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An Algebraic Circuit for a multivariate polynomial P is a computational model for constructing the polynomial P using only additions and multiplications. It is a syntactic model of computation, as opposed to the Boolean Circuit model, and hence lower bounds for this model are widely expected to be easier to prove than lower bounds for Boolean circuits. Despite this, we do not have superpolynomial lower bounds against general algebraic circuits of depth 3 (except over constant-sized finite fields) and depth 4 (over any field other than F2), while constant-depth Boolean circuit lower bounds have been known since the early 1980s. In this paper, we prove the first superpolynomial lower bounds against algebraic circuits of all constant depths over all fields of characteristic 0. We also observe that our super-polynomial lower bound for constant-depth circuits implies the first deterministic sub-exponential time algorithm for solving the Polynomial Identity Testing (PIT) problem for all small-depth circuits using the known connection between algebraic hardness and randomness. [ABSTRACT FROM AUTHOR]

The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

This article details Charles Babbage’s theoretical computer program, the first attempt to specific how to mechanize complex algorithms with a computer, written in 1837. The article also discusses his theoretical computer, the Analytical Engine, that his program would have run on. Topics include Babbage’s first and second code tables and the design of the Analytical Engine.

The article discusses a research paper that applies online resource allocation algorithms to refugee resettlement, aiming to improve refugees' integration into local communities and employment prospects. By utilizing concepts from algorithm design, such as balancing resource utilization and maintaining capacity for future refugees, the authors were able to enhance the employability metric for resettlement agencies like the Hebrew Immigrant Aid Society (HIAS) by approximately 10%. This research not only addresses critical societal issues but also highlights the potential of algorithms to positively impact real-world outcomes for vulnerable populations, encouraging collaboration between algorithm designers and practitioners on important societal problems.

The Burrows-Wheeler Transform (BWT) is an invertible text transformation that permutes symbols of a text according to the lexicographical order of its suffixes. BWT is the main component of popular lossless compression programs (such as bzip2) as well as recent powerful compressed indexes (such as the r-index7), central in modern bioinformatics. The compressibility of BWT is quantified by the number r of equal-letter runs in the output. Despite the practical significance of BWT, no nontrivial upper bound on r is known. By contrast, the sizes of nearly all other known compression methods have been shown to be either always within a polylog n factor (where n is the length of the text) from z, the size of Lempel--Ziv (LZ77) parsing of the text, or much larger in the worst case (by an nε factor for ε > 0). In this paper, we show that r = O (z log² n) holds for every text. This result has numerous implications for text indexing and data compression; in particular: (1) it proves that many results related to BWT automatically apply to methods based on LZ77, for example, it is possible to obtain functionality of the suffix tree in O (z polylog n) space; (2) it shows that many text processing tasks can be solved in the optimal time assuming the text is compressible using LZ77 by a sufficiently large polylog n factor; and (3) it implies the first nontrivial relation between the number of runs in the BWT of the text and of its reverse. In addition, we provide an O (z polylog n)-time algorithm converting the LZ77 parsing into the run-length compressed BWT. To achieve this, we develop several new data structures and techniques of independent interest. In particular, we define compressed string synchronizing sets (generalizing the recently introduced powerful technique of string synchronizing sets11) and show how to efficiently construct them. Next, we propose a new variant of wavelet trees for sequences of long strings, establish a nontrivial bound on their size, and describe efficient construction algorithms. Finally, we develop new indexes that can be constructed directly from the LZ77 parsing and efficiently support pattern matching queries on text substrings. [ABSTRACT FROM AUTHOR]

This article investigates artificial intelligence (AI) fairness in decision-making specifically examining the limitations of this process. The article discusses eight inherent limitations in AI fairness including lack of fairness in human decision-making used to train the AI model, lack of a universal fairness definition, and limited power over the complete decision-making process.

The article looks at how fairness can be defined and implemented in algorithmic decision-making. The author states that alternative, reasonable definitions of fairness can end up being mutually exclusive when operationalized in mathematical or algorithmic form. He discusses competing analyses of fairness in algorithms used to predict criminal behavior.

