only slightly restricting the flexibility present in pattern search methods for unconstrained problems. We prove global convergence despite the fact that pattern search methods do not have explicit information concerning the gradient and its projection onto the feasible region and consequently are unable to enforce explicitly a notion of sufficient feasible decrease.

Channel routing plays an important role in the development of automated layout systems for integrated circuits. Many layout systems first place modules on a chip and then wire together terminals on different modules that should be electrically connected. This wiring problem is often solved by heuristically partitioning the given space into rectangular channels and then assigning to each such channel a set of wires which are to pass through it. This solution reduces a global wiring problem to a set of disjoint (and hopefully easier) local channel routing subproblems. For this reason, the channel routing problem has been intensively studied for over a decade, and numerous heuristics and approximation algorithms have been proposed for its solution. The generic form of the channel routing problem may be described as follows. The channel consists of a rectilinear grid of tracks (or rows) and columns. Along the top and bottom tracks are numbers called terminals, and terminals with the same number form a net. A net with q terminals is called an q-terminal net. The smallest net is a two-terminal net; if q2, we have a multiterminal net. The channel routing problem is to connect all the terminals in each net using horizontal and vertical wires which are routed along the underlying rectilinear grid. The goal is to complete the wiring using the minimum number of tracks; i. e., to minimize the width of the channel.

Brian interviews two different guests.First is Hillary Boehm-Edwards. Hillary is a first-year commerce student at McMaster University. She talked about an article she wrote and published in the Toronto Star, an opinion editorial regarding the dangers of media algorithms and their ability to polarize opinions. Hillary explained how media algorithms influence the content users see on social media, especially within a political context and elaborates on how these algorithms contribute to misinformation and vaccine hesitancy? The article raises the question of who controls a user's opinions - the viewer or the algorithm. Could you discuss the balance of responsibility between the two?Then, Jenny Kim talks about the Kindfulness Workshop with the Urban Buddhist Monk's 6th Annual Kindful Canada Symposium 2023 event which happens this Saturday, Oct. 14th.

We analyze the performance of various heuristic algorithms for minimizing realizations of multiple-valued functions by the newly developed CCD 191 and CMOS [W] programmable logic arrays. The functions realized by such PLA's are in sum-of-products form, where sum is ordinary addition truncated to the highest logic value, and where product represents the MIN operation on functions of the input variables which are the interval literal operations. We compare three previously published heuristics, Pomper and Armstrong [14], Besslich [3], and Dueck and Miller [6], over sets of random and random symmetric functions. We show an exact minimization method that is a tree search using backtracking. A considerable reduction in the search space is achieved by considering constrained implicant sets and by eliminating some implicants altogether. Even with this improvement, the time required for exact minimization is extremely high when compared to all three heuristics. We also examine the case where only prime implicants are considered and show that such implicants have marginal value compared to constrained implicant sets. Our basis of comparison is the average number of product terms. We show that the heuristic methods are reasonably close to minimal and produce nearly the same average number of product terms. Interestingly though, there is surprisingly little overlap in the set of functions where the best realization is achieved. Thus, there is a benefit to applying all three heuristics to a given function and then choosing the best realization.

Non-resolved optical observations of satellites have been known to supply researchers with valuable information about satellite status. Until recently most non-resolved analysis techniques have required an expert human in the loop in order to attribute observed information to satellite features. We are designing algorithms that automate the process of rapidly characterizing satellites from non-resolved optical data of 3-axis stabilized geostationary satellites. We will present background information on corrections for lighting conditions that vary as a function of the Earth s season, and permit automated satellite characterization algorithms. Much of this information is empirical, based on a GEO-Sat Color Photometry Catalog (GCPC), for which collection began in 2004 and continues into the present.

In the ninth quarter of the work effort, we focused on a) conducting experiments on real-world data sets using the developed algorithms, b) continued design/implementation of the Multiscale Heat-Kernel Coordinates (MHKC) algorithms and c) packaging for releasing the software as open source. This report documents algorithm designs for the MHKC algorithms. The project is currently on track - in the upcoming quarter, we will continue applying the developed algorithms to various data sets and the design/implementation of the multiscale heat kernel coordinates algorithms. No problems are currently anticipated.

