Miracle-dz/UniFAR-FTUs huggingface dataset:
BIOINFORMATICS ORIGINAL

Motivation: The identiﬁcation of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identiﬁcation. Results: We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-speciﬁc regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more signiﬁcant as more relevant gene sets are formed at ﬁner levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a signiﬁcant analysis procedure is followed to assess the signiﬁcance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we ﬁrst compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identiﬁcation of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-speciﬁc regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. Availability and implementation: The ml-SVR MATLAB package can be downloaded at http:
Mercury in the environment

The naturally occurring levels of mercury in the environment are defined; both terrestrial and aquatic systems are considered. Currently defined limits for mercury in foodstuffs set by the FDA are discussed. The forms of mercury are defined and discussed; it is recognized that methylmercury is by far the most dangerous of the mercury compounds to man. The methylation process in water bodies is discussed; it appears that methylation occurs most rapidly in winter and early spring, under aerobic conditions. The health hazards associated with mercury poisoning, and the symptoms of mercury poisoning are described. Sources of environmental mercury and analytical and pollution control methods applicable to mercury are briefly discussed. 2 tables.
Sequential quasi-Monte Carlo: Introduction for Non-Experts, Dimension Reduction, Application to Partly Observed Diffusion Processes N.CHOPIN M.GERBER

SMC (Sequential Monte Carlo) is a class of Monte Carlo algorithms for filtering and related sequential problems. [16] introduced SQMC (Sequential quasiMonte Carlo), a QMC version of SMC. This paper has two objectives: (a) to introduce Sequential Monte Carlo to the QMC community, whose members are usually less familiar with state-space models and particle filtering; (b) to extend SQMC to the filtering of continuous-time state-space models, where the latent process is a diffusion. A recurring point in the paper will be the notion of dimension reduction, that is how to implement SQMC in such a way that it provides good performance despite the high dimension of the problem.
International Journal of Geographical Information Science

Large-scale global positioning system (GPS) positioning information of ﬂoating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency ﬂoating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.
The Semantic Web: Webizing Knowledge Representation

The World Wide Web opens up new opportunities for the use of knowledge representation: a formal description of the semantic content of Web pages can allow better processing by computational agents. Further, the naming scheme of the Web, using Universal Resource Indicators, allows KR systems to avoid the ambiguities of natural language and to allow linking between semantic documents. These capabilities open up a raft of new possibilities for KR, but also present challenges to some traditional KR assumptions.
11-008 Approximate reinforcement learning : An overview ∗

Reinforcement learning (RL) allows agents to learn how to optimally interact with complex environments. Fueled by recent advances in approximation-based algorithms, RL has obtained impressive successes in robotics, artificial intelligence, control, operations research, etc. However, the scarcity of su rvey papers about approximate RL makes it difficult for newcomers to grasp this intricate field. With the present overview, we take a step toward alleviating this situation. We review methods for approximate RL, starting from their dynamic programming roots and organizing them into three major classes: approximate value iteration, policy iteration, and policy search. Each class is subdivided into representative categories, highlighting among others offline and online algorithms, policy gradient methods, and simulation-based techniques. We also compare the different categories of methods, and outline possible ways to enhance the reviewed algorithms.
A systematic study on parameter correlations in large scale duplicate document detection 1

Although much work has been done on duplicate document detection (DDD) and its applications, we observe the absence of a systematic study on the performance and scalability of large-scale DDD algorithms. It is still unclear how various parameters in DDD correlate mutually, such as similarity threshold, precision/recall requirement, sampling ratio, and document size. This paper explores the correlations among several most important parameters in DDD and the impact of sampling ratio is of most interest since it heavily affects the accuracy and scalability of DDD algorithms. An empirical analysis is conducted on a million HTML documents from the TREC . GOV collection. Experimental results show that even using the same sampling ratio, the precision of DDD varies greatly on documents with different sizes. Based on this observation, we propose an adaptive sampling strategy for DDD, which minimizes the sampling ratio with the constraint of a given precision requirement. We believe that the insights from our analysis are helpful for guiding the future large scale DDD work.
This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation

RedLeaf is a new operating system developed from scratch in Rust to explore the impact of language safety on operating system organization. In contrast to commodity systems, RedLeaf does not rely on hardware address spaces for isolation and instead uses only type and memory safety of the Rust language. Departure from costly hardware isolation mechanisms allows us to explore the design space of systems that embrace lightweight ﬁne-grained isolation. We develop a new abstraction of a lightweight language-based isolation domain that provides a unit of information hiding and fault isolation. Domains can be dynamically loaded and cleanly terminated, i.e., errors in one domain do not affect the execution of other domains. Building on RedLeaf isolation mechanisms, we demonstrate the possibility to implement end-to-end zero-copy, fault isolation, and transparent recovery of device drivers. To evaluate the practicality of RedLeaf abstractions, we implement Rv6, a POSIX-subset operating system as a collection of RedLeaf domains. Finally, to demonstrate that Rust and ﬁne-grained isolation are practical—we develop efﬁcient versions of a 10Gbps Intel ixgbe network and NVMe solid-state disk device drivers that match the performance of the fastest DPDK and SPDK equivalents.
In addition to the bias introduced

I M A G E B Y S V I A T L A N A S H E I N A OU R INHERENT HUMAN tendency of favoring one thing or opinion over another is reflected in every aspect of our lives, creating both latent and overt biases toward everything we see, hear, and do. Any remedy for bias must start with awareness that bias exists; for example, most mature societies raise awareness of social bias through affirmative-action programs, and, while awareness alone does not completely alleviate the problem, it helps guide us toward a solution. Bias on the Web reflects both societal and internal biases within ourselves, emerging in subtler ways. This article aims to increase awareness of the potential effects imposed on us all through bias present in Web use and content. We must thus consider and account for it in the design of Web systems that truly address people’s needs. Bias has been intrinsically embedded in culture and history since the beginning of time. However, due to the rise of digital data, it can now spread faster than ever and reach many more people. This has caused bias in big data to become a trending and controversial topic in recent years. Minorities, especially, have felt the harmful effects of data bias when pursuing life goals, with outcomes governed primarily by algorithms, from mortgage loans to advertising personalization. While the obstacles they face remain an important roadblock, bias affects us all, though much of the time we are unaware it exists or how it might (negatively) influence our judgment and behavior. The Web is today’s most prominent communication channel, as well as a place where our biases converge. As social media are increasingly central to daily life, they expose us to influencers we might not have encountered previously. This makes understanding and recognizing bias on the Web more essential than ever. My main goal here is thus to raise the awareness level for all Web biases. Bias awareness would help us design better Web-based systems, as well as software systems in general.
Design and Implementation of Optimal Winner Determination Algorithm in Combinatorial e-Auctions

search area is heuristic and meta heuristic approaches. Especially in issue that the exploitation of combinatorial status in the large scale search area prevents the solution of the problem via classical calculating methods, so such problems is NP-complete. in this research, the problem of winner determination in combinatorial auctions have been formulated and by assessing older heuristic functions, we solve the problem by using of genetic algorithm and would show that this new method would result in better performance in comparison to other heuristic function such as simulated annealing greedy approach.
dartR v2: An accessible genetic analysis platform for conservation, ecology and agriculture

Innumerable approaches to analyse genetic data are now available to guide conservation, ecological and agricultural projects. However, streamlined and accessible tools are needed to bring these approaches within the reach of a broader user base. dartR was released in 2018 to lessen the intrinsic complexity of analysing single nucleotide polymorphisms (SNPs) and dominant markers (presence/absence of amplified sequence tags) by providing user‐friendly data quality control and marker selection functions. dartR users have grown steadily since its release and provided valuable feedback on their interaction with the package allowing us to enhance dartR capabilities. Here, we present Version 2 of dartR. In this version, we substantially increased the number of available functions from 45 to 144. In addition to improved functionality, we focused on enhancing the user experience by extending plot customisation, function standardisation, increasing user support and function speed. dartR provides functions for various stages in analysing genetic data, from data manipulation to reporting. dartR provides many functions for importing, exporting and linking to other packages, to provide an easy‐to‐navigate conduit between data generation and analysis options already available via other packages. We also implemented simulation functions whose results can be analysed seamlessly with several other dartR functions. As more methods and approaches mature to inform conservation, we envision that accessible platforms to analyse genetic data will play a crucial role in translating science into practice.
Gestalt of an Approach for Multidimensional Data Mining on Concept Taxonomy Forest to Discover Association Patterns with various Data Granularities