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B, not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate. [ABSTRACT FROM AUTHOR]

In this paper, we describe multi-itinerary optimization (MIO)--a novel Bing Maps service that automates the process of building itineraries for multiple agents while optimizing their routes to minimize travel time or distance. MIO can be used by organizations with a fleet of vehicles and drivers, mobile salesforce, or a team of personnel in the field, to maximize workforce efficiency. It supports a variety of constraints, such as service time windows, duration, priority, pickup and delivery dependencies, and vehicle capacity. MIO also considers traffic conditions between locations, resulting in algorithmic challenges at multiple levels (e.g., calculating time-dependent travel-time distance matrices at scale and scheduling services for multiple agents). To support an end-to-end cloud service with turnaround times of a few seconds, our algorithm design targets a sweet spot between accuracy and performance. Toward that end, we build a scalable approach based on the ALNS metaheuristic. Our experiments show that accounting for traffic significantly improves solution quality: MIO finds efficient routes that avoid late arrivals, whereas traffic-agnostic approaches result in a 15% increase in the combined travel time and the lateness of an arrival. Furthermore, our approach generates itineraries with substantially higher quality than a cutting-edge heuristic (LKH), with faster running times for large instances. [ABSTRACT FROM AUTHOR]

The article discusses an algorithm designed to compute maximum flow and minimum-cost flows. It builds on the works of prior researchers to propose new theorems and provides formulas and graphs to illustrate these theorems and schemes.

The article discusses Explainable artificial intelligence (XAI) and the expectation of artificial intelligence (AI) end users to understand how AI works, including how algorithmic decision making occurs. An overview of AI end users' desire for information on AI methodology on reaching decisions, including in regard to the ethical nature of AI, is provided.

The article focuses on how revisiting key algorithms from computing history can unlock the hidden efficiency in computer science problems. The author discusses the shortest-path problem that has been long-standing, explores the use of the Bellman-Ford algorithm, and examines the work of Aaron Bernstein, Danupon Nanongkai, and Christian Wulff-Nilsen.

A new machine-learning algorithm called xFakeSci has been developed by Ahmed Abdeen Hamed, a visiting research fellow at Binghamton University, to detect fake scientific articles produced by artificial intelligence. The algorithm can detect up to 94% of bogus papers, which is nearly twice as successful as other data-mining techniques. Hamed and collaborator Xindong Wu created 50 fake articles for each of three medical topics and compared them to real articles on the same topics. The algorithm analyzes the number of bigrams and how they are linked to other words and concepts in the text to identify patterns that distinguish fake articles from real ones. Hamed plans to expand the range of topics to further develop the algorithm and raise awareness about the issue of fake research papers. [Extracted from the article]

This article explores the trends in computer science research in India from 2019 to 2022 based on research coming out of leading Indian technology institutes, including Chennai Mathematical Institute, Indian Institute of Science and Indian Institute of Technology Delhi. Topics include research on automata theory, formal verification, computational complexity research, and Algorithms research.

A recent study conducted by Coltan Scrivner and his colleagues explores the relationship between behavioral attraction and morbid curiosity in women's mating interest. The researchers suggest that despite the potential costs associated with men who exhibit dangerous personalities, morbidly curious women may benefit from upregulating their preference for such men to satisfy short-term mating goals. The study does not delve into the business potential of this research, but it presents an opportunity for specialized morbid tool-making and tool use. Additionally, the article mentions a free online Morbid Curiosity Test created by Scrivner to measure one's position on the Morbid Curiosity Scale. [Extracted from the article]

Exploring a potential way to immensely speed up algorithms for the group isomorphism problem. [ABSTRACT FROM AUTHOR]

A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and the computation of the number of distinct elements in a data stream, also known as the zeroth frequency moment (F0) of a data stream. [ABSTRACT FROM AUTHOR]