We consider optimization and equilibrium models and algorithms for dynamic resource allocation. The most important accomplishment would be the paper: The Simplex and Policy-Iteration Methods are Strongly Polynomial for the Markov Decision Problem with a Fixed Discount Rate, where I proved that the classic policy-iteration method (Howard 1960), including the Simplex method (Dantzig 1947) with the most-negative-reduced-cost pivoting rule, is a strongly polynomial-time exact algorithm for solving the Markov decision problem (MDP) exactly with any fixed discount factor. Markov decision process (e.g., Shapley, 1953) is arguably one of the most widely used decision models and methodologies in practice, as a celebrated example to showcase the power of optimization to help making sensible decisions in a complex system and stochastic environment. And the two methods are the most used methods but their theoretical complexities were open before this result. We also explore an online resource allocation in a set of research publications, including prediction market, Internet auction, and etc.

Nathan Matias is a professor at Cornell University and leads the Citizens and Technology Lab. He believes that the strong tradition of scrappy, responsive, citizen science (which has led to positive changes in food safety and quality assurance regulations) can also bring positive changes to how algorithms impact our lives. The Citizens and Technology Lab, Cornell UniversityMozilla Foundation- volunteer opportunities  Algorithmic Justice League Joy Buolamwini, TED Talk, Fighting Bias in AlgorithmsThe MarkupConsumer ReportsThe History of the Good Housekeeping InstituteElinor Ostrom's Work on Governing The Commons: An Appreciation, LSEKurt Lewin, American Social Psychologist, Encyclopedia BritanicaDemocratic, Authoritarian, Laissez-Faire: What Type Of Leader Are You?, Forbes 2019 Learn more about your ad-choices at https://www.iheartpodcastnetwork.comSee omnystudio.com/listener for privacy information.

We study the problem of recovering the subspace spanned by the first $k$ principal components of $d$-dimensional data under the streaming setting, with a memory bound of $O(kd)$. Two families of algorithms are known for this problem. The first family is based on the framework of stochastic gradient descent. Nevertheless, the convergence rate of the family can be seriously affected by the learning rate of the descent steps and deserves more serious study. The second family is based on the power method over blocks of data, but setting the block size for its existing algorithms is not an easy task. In this paper, we analyze the convergence rate of a representative algorithm with decayed learning rate (Oja and Karhunen, 1985) in the first family for the general $k>1$ case. Moreover, we propose a novel algorithm for the second family that sets the block sizes automatically and dynamically with faster convergence rate. We then conduct empirical studies that fairly compare the two families on real-world data. The studies reveal the advantages and disadvantages of these two families.

Sample reuse techniques have significantly reduced the numerical complexity of probabilistic robustness analysis. Existing results show that for a nested collection of hyper-spheres the complexity of the problem of performing $N$ equivalent i.i.d. (identical and independent) experiments for each sphere is absolutely bounded, independent of the number of spheres and depending only on the initial and final radii. In this chapter we elevate sample reuse to a new level of generality and establish that the numerical complexity of performing $N$ equivalent i.i.d. experiments for a chain of sets is absolutely bounded if the sets are nested. Each set does not even have to be connected, as long as the nested property holds. Thus, for example, the result permits the integration of deterministic and probabilistic analysis to eliminate regions from an uncertainty set and reduce even further the complexity of some problems. With a more general view, the result enables the analysis of complex decision problems mixing real-valued and discrete-valued random variables.

We implemented a GPU based parallel code to perform Monte Carlo simulations of the two dimensional q-state Potts model. The algorithm is based on a checkerboard update scheme and assigns independent random numbers generators to each thread. The implementation allows to simulate systems up to ~10^9 spins with an average time per spin flip of 0.147ns on the fastest GPU card tested, representing a speedup up to 155x, compared with an optimized serial code running on a high-end CPU. The possibility of performing high speed simulations at large enough system sizes allowed us to provide a positive numerical evidence about the existence of metastability on very large systems based on Binder's criterion, namely, on the existence or not of specific heat singularities at spinodal temperatures different of the transition one.

