Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Feature Selection using Fuzzy Combination of Feature Subset for Transcriptome Data

Applying machine learning algorithms directly on high dimensional datasets, like those encountered in transcriptome analysis, may lead to high time complexity and low performance of learning models, especially when the number of samples is small compared to the dimensionality. Selecting the optimal set of features then becomes an essential task for such datasets. Filter methods are one of the main class of techniques used for feature selection wherein a score is assigned to features based on criteria such as information gain, statistical measures or similarity based measures and then selects the best scored features. Using filter methods on the complete dataset results in features that have good performance over the dataset but might perform poorly in certain regions of the data, which affects accuracy for data points of those regions. To overcome this degradation in performance, we propose two novel methods to assign a robust score by using the fuzzy combination of the region-specific optimal feature subsets obtained using a standard feature selection algorithm (we use mRMR for this paper). We compare the result with state-of-the-art feature selection algorithm, mRMR (Minimum Redundancy Maximum Relevance) in the terms of accuracy on certain standard datasets.
Agile Requirements Engineering with User Stories

90% of agile practitioners employ user stories for capturing requirements. Of these, 70% follow a simple template when creating user stories: As a <role> I want to <action>, [so that <benefit>]. User stories' popularity among practitioners and their simple yet strict structure make them ideal candidates for automatic reasoning based on natural language processing. In our research, we have found that circa 50% of real-world user stories contain easily preventable errors that may endanger their potential. To alleviate this problem, we have created methods, theories and tools that support creating better user stories. This tutorial combines our previous work into a pipeline for working with user stories: (1) The basics of creating user stories, and their use in requirements engineering; (2) How to improve user story quality with the Quality User Story Framework and AQUSA tool; and (3) How to generate conceptual models from user stories using the Visual Narrator and the Interactive Narrator tools. Our toolset is demonstrated with results obtained from 20+ software companies employing user stories.
Health and societal implications of medical and technological advances

Scientific and technological breakthroughs are transforming the future of medicine and health, but they inevitably carry risks and have societal implications that need to be addressed proactively. Scientific and technological breakthroughs are transforming the future of medicine and health, but they inevitably carry risks and have societal implications that need to be addressed proactively.
Hopf-Hopf Bifurcation and Chaos in A Ring Neural Oscillator due to A Single Shortcut

Bifurcations and chaos in a ring of seven sigmoidal neurons with unidirectional inhibitory coupling caused by a single shortcut are examined. In the absence of shortcuts, the network has the form of a ring oscillator and has a stable and an unstable periodic solution, which are generated through the Hopf bifurcations from the origin. In the presence of an inhibitory shortcut from the third neuron to the first neuron, the loci of the two periodic solutions intersect at a codimension-two Hopf-Hopf bifurcation point in the parameter plane. The loci of the Neimark-Sacker bifurcations of the periodic solutions emanate from the Hopf-Hopf bifurcation point and stable quasiperiodic solutions are generated. Multiple chaotic attractors are generated through period-doubling cascades of periodic solutions in the Arnold’s tongues emanating from the loci of the Neimark-Sacker bifurcations. The crises of the chaotic attractors occur so that transient chaos and crisis-induced intermittency are observed. A single shortcut brings about these rich dynamical properties in a simple ring neural network.
Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

Understanding music popularity is important not only for the artists who create and perform music but also for the music-related industry. It has not been studied well how music popularity can be defined, what its characteristics are, and whether it can be predicted, which are addressed in this paper. We first define eight popularity metrics to cover multiple aspects of popularity. Then, the analysis of each popularity metric is conducted with long-term real-world chart data to deeply understand the characteristics of music popularity in the real world. We also build classification models for predicting popularity metrics using acoustic data. In particular, we focus on evaluating features describing music complexity together with other conventional acoustic features including MPEG-7 and Mel-frequency cepstral coefficient (MFCC) features. The results show that, although room still exists for improvement, it is feasible to predict the popularity metrics of a song significantly better than random chance based on its audio signal, particularly using both the complexity and MFCC features.
Active unidirectional network flow generates a packet molecular transport in cells

Internet, social media, neuronal or blood vessel are organized in complex networks. These networks are characterized by several quantities such as the underlying graph connectivity (topology), how they grow in time, scaling laws or by the mean time a random search can cover a network and also by shortest path between two nodes. We present here a novel type of network property based on a unidirectional transport mechanism occurring in the Endoplasmic Reticulum network found in the cell cytoplasm. This mechanism is an active-waiting transportation, where molecules have to wait a random time before being transported from one node to the next one. We find that the consequence of this unusual network transportation is that molecules travel together by recurrent packets, which quite a large deviation behavior compared to classical propagation in graphs. To conclude, this form of transportation is associated with an efficient and robust molecular redistribution inside cells.
Deconstructing RDMA-enabled Distributed Transactions: Hybrid is Better!