Flash-based solid-state drives (SSDs) are a key component in most computer systems, thanks to their ability to support parallel I/O at sub-millisecond latency and consistently high throughput. At the same time, due to the limitations of the flash media, they perform writes out-of-place, often incurring a high internal overhead which is referred to as write amplification. Minimizing this overhead has been the focus of numerous studies by the systems research community for more than two decades. The abundance of system-level optimizations for reducing SSD write amplification, which is typically based on experimental evaluation, stands in stark contrast to the lack of theoretical algorithmic results in this problem domain. To bridge this gap, we explore the problem of reducing write amplification from an algorithmic perspective, considering it in both offline and online settings. In the offline setting, we present a near-optimal algorithm. In the online setting, we first consider algorithms that have no prior knowledge about the input and show that in this case, the greedy algorithm is optimal. Then, we design an online algorithm that uses predictions about the input. We show that when predictions are relatively accurate, our algorithm significantly improves over the greedy algorithm. We complement our theoretical findings with an empirical evaluation of our algorithms, comparing them with the state-of-the-art scheme. The results confirm that our algorithms exhibit an improved performance for a wide range of input traces. [ABSTRACT FROM AUTHOR]

This article discusses the use of theoretical analysis to measure the performance of algorithms and investigates its shortcomings using the field of sequencing bioinformatics. The author posits that theoretical analysis applied to algorithm performance has fallen short of its stated goals of predicting the empirical performance of an algorithm and being a yardstick to drive the design of new algorithms.

This article discusses the recent areas of focus at the National Institute of Informatics (NII) in Japan. First, a look at its academic services including SINET6, a backbone network service across Japan and its data platform service, the NII Research Data Cloud. The article also details NII research in computer science with projects in innovative computer vision and engineerable artificial intelligence, as well as applied research projects including a COVID-19 pneumonia diagnosis tool.

This article discusses how the combination of humans and artificial intelligence could target misinformation and polarization, by examining two scenarios- one where computing methods can assist human fact-checkers and one where human search methods can assist in search engine algorithms for personalization. Topics include the Australian Communication and Media Authority research on misinformation in Australia, whether filter bubbles are created by personalization attempts of algorithms, and the various computational methods that can assist fact-checking to prevent misinformation.

The author presents a set of puzzles involving tidy towers, a colored block stacking puzzle. First, several scenarios related to the puzzle are presented and solved. Lastly, the author presents two puzzles and information is provided for readers to submit their solutions.

The article addresses the topic of algorithm design in the solid-state drive (SSD) model of computation, noting its limitations as well as its benefits. The author references an accompanying paper which addresses one shortcoming in particular -- the phenomenon of "write amplification" -- by proposing a more accurate theoretical model of SSDs that incorporates read, write and erase operations.

This article explores the transformative impact of generative artificial intelligence (AI) tools like ChatGPT on education, highlighting enhanced teaching workflows, personalized instruction, and student engagement. It addresses ethical concerns such as algorithmic bias and data privacy while advocating for comprehensive AI literacy in curriculum.

The article explores generalization in deep learning algorithms and the role of implicit bias involved in training neural networks.

The threshold theorem is a seminal result in the field of quantum computing asserting that arbitrarily long quantum computations can be performed on a faulty quantum computer provided that the noise level is below some constant threshold. This remarkable result comes at the price of increasing the number of qubits (quantum bits) by a large factor that scales polylogarithmically with the size of the quantum computation we wish to realize. Minimizing the space overhead for fault-tolerant quantum computation is a pressing challenge that is crucial to benefit from the computational potential of quantum devices. In this paper, we study the asymptotic scaling of the space overhead needed for fault-tolerant quantum computation. We show that the polylogarithmic factor in the standard threshold theorem is in fact not needed and that there is a fault-tolerant construction that uses a number of qubits that is only a constant factor more than the number of qubits of the ideal computation. This result was conjectured by Gottesman who suggested to replace the concatenated codes from the standard threshold theorem by quantum error-correcting codes with a constant encoding rate. The main challenge was then to find an appropriate family of quantum codes together with an efficient classical decoding algorithm working even with a noisy syndrome. The efficiency constraint is crucial here: bear in mind that qubits are inherently noisy and that faults keep accumulating during the decoding process. The role of the decoder is therefore to keep the number of errors under control during the whole computation. On a technical level, our main contribution is the analysis of the small-set-flip decoding algorithm applied to the family of quantum expander codes. We show that it can be parallelized to run in constant time while correcting sufficiently many errors on both the qubits and the syndrome to keep the error under control. These tools can be seen as a quantum generalization of the bit-flip algorithm applied to the (classical) expander codes of Sipser and Spielman. [ABSTRACT FROM AUTHOR]