We consider the optimal online packet scheduling problem in a single-user energy harvesting wireless communication system, where energy is harvested from natural renewable sources, making future energy arrivals instants and amounts random in nature. The most general case of arbitrary energy arrivals is considered where neither the future energy arrival instants or amount, nor their distribution is known. The problem considered is to adaptively change the transmission rate according to the causal energy arrival information, such that the time by which all packets are delivered is minimized. We assume that all bits have arrived and are ready at the source before the transmission begins. For a minimization problem, the utility of an online algorithm is tested by finding its competitive ratio or competitiveness that is defined to be the maximum of the ratio of the gain of the online algorithm with the optimal offline algorithm over all input sequences. We derive a lower and upper bound on the competitive ratio of any online algorithm to minimize the total transmission time in an energy harvesting system. The upper bound is obtained using a `lazy' transmission policy that chooses its transmission power to minimize the transmission time assuming that no further energy arrivals are going to occur in future. The lazy transmission policy is shown to be strictly two-competitive. We also derive an adversarial lower bound that shows that competitive ratio of any online algorithm is at least 1.325.

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks which employ multiple hops and the amplify-and-forward cooperation strategy for both the uplink and the downlink. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained linear minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problems, we develop cost-effective algorithms for adaptive joint power allocation, and estimation of the parameters of the receiver and the channels. An analysis of the optimization problem is carried out and shows that the problem can have its convexity enforced by an appropriate choice of the power constraint parameter, which allows the algorithms to avoid problems with local minima. A study of the complexity and the requirements for feedback channels of the proposed algorithms is also included for completeness. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing non-cooperative and cooperative schemes.

We measure the sweep-to-sweep autocorrelations of blocked loops below and above the deconfinement transition for SU(3) on a $16^4$ lattice using 20000-140000 Monte-Carlo updating sweeps. A divergence of the autocorrelation time toward the critical $\beta$ is seen at high blocking levels. The peak is near $\beta$ = 6.33 where we observe 440 $\pm$ 210 for the autocorrelation time of $1\times 1$ Wilson loop on $2^4$ blocked lattice. The mixing of 7 Brown-Woch overrelaxation steps followed by one pseudo-heat-bath step appears optimal to reduce the autocorrelation time below the critical $\beta$. Above the critical $\beta$, however, no clear difference between these two algorithms can be seen and the system decorrelates rather fast.

The purpose of this research is to create a method of finding practical, robust control laws. The robustness of a controller is judged by Stochastic Robustness metrics and the level of robustness is optimized by searching for design parameters that minimize a robustness cost function.

The separation of vectors by multigrid (MG) algorithms is applied to the study of convergence and to the prediction of the performance of MG algorithms. The separation operator for a two level cycle algorithm is derived. It is used to analyze the efficiency of the cycle when mixing of eigenvectors occurs. In particular cases the separation analysis reduces to Fourier type analysis. The separation operator of a two level cycle for a Schridubger eigenvalue problem, is derived and analyzed in a Fourier basis. Separation analysis gives information on how to choose performance relaxations and inter-level transfers. Separation analysis is a tool for analyzing and designing algorithms, and for optimizing their performance.

The scope of the current work is to develop practical sensing implementations for robots operating in complex, partially unstructured environments. A focus in this work is to develop object models and estimation techniques which are specific to requirements of robot locomotion, approach and avoidance, and grasp and manipulation. Such problems have to date received limited attention in either computer or human vision - in essence, asking not only how perception is in general modeled, but also what is the functional purpose of its underlying representations. As in the past, researchers are drawing on ideas from both the psychological and machine vision literature. Of particular interest is the development 3-D shape and motion estimates for complex objects when given only partial and uncertain information and when such information is incrementally accrued over time. Current studies consider the use of surface motion, contour, and texture information, with the longer range goal of developing a fused sensing strategy based on these sources and others.

This paper compares two different approaches for sensor selection for distributed tracking: 1) The Frisbee method, and 2) Global Node Selection (GNS). The Frisbee method is based on the proximity of the nodes to the predicted location of the target; GNS is based on minimizing the unbiased Cramer Rao lower bound (CRLB). Both theoretical and experimental results indicate that the Frisbee method is as effective as GNS. Furthermore, the Frisbee method is attractive due to its very light computational load.

Raman spectroscopy is a powerful and effective technique for analyzing and identifying the chemical composition of a substance. Two types of Raman spectra estimation algorithms exist: supervised and unsupervised. In this paper, we perform a comparative analysis of five supervised algorithms for estimating Raman spectra. We describe a realistic measurement model for a dispersive Raman measurement device and observe that the measurement error variances vary significantly with bin index. Monte Carlo analyses with simulated measurements are used to calculate the bias root mean square error, and computational time for each algorithm. Our analyses show that it is important to use correct measurement weights and enforce the nonnegative constraint in parameter estimation.

