Miracle-dz/UniFAR-FTUs huggingface dataset:
TPF Client TPF Query Engine ( Facade ) QA Executor Answer Ranking Model Runner Candidate Selection Text Search Engine Model Query

Structured queries expressed in languages (such as SQL, SPARQL, or XQuery) offer a convenient and explicit way for users to express their information needs for a number of tasks. In this work, we present an approach to answer these directly over text data without storing results in a database. We specifically look at the case of knowledge bases where queries are over entities and the relations between them. Our approach combines distributed query answering (e.g. Triple Pattern Fragments) with models built for extractive question answering. Importantly, by applying distributed querying answering we are able to simplify the model learning problem. We train models for a large portion (572) of the relations within Wikidata and achieve an average 0.70 F1 measure across all models. We also present a systematic method to construct the necessary training data for this task from knowledge graphs and describe a prototype implementation.
Daemons of Hadoop: An Overview

- The term“Hadoop” is basically associated with new topic which is very much in trend i.e Big data. Big Data, as the name indicates itself that the data is of huge amount. So it depicts the trendy technologies to store and to manage large datasets which can be Structured, Un-Structured or Semi-Struc-tured. Traditional technologies or databases were not able to handle this new type of data. Due to this inability of traditional databases, there was a need for some new technique i.e Hadoop. Hadoop is the foremost platform for handling the large volume of data that people are using now a days. Today the size of data ishugeholding petabytes, terabytes or somewhat larger than this size. Hadoop is based on work done by Google in the late 1990s/early 2000s. This paper will discuss about the daemons of key components of Hadoop: HDFS & MapReduce and how both these components are used to store the Big Data and also to process the Big Data respectively.
Solution Phosphorus-31 Nuclear Magnetic Resonance Spectroscopy of Soils from 2005 to 2013: A Review of Sample Preparation and Experimental Parameters

Phosphorus nuclear magnetic resonance (³¹P NMR) spectroscopy is an important tool for the study of soil P and has significantly advanced our knowledge of soil P forms, particularly organic P; however, it must be used correctly to provide meaningful results. This review covers the ³¹P NMR studies of soil published from 2005 to 2013. The first part discusses preparing samples for ³¹P NMR, including extractants, pre- and post-extraction treatments, the physical state of the soil sample at the time of extraction, extraction length, the soil/extractant ratio, P recovery, P in residues, methods to concentrate extracts, redissolving samples for ³¹P NMR experiments, the use of the internal standard methylene diphosphonic acid, and the potential for degradation with any of these steps. The second part of this review focuses on NMR experiment parameters, including delay times, proton decoupling, and experiment length. Potential concerns in these areas are noted, and suggestions are given for procedures to optimize the information obtained from a ³¹P NMR experiment.
Direct comparisons of compressible magnetohydrodynamics and reduced magnetohydrodynamics turbulence

Direct numerical simulations of low Mach number compressible three-dimensional magnetohydrodynamic (CMHD3D) turbulence in the presence of a strong mean magnetic field are compared with simulations of reduced magnetohydrodynamics (RMHD). Periodic boundary conditions in the three spatial coordinates are considered. Different sets of initial conditions are chosen to explore the applicability of RMHD and to study how close the solution remains to the full compressible MHD solution as both freely evolve in time. In a first set, the initial state is prepared to satisfy the conditions assumed in the derivation of RMHD, namely, a strong mean magnetic field and plane-polarized fluctuations, varying weakly along the mean magnetic field. In those circumstances, simulations show that RMHD and CMHD3D evolve almost indistinguishably from one another. When some of the conditions are relaxed the agreement worsens but RMHD remains fairly close to CMHD3D, especially when the mean magnetic field is large enough. Moreover, t.
ENDOGENOUS MARKET STRUCTURES AND THE BUSINESS CYCLE 1