Memory leaks in web applications are pervasive and difficult to debug. Leaks degrade responsiveness by increasing garbage collection costs and can even lead to browser tab crashes. Previous leak detection approaches designed for conventional applications are ineffective in the browser environment. Tracking down leaks currently requires intensive manual effort by web developers, which is often unsuccessful. This paper introduces BLeak (Browser Leak debugger), the first system for automatically debugging memory leaks in web applications. BLeak’s algorithms leverage the observation that in modern web applications, users often repeatedly return to the same (approximate) visual state (e.g., the inbox view in Gmail). Sustained growth between round trips is a strong indicator of a memory leak. To use BLeak, a developer writes a short script (17–73 LOC on our benchmarks) to drive a web application in round trips to the same visual state. BLeak then automatically generates a list of leaks found along with their root causes, ranked by return on investment. Guided by BLeak, we identify and fix over 50 memory leaks in popular libraries and apps including Airbnb, AngularJS, Google Analytics, Google Maps SDK, and jQuery. BLeak’s median precision is 100%; fixing the leaks it identifies reduces heap growth by an average of 94%, saving from 0.5MB to 8MB per round trip. [ABSTRACT FROM AUTHOR]

This article details the concept of critical path tracing to analyze latency in complex distributed systems. Critical path tracing works by compiling a list of the steps responsible for the slowest path of request processing and then optimizing those steps to reduce latency overall. Topics include remote procedure call or RPC telemetry, central process unit of CPU profiler analysis, and distributed tracing analysis. A detailed look at how critical path tracing works is included.

Coordinate Rotation Digital Computer (CORDIC) is a robust iterative algorithm that computes many transcendental mathematical functions. This paper proposes a reconfigurable CORDIC hardware design and FPGA realization that includes all possible configurations of the CORDIC algorithm. The proposed architecture is introduced in two approaches: multiplier-less and single multiplier approaches, each with its advantages. Compared to recent related works, the proposed implementation overpasses them in the included number of configurations. Additionally, it demonstrates efficient hardware utilization and suitability for potential applications. Furthermore, the proposed design is applied to a memristive chaotic system with different transcendental functions computed using the proposed reconfigurable block. The memristive system design is realized on the Artix-7 FPGA board, yielding throughputs of 0.4483 and 0.3972 Gbit/s for the two approaches of reconfigurable CORDIC. [ABSTRACT FROM AUTHOR]

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this paper, we study a number of flood-risk related problems: given a terrain Σ, represented as a triangulated xy-monotone surface with n vertices, a rain distribution, and a volume of rain ψ, determine which portions of Σ are flooded. We give an overview of efficient algorithms for these problems as well as explore the efficacy and efficiency of these algorithms on real terrains. [ABSTRACT FROM AUTHOR]

This article details the latest advances of the Shor algorithm and its impact on quantum computing. First, an explanation of Peter Shor’s algorithm developed in the mid-1990s is provided followed by a look at Oded Regev’s theoretical improvement, as well as Vinod Vaikuntanathan and Seyoon Ragavan’s contribution with a new scheme. Topics include modular multiplication, the quantum Fourier transform, and asymptotic performance.

The regulatory uses are real: In 2021, more than 100 drug and biologic applications submitted to the FDA included AI/ML components. Keywords: Algorithms; Artificial Intelligence; Bioengineering; Biologics; Biotechnology; Cybersecurity; Cyborgs; Drug Development; Drug Manufacturing; Drugs and Therapies; Emerging Technologies; FDA; Genetic Engineering; Genetically-Engineered Proteins; Government Agencies Offices and Entities; Health and Medicine; Machine Learning; Office of the FDA Commissioner; Public Health; Technology; U.S. Food and Drug Administration EN Algorithms Artificial Intelligence Bioengineering Biologics Biotechnology Cybersecurity Cyborgs Drug Development Drug Manufacturing Drugs and Therapies Emerging Technologies FDA Genetic Engineering Genetically-Engineered Proteins Government Agencies Offices and Entities Health and Medicine Machine Learning Office of the FDA Commissioner Public Health Technology U.S. Food and Drug Administration 497 497 1 05/22/23 20230523 NES 230523 2023 MAY 22 (NewsRx) -- By a News Reporter-Staff News Editor at Clinical Trials Week -- By: Patrizia Cavazzoni, M.D., Director of the Center for Drug Evaluation and Research Artificial intelligence (AI) and machine learning (ML) are no longer futuristic concepts; they are now part of how we live and work. [Extracted from the article]

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity. [ABSTRACT FROM AUTHOR]

This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

This article discusses computational modeling and simulation, with a focus on the development of mathematical software with an emphasis on how it needs to grow in the future. Topics include the optimization necessary to both the mathematical models and algorithms that support computational science software development, with a focus on decreasing operations, packaging software, and performance portability.