There is currently an active debate on which RDMA primitive (i.e., one-sided or two-sided) is optimal for distributed transactions. Such a debate has led to a number of optimizations based on one RDMA primitive, which was shown with better performance than the other. In this paper, we perform a systematic comparison between different RDMA primitives with a combination of various optimizations using representative OLTP workloads. More specifically, we first implement and compare different RDMA primitives with existing and our new optimizations upon a single well-tuned execution framework. This gives us insights into the performance characteristics of different RDMA primitives. Then we investigate the implementation of optimistic concurrency control (OCC) by comparing different RDMA primitives using a phase-by-phase approach with various transactions from TPC-C, SmallBank, and TPC-E. Our results show that no single primitive (one-sided or two-sided) wins over the other on all phases. We further conduct an end-to-end comparison of prior designs on the same codebase and find none of them is optimal. Based on the above studies, we build DrTM+H, a new hybrid distributed transaction system that always embraces the optimal RDMA primitives at each phase of transactional execution. Evaluations using popular OLTP workloads including TPC-C and SmallBank show that DrTM+H achieves over 7.3 and 90.4 million transactions per second on a 16-node RDMA-capable cluster (ConnectX-4) respectively, without locality assumption. This number outperforms the pure one-sided and two-sided systems by up to 1.89X and 2.96X for TPC-C with over 49% and 65% latency reduction. Further, DrTM+H scales well with a large number of connections on modern RDMA network.
Finding Options that Minimize Planning Time

We formalize the problem of selecting the optimal set of options for planning as that of computing the smallest set of options so that planning converges in less than a given maximum of value-iteration passes. We first show that the problem is NP-hard, even if the task is constrained to be deterministic---the first such complexity result for option discovery. We then present the first polynomial-time boundedly suboptimal approximation algorithm for this setting, and empirically evaluate it against both the optimal options and a representative collection of heuristic approaches in simple grid-based domains including the classic four-rooms problem.
Ranking-Embedded Transfer Canonical Correlation Analysis for Person Re-Identification

Person re-identification (re-ID) seeks to match the identical individuals across different cameras and is still a challenging visual task due to substantial variances of person appearance in complex scenarios. Different from most of conventional person re-ID methods, which generally reduce person re-ID task to either a multi-view learning problem or a multi- domain learning problem alone, this paper treats such a task as a multi-view multi-domain (MVMD) learning problem to exploit the both benefits by refreshing canonical correlation analysis (CCA) with two improvements, termed as ranking-embedded transfer CCA (RTCCA). Specifically, to bridge the semantic gap between different views, we first embed a ranking weight matrix into CCA to strength the correlations among the multi-view images of the same identity and simultaneously to weaken that of different identities. Furthermore, we utilize the well-known distribution metric maximum mean discrepancy (MMD) as a regularization term to reduce the domain shift between training set and testing set. More importantly, the two improvements benefit from each other and the joint merit can further boost the re-ID performance. Experiments on three benchmarks verify the efficacy of the proposed RTCCA when compared with the recently representative baseline person re-ID methods.
PUF-AES-PUF: a novel PUF architecture against non-invasive attacks

In this letter, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their challenge-to-response pairs (CRPs) against machine learning attacks. Moreover, an internal confidential data is added to the secret key of the AES cryptographic circuit in the new PUF architecture to update the secret key in real-time against side-channel attacks. As shown in the results, even if 1 million number of data are enabled by the adversary to implement machine learning or side-channel attacks, the proposed PUF can not be cracked. By contrast, only 5,000 (1,000) number of data are sufficient to leak the confidential information of a conventional PUF via machine learning (side-channel) attacks.
Time-Scale Expansion-Based Approximated Optimal Control for Underactuated Systems Using Projection Neural Networks

In this paper, a time-scale expansion-based scheme is proposed for approximately solving the optimal control problem of continuous-time underactuated nonlinear systems subject to input constraints and system dynamics. By time-scale Taylor approximation of the original performance index, the optimal control problem is relaxed into an approximated optimal control problem. Based on the system dynamics, the problem is further reformulated as a quadratic programming problem, which is solved by a projection neural network. Theoretical analysis on the closed-loop system synthesized by the controlled system and the projection neural network is conducted, which reveals that, under certain conditions, the closed-loop system possesses exponential stability and the original performance index converges to zero as time tends to infinity. In addition, two illustrative examples, which are based on a flexible joint manipulator and an underactuacted ship, are provided to validate the theoretical results and demonstrate the efficacy and superiority of the proposed control scheme.
Regulation of actin-based apical structures on epithelial cells