We characterize endogenous market structures under Bertrand and Cournot competition in a DSGE model. Short run markups vary countercyclically because of the impact of entry on competition. Long run markups are decreasing in the discount factor and in productivity, and increasing in the exit rate and in the entry costs. Dynamic ine¢ ciency can emerge due to excessive entry under Cournot competition. Positive temporary shocks attract entry, which strengthens competition so as to temporary reduce the markups and increase real wages: this competition e¤ect creates an intertemporal substitution e¤ect which boosts consumption and employment. Endogenous market structures improve the ability of a exible prices model in matching impulse response functions and second moments for US data. JEL classi cation: L11, E32.
Title Equalizing multihop OFDM relay channel under unknownchannel orders and doppler frequencies

In this paper, equalization of multihop relaying orthogonal frequency division multiplexing (OFDM) signal is investigated under time-varying channel with unknown noise powers, channel orders and Doppler frequencies. An iterative algorithm is developed under variational expectation maximization (EM) framework. The proposed algorithm iteratively estimates the channel, learns the channel and noise statistical information, and recovers the unknown data, using only limited number of pilot subcarrier in one OFDM symbol. Simulation results show that, without any statistical information, the performance of the proposed algorithm is very close to that of the optimal channel estimation and data detection algorithm, which requires specific information on system structure, channel tap positions, channel lengths, Doppler shifts as well as noise powers.
A note on hyperparameters in black-box adversarial examples

Since Biggio et al. (2013) and Szegedy et al. (2013) first drew attention to adversarial examples, there has been a flood of research into defending and attacking machine learning models. However, almost all proposed attacks assume white-box access to a model. In other words, the attacker is assumed to have perfect knowledge of the models weights and architecture. With this insider knowledge, a white-box attack can leverage gradient information to craft adversarial examples. Black-box attacks assume no knowledge of the model weights or architecture. These attacks craft adversarial examples using information only contained in the logits or hard classification label. Here, we assume the attacker can use the logits in order to find an adversarial example. Empirically, we show that 2-sided stochastic gradient estimation techniques are not sensitive to scaling parameters, and can be used to mount powerful black-box attacks requiring relatively few model queries.
Information Theoretic Limits For Standard and One-Bit Compressed Sensing with Graph-Structured Sparsity

In this paper, we analyze the information theoretic lower bound on the necessary number of samples needed for recovering a sparse signal under different compressed sensing settings. We focus on the weighted graph model, a model-based framework proposed by [1], for standard compressed sensing as well as for one-bit compressed sensing. We study both the noisy and noiseless regimes. Our analysis is general in the sense that it applies to any algorithm used to recover the signal. We carefully construct restricted ensembles for different settings and then apply Fano’s inequality to establish the lower bound on the necessary number of samples. Furthermore, we show that our bound is tight for one-bit compressed sensing, while for standard compressed sensing, our bound is tight up to a logarithmic factor of the number of non-zero entries in the signal. A full version of this paper is accessible at: https://www.cs. purdue.edu/homes/abarik/abarik_cs_icassp_full. pdf
Information Complexity of Treasure Hunt in Geometric Terrains

Treasure hunt is the task of finding an inert target by a mobile agent in an unknown environment. We consider treasure hunt in geometric terrains with obstacles. Both the terrain and the obstacles are modeled as polygons and both the agent and the treasure are modeled as points. The agent navigates in the terrain, avoiding obstacles, and finds the treasure when there is a segment of length at most 1 between them, unobstructed by the boundary of the terrain or by the obstacles. The cost of finding the treasure is the length of the trajectory of the agent. We investigate the amount of information that the agent needs a priori in order to find the treasure at cost $O(L)$, where $L$ is the length of the shortest path in the terrain from the initial position of the agent to the treasure, avoiding obstacles. Following the paradigm of algorithms with advice, this information is given to the agent in advance as a binary string, by an oracle cooperating with the agent and knowing the whole environment: the terrain, the position of the treasure and the initial position of the agent. Information complexity of treasure hunt is the minimum length of the advice string (up to multiplicative constants) that enables the agent to find the treasure at cost $O(L)$. We first consider treasure hunt in regular terrains which are defined as convex polygons with convex $c$-fat obstacles, for some constant $c>1$. A polygon is $c$-fat if the ratio of the radius of the smallest disc containing it to the radius of the largest disc contained in it is at most $c$. For the class of regular terrains, we establish the exact information complexity of treasure hunt. We then show that information complexity of treasure hunt for the class of arbitrary terrains (even for non-convex polygons without obstacles, and even for those with only horizontal or vertical sides) is exponentially larger than for regular terrains.
Acute Tachycardia Increases Aortic Distensibility, but Reduces Total Arterial Compliance Up to a Moderate Heart Rate