High-speed monitoring of Internet traffic is an important and challenging problem, with applications to real-time attack detection and mitigation, traffic engineering, etc. However, packet-level monitoring requires fast streaming algorithms that use very little memory space and little communication among collaborating network monitoring points. In this paper, we consider the problem of detecting superspreaders, which are sources that connect to a large number of distinct destinations. We propose several new streaming algorithms for detecting superspreaders, and prove guarantees on their accuracy and memory requirements. We also show experimental results on real network traces. Our algorithms are substantially more efficient (both theoretically and experimentally) than previous approaches. We also provide several extensions to our algorithms -- we show how to identify superspreaders in a distributed setting, with sliding windows, and when deletions are allowed in the stream. More generally, our algorithms are applicable to any problem that can be formulated as follows: given a stream of (x,y) pairs, find all the x's that are paired with a large number of distinct y's. We call this the heavy distinct-hitters problem. There are many network security applications of this general problem. This paper discusses these and other applications, and for concreteness, focuses on the superspreader problem.

We obtain fast parallel algorithms for several scheduling problems. Some of the problems considered are: scheduling to minimize the number of tardy jobs; job sequencing with deadlines; scheduling to minimize earliness and tardiness penalties; channel assignment; and minimizing the mean finish time. The shared memory model of parallel computers is used. (Author)

This paper examines a class of proximal minimization algorithms in which the objective function of the underlying convex program is approximated by cutting planes. This class includes algorithms such as cutting plane, cutting plane with line search and bundle methods. Among these algorithms, the bundle methods can be viewed as a quadratic counterpart of the cutting plane algorithm with line search, for they both attempt to decrease the true objective function at every iteration. On the other hand, the cutting plane algorithm does not explicitly and/or directly attempt to decrease the true objective function. However, it relies on the monotonicity of the approximating function to guarantee convergence to an optimal solution. This prompts the question of whether there exists a quadratic counterpart for the cutting plane algorithm. To provide an affirmative answer, this paper constructs a new convergent algorithm which resembles, but is different from, the bundle methods. Also, to make the relationship between bundle methods and proximal minimization more concrete, this paper also supplies a convergence proof for a variant of the bundle methods which utilizes analysis common to proximal minimization.

During the past year, we have continued our efforts in image and video compression. We developed a real time software only scalable video compression codec. We have also optimized the scalable coder for transmission over wireless links by jointly optimizing the channel and source coders. We also have developed a video retrieval algorithm using motion, size, and color. We constrained the algorithm to be able to retrieve video in real time and only using information available in compressed bit-streams. In the are of video compression, we continued improving video coding using matching pursuits. We improved the coding efficiency performance by more than 1 dB for many of MPEG-4 test sequences. We have worked on reducing the complexity of the encoder and developed a real time software only low bit rate encoder. We also addressed the error resilience issue and developed a more error resilient matching pursuit video codec that performs as well as original coder, if not better.

YouTube's algorithms are abhorrent in a new way, details on the next Playstation console, Google makes nice with developers, and three interesting sides to the TikTok story.Sponsors:EveryKey.com 20% off with promocode RIDE20 Mealime AppLinks: YouTube shows 9/11 link on live videos of unrelated Notre Dame fire (The Verge)EXCLUSIVE: WHAT TO EXPECT FROM SONY'S NEXT-GEN PLAYSTATION (Wired) 15 MONTHS OF FRESH HELL INSIDE FACEBOOK (Wired) Next major macOS version will include standalone Music, Podcasts, and TV apps, Books app gets major redesign (9to5Mac) Improving the update process with your feedback (Android Developer's Blog) AT&T sells Hulu stake, valuing the video streaming company at $15 billion (CNBC) Short video app TikTok extends reach in global markets, sees in-app purchases surge more than 200 per cent (South China Morning Post)TikTok downloads ordered to be blocked on iOS and Android in India over porn and other illegal content (TechCrunch) TikTok sensation Lil Nas X rewrites the rules of country music (Axios)

📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings JavaScript Algorithms and Data Structures This repository contains JavaScript based examples of manypopular algorithms and data structures. Each algorithm and data structure has its own separate READMEwith related explanations and links for further reading (including onesto YouTube videos). Read this in other languages: 简体中文 , 繁體中文 , 한국어 , 日本語 , Polski , Français , Español , Português , Русский , Türk , Italiana , Bahasa Indonesia , Українська , Arabic ☝ Note that this project is meant to be used for learning and researching purposesonly, and it is not meant to be used for production. Data Structures A data structure is a particular way of organizing and storing data in a computer so that it canbe accessed and modified efficiently. More precisely, a data structure is a collection of datavalues, the relationships among them, and the functions or operations that can be applied tothe data. B - Beginner, A - Advanced B Linked List B Doubly Linked List B Queue B Stack B Hash Table B Heap - max and min heap versions B Priority Queue A Trie A Tree A Binary Search Tree A AVL Tree A Red-Black Tree A Segment Tree - with min/max/sum range queries examples A Fenwick Tree (Binary Indexed Tree) A Graph (both directed and undirected) A Disjoint Set A Bloom Filter Algorithms An algorithm is an unambiguous specification of how to solve a class of problems. It isa set of rules that precisely define a sequence of operations. B - Beginner, A - Advanced Algorithms by Topic Math B Bit Manipulation - set/get/update/clear bits, multiplication/division by two, make negative etc. B Factorial B Fibonacci Number - classic and closed-form versions B Prime Factors - finding prime factors and counting them using Hardy-Ramanujan's theorem B Primality Test (trial division method) B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Least Common Multiple (LCM) B Sieve of Eratosthenes - finding all prime numbers up to any given limit B Is Power of Two - check if the number is power of two (naive and bitwise algorithms) B Pascal's Triangle B Complex Number - complex numbers and basic operations with them B Radian & Degree - radians to degree and backwards conversion B Fast Powering B Horner's method - polynomial evaluation B Matrices - matrices and basic matrix operations (multiplication, transposition, etc.) B Euclidean Distance - distance between two points/vectors/matrices A Integer Partition A Square Root - Newton's method A Liu Hui π Algorithm - approximate π calculations based on N-gons A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Sets B Cartesian Product - product of multiple sets B Fisher–Yates Shuffle - random permutation of a finite sequence A Power Set - all subsets of a set (bitwise and backtracking solutions) A Permutations (with and without repetitions) A Combinations (with and without repetitions) A Longest Common Subsequence (LCS) A Longest Increasing Subsequence A Shortest Common Supersequence (SCS) A Knapsack Problem - "0/1" and "Unbound" ones A Maximum Subarray - "Brute Force" and "Dynamic Programming" (Kadane's) versions A Combination Sum - find all combinations that form specific sum Strings B Hamming Distance - number of positions at which the symbols are different A Levenshtein Distance - minimum edit distance between two sequences A Knuth–Morris–Pratt Algorithm (KMP Algorithm) - substring search (pattern matching) A Z Algorithm - substring search (pattern matching) A Rabin Karp Algorithm - substring search A Longest Common Substring A Regular Expression Matching Searches B Linear Search B Jump Search (or Block Search) - search in sorted array B Binary Search - search in sorted array B Interpolation Search - search in uniformly distributed sorted array Sorting B Bubble Sort B Selection Sort B Insertion Sort B Heap Sort B Merge Sort B Quicksort - in-place and non-in-place implementations B Shellsort B Counting Sort B Radix Sort Linked Lists B Straight Traversal B Reverse Traversal Trees B Depth-First Search (DFS) B Breadth-First Search (BFS) Graphs B Depth-First Search (DFS) B Breadth-First Search (BFS) B Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Dijkstra Algorithm - finding the shortest paths to all graph vertices from single vertex A Bellman-Ford Algorithm - finding the shortest paths to all graph vertices from single vertex A Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices A Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions) A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Topological Sorting - DFS method A Articulation Points - Tarjan's algorithm (DFS based) A Bridges - DFS based algorithm A Eulerian Path and Eulerian Circuit - Fleury's algorithm - Visit every edge exactly once A Hamiltonian Cycle - Visit every vertex exactly once A Strongly Connected Components - Kosaraju's algorithm A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city Cryptography B Polynomial Hash - rolling hash function based on polynomial B Rail Fence Cipher - a transposition cipher algorithm for encoding messages B Caesar Cipher - simple substitution cipher B Hill Cipher - substitution cipher based on linear algebra Machine Learning B NanoNeuron - 7 simple JS functions that illustrate how machines can actually learn (forward/backward propagation) B k-NN - k-nearest neighbors classification algorithm B k-Means - k-Means clustering algorithm Image Processing B Seam Carving - content-aware image resizing algorithm Uncategorized B Tower of Hanoi B Square Matrix Rotation - in-place algorithm B Jump Game - backtracking, dynamic programming (top-down + bottom-up) and greedy examples B Unique Paths - backtracking, dynamic programming and Pascal's Triangle based examples B Rain Terraces - trapping rain water problem (dynamic programming and brute force versions) B Recursive Staircase - count the number of ways to reach to the top (4 solutions) B Best Time To Buy Sell Stocks - divide and conquer and one-pass examples A N-Queens Problem A Knight's Tour Algorithms by Paradigm An algorithmic paradigm is a generic method or approach which underlies the design of a classof algorithms. It is an abstraction higher than the notion of an algorithm, just