ABSTRACT Cells of transporting epithelia are characterized by the presence of abundant F-actin-based microvilli on their apical surfaces. Likewise, auditory hair cells have highly reproducible rows of apical stereocilia (giant microvilli) that convert mechanical sound into an electrical signal. Analysis of mutations in deaf patients has highlighted the critical components of tip links between stereocilia, and related structures that contribute to the organization of microvilli on epithelial cells have been found. Ezrin/radixin/moesin (ERM) proteins, which are activated by phosphorylation, provide a critical link between the plasma membrane and underlying actin cytoskeleton in surface structures. Here, we outline recent insights into how microvilli and stereocilia are built, and the roles of tip links. Furthermore, we highlight how ezrin is locally regulated by phosphorylation, and that this is necessary to maintain polarity. Localized phosphorylation is achieved through an intricate coincidence detection mechanism that requires the membrane lipid phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2] and the apically localized ezrin kinase, lymphocyte-oriented kinase (LOK, also known as STK10) or Ste20-like kinase (SLK). We also discuss how ezrin-binding scaffolding proteins regulate microvilli and how, despite these significant advances, it remains to be discovered how the cell polarity program ultimately interfaces with these processes. Summary: Apical surfaces of epithelial cells are characterized by the presence of abundant F-actin-based microvilli. We outline recent insights into regulation and maintenance of microvilli and stereocilia.
Rising Temperatures, Molting Phenology, and Epizootic Shell Disease in the American Lobster

Phenological mismatch—maladaptive changes in phenology resulting from altered timing of environmental cues—is an increasing concern in many ecological systems, yet its effects on disease are poorly characterized. American lobster (Homarus americanus) is declining at its southern geographic limit. Rising seawater temperatures are associated with seasonal outbreaks of epizootic shell disease (ESD), which peaks in prevalence in the fall. We used a 34-year mark-recapture data set to investigate relationships between temperature, molting phenology, and ESD in Long Island Sound, where temperatures are increasing at 0.4°C per decade. Our analyses support the hypothesis that phenological mismatch is linked to the epidemiology of ESD. Warming spring temperatures are correlated with earlier spring molting. Lobsters lose diseased cuticle by molting, and early molting increases the intermolt period in the summer, when disease prevalence is increasing to a fall peak. In juvenile and adult male lobsters, September ESD prevalence was correlated with early molting, while October ESD prevalence was correlated with summer seawater temperature. This suggests that temperature-induced molting phenology affects the timing of the onset of ESD, but later in the summer this signal is swamped by the stronger signal of summer temperatures, which we hypothesize are associated with an increased rate of new infections. October ESD prevalence was ∼80% in years with hot summers and ∼30% in years with cooler summers. Yearly survival of diseased lobsters is <50% that of healthy lobsters. Thus, population impacts of ESD are expected to increase with increasing seawater temperatures.
Adapted Immune Responses of Myeloid-Derived Cells in Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is considered to be one of the most frequent chronic liver diseases worldwide and is associated with an increased risk of developing liver cirrhosis and hepatocellular carcinoma. Hepatic macrophages, mainly comprising monocyte derived macrophages and tissue resident Kupffer cells, are characterized by a high diversity and plasticity and act as key regulators during NAFLD progression, in conjunction with other infiltrating myeloid cells like neutrophils or dendritic cells. The activation and polarization of myeloid immune cells is influenced by dietary components, inflammatory signals like danger-associated molecular patterns (DAMPs) or cytokines as well as gut-derived inflammatory factors such as pathogen-associated molecular patterns (PAMPs). The functionality of myeloid leukocytes in the liver is directly linked to their inflammatory polarization, which is shaped by local and systemic inflammatory mediators such as cytokines, chemokines, PAMPs, and DAMPs. These environmental signals provoke intracellular adaptations in myeloid cells, including inflammasome and transcription factor activation, inflammatory signaling pathways, or switches in cellular metabolism. Dietary changes and obesity also promote a dysbalance in intestinal microbiota, which can facilitate intestinal permeability and bacterial translocation. The aim of this review is to highlight recent findings on the activating pathways of innate immune cells during the progression of NAFLD, dissecting local hepatic and systemic signals, dietary and metabolic factors as well as pathways of the gut-liver axis. Understanding the mechanism by which plasticity of myeloid-derived leukocytes is related to metabolic changes and NAFLD progression may provide options for new therapeutic approaches.
ORM : Un Moteur de Persistance Objet Relationnelle