Data Mining is one of the most significant tools for discovering as association rules. Yet, there are some drawbacks in conventional mining techniques. Since most of them perform the plain mining based on pre-defined schemata through the data warehouse as a whole, a re- scan must be done whenever new attributes are added. Secondly, an association rule may be true on a certain granularity but fail on a smaller one. Last but not least, they are usually designed specifically to find either frequent or infrequent rules. This article aims at providing a novel data schema and an algorithm to solve aforementioned problems. A forest of concept taxonomies is used as the data structure for discovery of associations rules that consist of concepts picked up from various taxonomies. Then, the mining process is formulated as a combination of finding the large itemsets, generating, updating and output the association patterns. Crucial mechanisms in each step will be clarified. At last, this paper presents experimental results regarding efficiency, scalability etc. of the proposed approach to prove the advents of the approach.
Meaningful Modeling: What's the Semantics of "Semantics"?

The Unified Modeling Language (UML) is a complex collection of mostly diagrammatic notations for software modeling, and its standardization has prompted an animated discussion about UML's semantics and how to represent it. We have thus set out to clarify some of the notions involved in defining modeling languages, with an eye toward the particular difficulties arising in defining UML. We are primarily interested in distinguishing a language's notation, or syntax, from its meaning, or semantics, as well as recognizing the differences between variants of syntax and semantics in their nature, purpose, style, and use.
Expressing graph algorithms using generalized active messages

Recently, graph computation has emerged as an important class of high-performance computing application whose characteristics differ markedly from those of traditional, compute-bound kernels. Libraries such as BLAS, LAPACK, and others have been successful in codifying best practices in numerical computing. The data-driven nature of graph applications necessitates a more complex application stack incorporating runtime optimization. In this paper, we present a method of phrasing graph algorithms as collections of asynchronous, concurrently executing, concise code fragments which may be invoked both locally and in remote address spaces. A runtime layer performs a number of dynamic optimizations, including message coalescing, message combining, and software routing. We identify a number of common patterns in these algorithms, and explore how this programming model can express those patterns. Algorithmic transformations are discussed which expose asyn- chrony that can be leveraged by the runtime to improve performance and reduce resource utilization. Practical implementations and performance results are provided for a number of representative algorithms.
Programming up to Congruence Vilhelm Sjöberg

This paper presents a dependently-typed programming language that uses an adaptation of a congruence closure algorithm for proof and type inference. While most dependently-typed languages automatically use equalities that follow from β-reductions but do not automatically use known assumptions from the context, our language does the opposite. It uses assumptions but does not automatically reduce expressions. Our work includes the specification of the language via a bidirectional type system, which works “up-to-congruence,” and an algorithm for elaborating expressions in this language to an explicitly typed core language. We prove that our elaboration algorithm is complete with respect to the source type system, and always produces well typed terms in the core language. This algorithm has been implemented in the ZOMBIE language, which includes general recursion, irrelevant arguments, heterogeneous equality and datatypes.
The Diiculty of Random Attribute Noise

This paper studies the robustness of pac learning algorithms when the instance space is f0; 1g n , and the examples are corrupted by purely random noise aaecting only the instances (and not the labels). In the past, connicting results on this subject have been obtained|the \best agreement" rule can only tolerate small amounts of noise, yet in some cases large amounts of noise can be tolerated. We show that the truth lies somewhere between these two alternatives. For uniform attribute noise, in which each attribute is ipped independently at random with the same probability, we present an algorithm that pac learns monomials for any (unknown) noise rate less than 1=2. Contrasting this positive result, we show that nonuniform random attribute noise, where each attribute i is ipped randomly and independently with its own probability p i , is nearly as harmful as malicious noise|no algorithm can tolerate more than a very small amount of such noise.
KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen

Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for neutrinoless double beta decay ($0\nu\beta\beta$). Using a simplified background model for KamLAND we show that KamNet outperforms a conventional CNN on benchmarking MC simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.
TSS, COD and priority pollutants in stormwater – Event fluxes modelling using conceptual approach

The original and modified versions of a conceptual model have been evaluated for predicting the stormwater pollutant event fluxes (M/ha). studied pollutants include the total suspended solids TSS, the chemical oxygen demand COD and the European Water Framework Directive (WFD, 2000/60/CE) priority pollutants. Both versions of the model contain two modules: i) the buildup module which allows estimating the pollutant mass accumulated on the catchment surface during the dry period preceding a rainfall event and ii) the washoff module which is used to estimate the pollutant events masses contained in the runoff volume. Both versions of the model contain the same washoff module and differ by the buildup module: exponential asymptotic for the original version and logarithmic for the modified version. The results show that both the two evaluated versions of the model have the satisfying performances and that the level of these performances depends to the pollutant type and the concerned catchment characteristics. However, having only two parameters to be estimated against four, the performances of the modified model are globally higher than these of the original model.
Software Project Management Using Machine Learning Technique - A Review

Project management planning assessment is of great significance in project performance activities. The creation of project management cannot be effectively handled without a practical and rational strategy. This paper offers a large-scale review analysis of articles based on machine learning and risk evaluation management for software projects. The reviews are presented and classified into two groups. The first group covers project management analysis and survey articles. The second group contains works on the steps and experimental criteria that are widely used in the management of machine learning projects. The paper provides a deeper insight and an important framework for future work in the project risk assessment, highlights the estimation of project risk using machine-learning is more efficient in reducing the project's fault and provides a further way to reduce the probability chances effectively and to increase the software development performance ratio.
DeepEar: Sound Localization with Binaural Microphones

Binaural microphones, referring to two microphones with artificial human-shaped ears, are pervasively used in humanoid robots and hearing aids improving sound quality. In many applications, it is crucial for such robots to interact with humans by finding the voice direction. However, sound source localization with binaural microphones remains challenging, especially in multi-source scenarios. Prior works utilize microphone arrays to deal with the multi-source localization problem. Extra arrays yet incur higher deployment costs and take up more space. However, human brains have evolved to locate multiple sound sources with only two ears. Inspired by this fact, we propose DeepEar, a binaural microphone-based localization system that can locate multiple sounds. To this end, we develop a neural network to mimic the acoustic signal processing pipeline of the human auditory system. Different from hand-crafted features used in prior works, DeepEar can automatically extract useful features for localization. More importantly, the trained neural networks can be extended and adapted to new environments with a minimum amount of extra training data. Experiment results show that DeepEar can substantially outperform the state-of-the-art deep learning approach, with a sound detection accuracy of 93.3% and an azimuth estimation error of 7.4 degrees in multisource scenarios.
Clustering Unstructured Text Documents Using Fading Function

— Clustering unstructured text documents is an important issue in data mining community and has a number of applications such as document archive filtering, document organization and topic detection and subject tracing. In the real world, some of the already clustered documents may not be of importance while new documents of more significance may evolve. Most of the work done so far in clustering unstructured text documents overlooks this aspect of clustering. This paper, addresses this issue by using the Fading Function. The unstructured text documents are clustered. And for each cluster a statistics structure called Cluster Profile (CP) is implemented. The cluster profile incorporates the Fading Function. This Fading Function keeps an account of the time-dependent importance of the cluster. The work proposes a novel algorithm Clustering n-ary Merge Algorithm (CnMA) for unstructured text documents, that uses Cluster Profile and Fading Function. Experimental results illustrating the effectiveness of the proposed technique are also included.