as analgorithm is an abstraction higher than a computer program. Brute Force - look at all the possibilities and selects the best solution B Linear Search B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top A Maximum Subarray A Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city A Discrete Fourier Transform - decompose a function of time (a signal) into the frequencies that make it up Greedy - choose the best option at the current time, without any consideration for the future B Jump Game A Unbound Knapsack Problem A Dijkstra Algorithm - finding the shortest path to all graph vertices A Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph A Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph Divide and Conquer - divide the problem into smaller parts and then solve those parts B Binary Search B Tower of Hanoi B Pascal's Triangle B Euclidean Algorithm - calculate the Greatest Common Divisor (GCD) B Merge Sort B Quicksort B Tree Depth-First Search (DFS) B Graph Depth-First Search (DFS) B Matrices - generating and traversing the matrices of different shapes B Jump Game B Fast Powering B Best Time To Buy Sell Stocks - divide and conquer and one-pass examples A Permutations (with and without repetitions) A Combinations (with and without repetitions) Dynamic Programming - build up a solution using previously found sub-solutions B Fibonacci Number B Jump Game B Unique Paths B Rain Terraces - trapping rain water problem B Recursive Staircase - count the number of ways to reach to the top B Seam Carving - content-aware image resizing algorithm A Levenshtein Distance - minimum edit distance between two sequences A Longest Common Subsequence (LCS) A Longest Common Substring A Longest Increasing Subsequence A Shortest Common Supersequence A 0/1 Knapsack Problem A Integer Partition A Maximum Subarray A Bellman-Ford Algorithm - finding the shortest path to all graph vertices A Floyd-Warshall Algorithm - find the shortest paths between all pairs of vertices A Regular Expression Matching Backtracking - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you testif it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on adifferent path of finding a solution. Normally the DFS traversal of state-space is being used. B Jump Game B Unique Paths B Power Set - all subsets of a set A Hamiltonian Cycle - Visit every vertex exactly once A N-Queens Problem A Knight's Tour A Combination Sum - find all combinations that form specific sum Branch & Bound - remember the lowest-cost solution found at each stage of the backtrackingsearch, and use the cost of the lowest-cost solution found so far as a lower bound on the cost ofa least-cost solution to the problem, in order to discard partial solutions with costs larger than thelowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-spacetree is being used. How to use this repository Install all dependencies npm install Run ESLint You may want to run it to check code quality. npm run lint Run all tests npm test Run tests by name npm test -- 'LinkedList' Troubleshooting In case if linting or testing is failing try to delete the node_modules folder and re-install npm packages: rm -rf ./node_modulesnpm i Playground You may play with data-structures and algorithms in ./src/playground/playground.js file and writetests for it in ./src/playground/__test__/playground.test.js . Then just simply run the following command to test if your playground code works as expected: npm test -- 'playground' Useful Information References ▶ Data Structures and Algorithms on YouTube Big O Notation Big O notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows.On the chart below you may find most common orders of growth of algorithms specified in Big O notation. Source: Big O Cheat Sheet . Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data. | Big O Notation | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements || -------------- | ---------------------------- | ----------------------------- | ------------------------------- || O(1) | 1 | 1 | 1 || O(log N) | 3 | 6 | 9 || O(N) | 10 | 100 | 1000 || O(N log N) | 30 | 600 | 9000 || O(N^2) | 100 | 10000 | 1000000 || O(2^N) | 1024 | 1.26e+29 | 1.07e+301 || O(N!) | 3628800 | 9.3e+157 | 4.02e+2567 | Data Structure Operations Complexity | Data Structure | Access | Search | Insertion | Deletion | Comments || ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- || Array | 1 | n | n | n | || Stack | n | n | 1 | 1 | || Queue | n | n | 1 | 1 | || Linked List | n | n | 1 | n | || Hash Table | - | n | n | n | In case of perfect hash function costs would be O(1) || Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(n)) || B-Tree | log(n) | log(n) | log(n) | log(n) | || Red-Black Tree | log(n) | log(n) | log(n) | log(n) | || AVL Tree | log(n) | log(n) | log(n) | log(n) | || Bloom Filter | - | 1 | 1 | - | False positives are possible while searching | Array Sorting Algorithms Complexity | Name | Best | Average | Worst | Memory | Stable | Comments || --------------------- | :-------------: | :-----------------: | :-----------------: | :-------: | :-------: | :-------- || Bubble sort | n | n 2 | n 2 | 1 | Yes | || Insertion sort | n | n 2 | n 2 | 1 | Yes | || Selection sort | n 2 | n 2 | n 2 | 1 | No | || Heap sort | n log(n) | n log(n) | n log(n) | 1 | No | || Merge sort | n log(n) | n log(n) | n log(n) | n | Yes | || Quick sort | n log(n) | n log(n) | n 2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space || Shell sort | n log(n) | depends on gap sequence | n (log(n)) 2 | 1 | No | || Counting sort | n + r | n + r | n + r | n + r | Yes | r - biggest number in array || Radix sort | n * k | n * k | n * k | n + k | Yes | k - length of longest key | Project Backers You may support this project via ❤️️ GitHub or ❤️️ Patreon . Folks who are backing this project ∑ = 0 To restore the repository download the bundle wget https://archive.org/download/github.com-trekhleb-javascript-algorithms_-_2021-04-27_15-32-27/trekhleb-javascript-algorithms_-_2021-04-27_15-32-27.bundle and run: git clone trekhleb-javascript-algorithms_-_2021-04-27_15-32-27.bundle Source: https://github.com/trekhleb/javascript-algorithms Uploader: trekhleb Upload date: 2021-04-27