The article discusses the development of the algorithm known as norms to address the statistic problem known as nearest neighbor, referencing papers in the "Annual Symposium on Foundations of Computer Science" and "Proceedings of the ACM Symposium on Theory of Computing" journals. An overview of researchers' designing of normed spaces is provided. The uses of data analysis and expander graphs, including in regard to measuring the distance from data points, are discussed.

Since chaotic cryptography has a long-term problem of dynamic degradation, this paper presents proof that chaotic systems resist dynamic degradation through theoretical analysis. Based on this proof, a novel one-dimensional two-parameter with a wide-range system mixed coupled map lattice model (TWMCML) is given. The evaluation of TWMCML shows that the system has the characteristics of strong chaos, high sensitivity, broader parameter ranges and wider chaos range, which helps to enhance the security of chaotic sequences. Based on the excellent performance of TWMCML, it is applied to the newly proposed encryption algorithm. The algorithm realizes double protection of private images under the premise of ensuring efficiency and safety. First, the important information of the image is extracted by edge detection technology. Then the important area is scrambled by the three-dimensional bit-level coupled XOR method. Finally, the global image is more fully confused by the dynamic index diffusion formula. The simulation experiment verified the effectiveness of the algorithm for grayscale and color images. Security tests show that the application of TWMCML makes the encryption algorithm have a better ability to overcome conventional attacks. [ABSTRACT FROM AUTHOR]

The article focuses on the search for algorithms that can reduce the time needed for multiplication problems within the fields of machine learning (ML), deep learning (DL), and artificial intelligence (AI). Topics discussed include the history of matrix multiplication and the data science group DeepMind and their ML approach AlphaTensor.

This article presents information on how algorithms can be utilized to properly disperse natural disaster aid from governments and relief organizations. Topics include the need for automation and data analysis in order to assist in delivering aid in a timely and appropriate manner, how to prevent fraud in relief aid, and also how algorithms can make initial assessments for qualification of need in disaster situations. Organizations highlighted in the article include the American Red Cross, GiveDirectly, and the United Kingdom’s Department for Work & Pensions.

The article presents three principles concerning five challenges to the establishment of trustworthy artificial intelligence (AI), including the development computer languages and algorithms for data modeling, trustworthy-by-construction design, and complex machine learning (ML) representations.

This paper is concerned with the problem of event-triggered optimized control for uncertain nonlinear Itô-type stochastic systems with time-delay and unknown dynamic. By using fuzzy logic systems to approximate two unknown nonlinear functions with the delayed state and current state, respectively. The adaptive identifier is constructed to determine the stochastic system, and the optimized control is designed by using the identifier and adaptive dynamic programming (ADP) of actor-critic architecture. Almost all of the works are concentrated on ADP-based optimal control and it will inevitably cause the complexity of computation and requirements of persistence excitation (PE) assumption. In this paper, the ADP algorithm is obtained based on the negative gradient of a simple positive function (equivalent to the HJB equation), and so the proposed optimal control is simple and can release the PE assumption. Moreover, the event-triggered control approach is proposed to reduce computing burden and communication resources. Furthermore, we prove that the states of system and FLSs parameter errors are semi-globally uniformly ultimately bounded (SGUUB) in mean square via the adaptive identifier and the Lyapunov direct method as well as identifier-actor-critic architecture-based ADP algorithm. Finally, the effectiveness of the proposed method is illustrated through two numerical examples. [ABSTRACT FROM AUTHOR]

The article describes several achievements in the field of theoretical computer science (TCS) and computer research in Latin America. It discusses several areas of research that were developed by regional conferences including LATIN, LAGOS, and Latincrypt. The article discusses research including graph theory, pattern matching and information retrieval, and algorithms.