Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression of short-read sequences using path encoding

Storing, transmitting, and archiving the amount of data produced by next generation sequencing is becoming a significant computational burden. For example, large-scale RNA-seq meta-analyses may now routinely process tens of terabytes of sequence. We present here an approach to biological sequence compression that reduces the difficulty associated with managing the data produced by large-scale transcriptome sequencing. Our approach offers a new direction by sitting between pure reference-based compression and reference-free compression and combines much of the benefit of reference-based approaches with the flexibility of de novo encoding. Our method, called path encoding, draws a connection between storing paths in de Bruijn graphs — a common task in genome assembly — and contextdependent arithmetic coding. Supporting this method is a system, called a bit tree, to compactly store sets of kmers that is of independent interest. Using these techniques, we are able to encode RNA-seq reads using 3% – 11% of the space of the sequence in raw FASTA files, which is on average more than 34% smaller than recent competing approaches. We also show that even if the reference is very poorly matched to the reads that are being encoded, good compression can still be achieved.
des publications du CNRC

Business process management systems traditionally focused on supporting the modeling and automation of business processes, with the objective of enabling fast and cost-effective process execution. As more and more processes become automated, customers are increasingly interested in managing process execution. This paper presents a set of concepts and a methodology towards business process intelligence using dynamic process performance evaluation, including measurement models based on ABM (Activity Based Management) and a dynamic enterprise process performance evaluation methodology. The proposed measurement models support the analysis of six process flows within a manufacturing enterprise including activity flow, information flow, resource flow, cost flow, cash flow, and profit flow, which are crucial for enterprise managers to control the process execution quality and detect problems and areas for improvements. The proposed process performance evaluation methodology uses time, quality, service, cost, speed, efficiency , and importance as seven evaluation criteria. A prototype system supporting dynamic enterprise process modeling, analysis of six process flows, and process performance prediction has been implemented to validate the proposed methodology.
A Web-Based Environment for Learning Normalization of Relational Database Schemata

Database normalization is a technique for designing relational database tables to minimize duplication of information in order to safeguard the database against certain types of logical or structural problems, namely data anomalies. Therefore database normalization is a central topic in database theory, and its correct understanding is crucial for students. Unfortunately, the subject it is often considered to be dry and purely theoretical and is often not well received by students. A web-based environment for learning normalization of relational database schemata is developed to give students an interactive hands-on experience in database normalization process. It also provides lecturers with an easy way for creating and testing assignments on the subject. The environment is suitable for relational database and design and data management courses. This report describes the design and development of LDBN (Learn DataBase Normalization) a reference implementation of the learning environment. It also discuss problems that lie within educational and web-based software development.
eScholarship@UMassChan eScholarship@UMassChan

Background: Medication and adverse drug event (ADE) information extracted from electronic health record (EHR) notes can be a rich resource for drug safety surveillance. Existing observational studies have mainly relied on structured EHR data to obtain ADE information; however, ADEs are often buried in the EHR narratives and not recorded in structured data. Objective: To unlock ADE-related information from EHR narratives, there is a need to extract relevant entities and identify relations among them. In this study, we focus on relation identification. This study aimed to evaluate natural language processing and machine learning approaches using the expert-annotated medical entities and relations in the context of drug safety surveillance, and investigate how different learning approaches perform under different configurations. Methods: We have manually annotated 791 EHR notes with 9 named entities (eg, medication, indication, severity, and ADEs) and 7 different types of relations (eg, medication-dosage, medication-ADE, and severity-ADE). Then, we explored 3 supervised machine learning systems for relation identification: (1) a support vector machines (SVM) system, (2) an end-to-end deep neural network system, and (3) a supervised descriptive rule induction baseline system. For the neural network system, we exploited the state-of-the-art recurrent neural network (RNN) and attention models. We report the performance by macro-averaged precision, recall, and F1-score across the relation types. Results: Our results show that the SVM
DEAS 2005: workshop on the design and evolution of autonomic application software

Understanding software engineering issues for autonomic computing systems is critical for the software and information technology sectors, which are continually challenged to reduce the complexity of their systems. To be autonomic, a system must know itself as well as its boundaries and its environment, configure and reconfigure itself, continually optimize itself, recover or heal from malfunction, protect itself, and function in a heterogeneous world-while keeping its complexity hidden from the user. The goal of this workshop is to bring together researchers and practitioners, who investigate concepts, methodologies, techniques, technologies, and tools to design and evolve autonomic software. Categories & Subject Descriptors: D.2. 10 [Software Design]: D.2. 1 1 [Software Architectures] General Terms: Design
Separation of telomere protection from length regulation by two different point mutations at amino acid 492 of RTEL1