During the period of this grant, our detailed technical accomplishments are reported through journal articles and technical reports. Each of our semi- annual reports will highlight certain technical areas and provide a summary listing of our technical articles related to the project.

Several algorithms for introducing artificial dissipation into a central difference approximation to the Euler and Navier Stokes equations are considered. The focus of the paper is on the convective upwind and split pressure (CUSP) scheme, which is designed to support single interior point discrete shock waves. This scheme is analyzed and compared in detail with scalar and matrix dissipation (MATD) schemes. Resolution capability is determined by solving subsonic, transonic, and hypersonic flow problems. A finite-volume discretization and a multistage time-stepping scheme with multigrid are used to compute solutions to the flow equations. Numerical results are also compared with either theoretical solutions or experimental data. For transonic airfoil flows the best accuracy on coarse meshes for aerodynamic coefficients is obtained with a simple MATD scheme.

The Land Product Evaluation and Algorithm Testing Element (Land PEATE), a component of the Science Data Segment of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), is being developed at the NASA Goddard Space Flight Center (GSFC). The primary task of the Land PEATE is to assess the quality of the Visible Infrared Imaging Radiometer Suite (VIIRS) Land data products made by the Interface Data Processing System (IDPS) using the Operational (OPS) Code during the NPP era and to recommend improvements to the algorithms in the IDPS OPS code. The Land PEATE uses a version of the MODIS Adaptive Processing System (MODAPS), NPPDAPS, that has been modified to produce products from the IDPS OPS code and software provided by the VIIRS Science Team, and uses the MODIS Land Data Operational Product Evaluation (LDOPE) team for evaluation of the data records generated by the NPPDAPS. Land PEATE evaluates the algorithms by comparing data products generated using different versions of the algorithm and also by comparing to heritage products generated from different instrument such as MODIS using various quality assessment tools developed at LDOPE. This paper describes the Land PEATE system and some of the approaches used by the Land PEATE for evaluating the VIIRS Land algorithms during the pre-launch period of the NPP mission and the proposed plan for long term monitoring of the quality of the VIIRS Land products post-launch.