Resume: Le travail presente dans ce papier, s’inscrit dans le cadre d’un projet objet-relationnel qui a pour but la gestion de la persistance des objets a l’aide des SGBD relationnels ; il vise a exploiter l’existant en matiere de bases de donnees, et a reduire le cout d’integration de l’approche objet dans les systemes actuels. Il consiste a faire correspondre les objets du modele objet aux tables du modele relationnel en utilisant des techniques de representation sous forme relationnelle des objets. Ensuite, une mise en œuvre d’une couche logicielle portable sur toutes les plateformes situee au dessus d’une base de donnees relationnelle realisant cette correspondance automatique a ete realisee. Mots cles: Persistance, Objet-Relationnel, Reutilisation, Portabilite.
Path Planning of Multiagent Constrained Formation through Deep Reinforcement Learning

A parallel deep Q-network (DQN) algorithm is presented for solving multiagent constrained formation path planning, where reaching destination, avoiding obstacles, and maintaining formation are simultaneously considered as independent or interactive tasks. Parallel Q-networks are utilized for each agent to sense different feature information and learn independent behavior policy. Comprehensive reward function is designed in consideration of respective requirements and interaction constraints to correctly guide the training. In order to demonstrate the effectiveness of the algorithm, we build an end-to-end model by designing a pixel game. Both training and testing are carried out in the game with double dueling DQN and the results show that the parallel deep Q-network path planner eventually complete the three tasks very well.
Inferring Design Constraints From Game Ruleset Analysis

Designing game rulesets is an important part of automated game design, and often serves as a foundation for all other parts of the game, from levels to visuals. Popular ways of understanding game rulesets include using AI agents to play the game, which can be unreliable and computationally expensive, or restricting the design space to a set of known good game concepts, which can limit innovation and creativity. In this paper we detail how ANGELINA, an automated game designer, uses an abductive analysis of game rulesets to rapidly cull its design space. We show how abduction can be used to provide an understanding of possible paths through a ruleset, reduce unplayable or undesirable rulesets without testing, and can also help discover dynamic heuristics for a game that can guide subsequent tasks like level design.
Large-Scale Linear NPSVM via One Permutation Hashing

Nonparallel support vector machine (NPSVM) is a novel nonparallel classifier for binary classification with large amounts of theoretical and practical advantages. How to train NPSVM efficiently on data with huge dimensions has not been studied. Recently, a variety of minwise hashing algorithms such as b-bit minwise hashing, connected bit minwise hashing and f-fractional bit minwise hashing are effectively applied to obtain a compact representation of the original data. However, they still have serious defects that the generation of k random permutations is time-consuming and the processing of the original dataset damages data structure. Fortunately, a simple and effective solution called one permutation hashing appears to avoid the disadvantages of the expensive preprocessing cost and the destruction of the original dataset. In this paper, we combine one permutation hashing scheme with linear NPSVM to speed up the training and testing phases for classification on large-scale and high dimensional datasets. Both theoretical analyses and experiments demonstrate that our algorithm achieves massive advantages in accuracy, efficiency and energy-consumption.
Robust data-driven discovery of governing physical laws with error bars

Discovering governing physical laws from noisy data is a grand challenge in many science and engineering research areas. We present a new approach to data-driven discovery of ordinary differential equations (ODEs) and partial differential equations (PDEs), in explicit or implicit form. We demonstrate our approach on a wide range of problems, including shallow water equations and Navier–Stokes equations. The key idea is to select candidate terms for the underlying equations using dimensional analysis, and to approximate the weights of the terms with error bars using our threshold sparse Bayesian regression. This new algorithm employs Bayesian inference to tune the hyperparameters automatically. Our approach is effective, robust and able to quantify uncertainties by providing an error bar for each discovered candidate equation. The effectiveness of our algorithm is demonstrated through a collection of classical ODEs and PDEs. Numerical experiments demonstrate the robustness of our algorithm with respect to noisy data and its ability to discover various candidate equations with error bars that represent the quantified uncertainties. Detailed comparisons with the sequential threshold least-squares algorithm and the lasso algorithm are studied from noisy time-series measurements and indicate that the proposed method provides more robust and accurate results. In addition, the data-driven prediction of dynamics with error bars using discovered governing physical laws is more accurate and robust than classical polynomial regressions.
Reconciling Requirements and Continuous Integration in an Agile Context

The RE'18 theme is: "Crossing Boundaries and Increasing Impact". In this context we propose a tutorial on Requirements in an Agile context that aims at exploring the boundaries between requirements, specifications, stories, scenarios and tests. It follows the recent work started in the RE community about agility, from a practical point of view.