RTEL1 is an essential DNA helicase that plays multiple roles in genome stability and telomere length regulation. A variant of RTEL1 with a lysine at position 492 is associated with short telomeres in Mus spretus, while a conserved methionine at this position is found in M. musculus, which has ultra-long telomeres. In humans, a missense mutation at this position (RTEL1M492I) causes a fatal telomere biology disease termed Hoyeraal-Hreidarsson syndrome (HHS). We previously described a M. musculus mouse model termed ‘Telomouse’, in which changing methionine 492 to a lysine (M492K) shortened the telomeres to their length in humans. Here, we report on the derivation of a mouse strain carrying the M492I mutation, termed ‘HHS mouse’. The HHS mouse telomeres are not as short as those of Telomice but nevertheless they display higher levels of telomeric DNA damage, fragility and recombination, associated with anaphase bridges and micronuclei. These observations indicate that the two mutations separate critical functions of RTEL1: M492K mainly reduces the telomere length setpoint, while M492I predominantly disrupts telomere protection. The two mouse models enable dissecting the mechanistic roles of RTEL1 and the different contributions of short telomeres and DNA damage to telomere biology diseases, genomic instability, cancer, and aging.
Acquiring Mobile Robot Behaviors by Learning Trajectory Velocities

The development of robots that learn from experience is a relentless challenge confronting artificial intelligence today. This paper describes a robot learning method which enables a mobile robot to simultaneously acquire the ability to avoid objects, follow walls, seek goals and control its velocity as a result of interacting with the environment without human assistance. The robot acquires these behaviors by learning how fast it should move along predefined trajectories with respect to the current state of the input vector. This enables the robot to perform object avoidance, wall following and goal seeking behaviors by choosing to follow fast trajectories near: the forward direction, the closest object or the goal location respectively. Learning trajectory velocities can be done relatively quickly because the required knowledge can be obtained from the robot’s interactions with the environment without incurring the credit assignment problem. We provide experimental results to verify our robot learning method by using a mobile robot to simultaneously acquire all three behaviors.
ZONAL AND ASSOCIATED FUNCTIONS ON SO 0 ( p, q ) GROUPS

Explicit expressions for associated spherical functions of SO ( p, q ) matrix groups are obtained using a generalized hypergeometric series of two variables. In this paper we present explicit expressions for zonal functions of de Sitter groups and the group of conformal invariance. Moreover, we present a theorem on the transformation of derivative of distributions, concentrated on smooth surfaces, with respect to inﬁnite-dimensional Lie group C ∞ ( R n , GL ( n )).
Potential applications of microalgae bacteria consortia for waste treatment and valuable bioproducts

: The application of microalgae and bacteria in wastewater treatment has attracted interest due to the greater environmental adaptability and stability resulting from their interactions, exceed those obtained with microalgae al sustainability and economic competitiveness. This manuscript aims to support existing and relevant literature on the use of microalgae and bacteria. As a result, numerous scholars and authors have been emphasizing recent research on the biotechnology of algae and bacteria, so this revision will be useful to advance and facilitate the technological development of biological processes
Transformers on Dynamical Systems - An Exploration of In-context Learning

Large Language Models (LLMs) have shown to be highly effective at performing in-context learning, where, given a prompt, the model can learn from the prompt and complete the sequence without needing to perform additional gradient steps or fine-tuning. In this project, we investigated the ability of Trans-former models to perform in-context learning on linear dynamical systems. We first experimented with Transformers trained on a single system, where the task for evaluation was to filter noise on trajectories sampled from the same system. Then, we experimented with Transformers trained on multiple systems of the same type, where the task was to perform simultaneous system identification and filtering. This is still very much a work in progress, and I hope to continue to work on this in the coming weeks.
Mobile Apps for COVID-19 Detection and Diagnosis for Future Pandemic Control: Multidimensional Systematic Review

Background: In the modern world, mobile apps are essential for human advancement, and pandemic control is no exception. The use of mobile apps and technology for the detection and diagnosis of COVID-19 has been the subject of numerous investigations, although no thorough analysis of COVID-19 pandemic prevention has been conducted using mobile apps, creating a gap. Objective: With the intention of helping software companies and clinical researchers, this study provides comprehensive information regarding the different fields in which mobile apps were used to diagnose COVID-19 during the pandemic. Methods: In this systematic review, 535 studies were found after searching 5 major research databases (ScienceDirect, Scopus, PubMed, Web of Science, and IEEE). Of these, only 42 (7.9%) studies concerned with diagnosing and detecting COVID-19
Design of optimal groundwater monitoring well network using stochastic modeling and reduced‐rank spatial prediction

A method for the stochastic design of groundwater quality observation well network is presented. The method uses calibration‐constrained Null‐space Monte Carlo analysis for the stochastic simulation of the reduction ratio of peak concentration and the time corresponding to this in an injection well field. The numerical groundwater model simulations are constrained with a limited amount of field measurements. The objective of the monitoring network design is to identify optimal monitoring locations that allow for prediction of spatial fields from the data collected at limited number of points in the spatial domain. These locations need to be robust to different possible outcomes simulated using the stochastic model runs, and result in good spatial predictions, regardless of which one of the many possibilities turned out to be the true representation of nature. Multiple simulated fields of concentration and time are used to identify a small set of empirical orthogonal functions (spatial basis functions) for reduced‐rank prediction of the spatial patterns in these two fields. The Differential Evolution algorithm was used to find the monitoring locations that allowed for optimal reconstruction of all the simulated fields (potential future states of reality) from the set of empirical orthogonal functions. The applicability is demonstrated for designing a monitoring network for an injection well field. Optimal locations of 10 monitoring wells were identified. The method has the capability to simultaneously identify the optimal locations and inform optimal times for monitoring reduction ratio of peak concentration. The method is flexible to iteratively combine stochastic modeling and monitoring for optimal groundwater management.
Modeling the effect of surface active elements on weld pool fluid flow, heat transfer and geometry

The influence of sulfur on the heat flow and fluid flow and the transient development of the weld pool was quantitatively evaluated for two heats of Type 304 stainless steel containing 90 and 240 ppM sulfur, respectively. A transient heat transfer model was utilized to simulate the heat flow and fluid flow in the weld pool during stationary laser and gas tungsten arc (GTA) spot welds. A recently developed surface tension model was utilized to calculate the temperature coefficient of surface tension (d.gamma./dT) as a function of temperature and sulfur content. This allows a realistic evaluation of the effect of surface-active elements on the fluid flow and weld geometry. The computed results indicate that during stationary welding, the weld pool surface temperatures are fairly high. As a consequence, the temperature coefficient of surface tension becomes negative and a radially outward or a bifurcated flow of weld metal occurs even when the weld pool contains a significant amount of sulfur. The predictions of the model were verified by comparing the calculated and experimentally observed fusion zone geometry. The results indicate very good agreement between the predicted and experimentally observed fusion zone geometry, for both laser and gas tungsten arc (GTA) spotmore » welds, due partly to the accurate treatment of surface tension gradient driven flows. 21 refs., 7 figs., 2 tabs. « less
ECONOMIC ACTIVITY ACROSS SPACE: A SUPPLY AND DEMAND APPROACH

In October 1929, the Dutch electronics firm Philips approached John Maynatd Keynes to write confidential reports on the state of the British and world economies, which he did from January 1930 to November 1934, at first monthly and then quarterly. These substantial reports (Keynes’s November 1931 report was twelve typed pages) show Keynes narrating the Great Depression in real time, as the world went through the US slowdown after the Wall Street crash, the Credit-Anstalt collapse in Austria, the German banking crisis (summer 1931), Britain’s departure from the gold exchange standard in August and September 1931, the US banking crisis leading to the Bank Holiday of March 1933, the London Economic Conference of 1933, and the coming of the New Deal. This series of reports has not been discussed in the literature, though the reports and surrounding correspondence are in the Chadwyck-Healey microfilm edition of the Keynes Papers. We examine Keynes’s account of the unfolding events of the early 1930s, his insistence that the crisis would be more severe and long-lasting than most observers predicted, and his changing position on whether monetary policy would be sufficient to promote recovery and relate his reading of contemporary events to his theoretical development.
Information as a Key Resource: The Influence of RMA and Network-Centric Operations on the Transformation of the German Armed Forces

Abstract : Information technology has had an almost unparalleled influence on the modernization and transformation of the armed forces in Western societies in recent years. Many areas of the US military in particular have been specially equipped with modern network-shaped technology since the 1990s, with the aim of achieving a qualitatively new degree of precision and speed in military operations. The Revolution in Military Affairs (RMA), and its implementation in the doctrine of Network-Centric Warfare (NCW), have become new military strategy paradigms for the US armed forces. Along with the security policy redefinition of the threat situation following the end of the Cold War, this development constitutes the strongest impetus for change in the armed forces of Western European countries. This discussion must begin with a warning that the development being described is more complex than it may appear at first glance. The desire to attribute the transformation of the armed forces solely to technological innovation and a technodeterministic impetus is one-dimensional and falls far short of the truth. The concept underpinning the revolution in military affairs comprises a multitude of factors such as security policy, military strategy and socio-political decisions in addition to the technological ones, all of which exert their own influence in tandem with the new technology. For this reason, an explanation that attributes the changes in the armed forces of Western Europe solely to the considerable speed of technological innovation must necessarily be incomplete. Instead, technological progress appears to be embedded in political, social and strategic changes and decisions. A contribution that seeks to analyze the influence of RMA must consequently always take into account a whole cluster of causes and effects. New security policy constellations, new actors on the world stage and an increasing asymmetry of force form the background to contemporary armed conflicts.
Mechanical and tribological behaviour of coconut shell char reinforced polymer cosmposite

Recently conductive polymer composites obtained by filling polymer matrixes with various Carbon blacks were also reported. Particulate fillers of which carbon black is notable example are widely used as reinforcing fillers in polymer industry. These fillers are added to polymers to achieve desirable and enhance the product service qualities. Commercially available carbon blacks are obtained from thermal cracking of natural gas and furnace black produced by incomplete combustion of oil filled stocks. This carbon black is relatively expensive due to its dependence on dwindling supply of crude oil. It is therefore essential to develop viable alternative source of fillers from renewable resources such as agricultural waste, bamboo stem, oil palm empty fruit bunches and coconut shells which are carbonaceous in nature and rich in organic materials. This biomass can be converted into carbon black thereby reducing unwanted, low value agricultural reduces and underutilized crop into useful, high value materials. Increase of environmental awareness has led to a growing interest in researching ways of an effective utilization of coconut shell, from which shell is particularly valuable due to its high contains 70% carbon, 1% ash, 30.1% lignin, 19.8% cellulose and 68.7% hemicellulose. It is felt that the value of this agricultural residue can be upgraded by bonding with resin to produce composite suitable for tribological applications. Keeping this in view the present work has been under taken to develop a polymer matrix composite (epoxy resin) using coconut shell char and to study its tribological behavior, the new hard porous carbon material coconut shell char has been developed by carburizing coconut shell as the main raw material at three different temperature range 600°C and 8000C. The composite are prepared with different volume raction of coconut shell Char. Experiments have been conducted under laboratory condition to assess the erosive wear behavior of the developed composite.
Zen and the Art of Doughnut Economics: When Limits are Zen and the Art of Doughnut Economics: When Limits are Strangely Liberating Strangely Liberating

In meditation we simply offer ourselves—all our attention-energy—to appreciating the moment in which we find ourselves. It is attending in the sense of vigilant caring—our most primordial mode of contribution… . Meditation can be seen, then, as an alternative technology—an alternative to our technological bias toward control. Meditation breaks down the cycle of our wanting. (Hershock 1999, p. 280)
in Computer

— A Chatbot is a computer program which conducts a conversation via auditory or textual methods. Chatbots are typically used in dialog systems for various practical purposes including customer service or information acquisition. Many of the chatbots today are not efficient in producing the required results quickly. This project aims to build a chatbot aka Rattata that quickly responds to a given input query. The first build of Rattata focuses on using a method of scanning for keywords within the input, then pull a reply with the most matching keywords, or the most similar wording pattern, from a database. The database is an xml-based dataset from stack-exchange. The dataset is clustered using the K-Means machine learning algorithm, to make the dataset more readable, with only the required attributes. Then the input query is processed using query processing algorithms like Krovetz Stemming algorithm and the Parallel Stemmer algorithm. After processing the input query, the required data is fetched from the database and displayed to the end user. In the second build we focus on improving the processing of the keywords using Natural Language Processing.