Background: The differential effects of rapid cardiac pacing on small and large vessels have not been well-established. The objective of this study was to investigate the effect of pacing-induced acute tachycardia on hemodynamics and arterial stiffness. Methods: The pressure and flow waves in ascending aorta and femoral artery of six domestic swine were recorded simultaneously at baseline and heart rates (HR) of 135 and 155 beats per minutes (bpm) and analyzed by the models of Windkessel and Womersley types. Accordingly, the flow waves were simultaneously measured at carotid and femoral arteries to quantify aortic pulse wave velocity (PWV). The arterial distensibility was identified in small branches of coronary, carotid and femoral arteries with diameters of 300–600 μm by ex vivo experiments. Results: The rapid pacing in HR up to 135 bpm reduced the total arterial compliance, stroke volume, systemic pulse pressure, and central systolic pressure by 36 ± 17, 38 ± 26, 29 ± 16, and 23 ± 12%, respectively, despite no statistical difference of mean aortic pressure, cardiac output, peripheral resistance, and vascular flow patterns. The pacing also resulted in a decrease of distensibility of small muscular arteries, but an increase of aortic distensibility. Pacing from 135 to 155 bpm had negligible effects on systemic and local hemodynamics and arterial stiffness. Conclusions: There is an acute mismatch in the response of aorta and small arteries to pacing from basal HR to 135 bpm, which may have important pathological implications under chronic tachycardia conditions.
Military-Related Exposures, Social Determinants of Health, and Dysbiosis: The United States-Veteran Microbiome Project (US-VMP)

Significant effort has been put forth to increase understanding regarding the role of the human microbiome in health- and disease-related processes. In turn, the United States (US) Veteran Microbiome Project (US-VMP) was conceptualized as a means by which to serially collect microbiome and health-related data from those seeking care within the Veterans Health Administration (VHA). In this manuscript, exposures related to military experiences, as well as conditions and health-related factors among patients seen in VHA clinical settings are discussed in relation to common psychological and physical outcomes. Upon enrollment in the study, Veterans complete psychometrically sound (i.e., reliable and valid) measures regarding their past and current medical history. Participants also provide skin, oral, and gut microbiome samples, and permission to track their health status via the VHA electronic medical record. To date, data collection efforts have been cross-diagnostic. Within this manuscript, we describe current data collection practices and procedures, as well as highlight demographic, military, and psychiatric characteristics of the first 188 Veterans enrolled in the study. Based on these findings, we assert that this cohort is unique as compared to those enrolled in recent large-scale studies of the microbiome. To increase understanding regarding disease and health among diverse cohorts, efforts such as the US-VMP are vital. Ongoing barriers and facilitators to data collection are discussed, as well as future research directions, with an emphasis on the importance of shifting current thinking regarding the microbiome from a focus on normalcy and dysbiosis to health promotion and disease prevention.
nn-dependability-kit: Engineering Neural Networks for Safety-Critical Systems

nn-dependability-kit is an open-source toolbox to support safety engineering of neural networks. The key functionality of nn-dependability-kit includes (a) novel dependability metrics for indicating sufficient elimination of uncertainties in the product life cycle, (b) formal reasoning engine for ensuring that the generalization does not lead to undesired behaviors, and (c) runtime monitoring for reasoning whether a decision of a neural network in operation time is supported by prior similarities in the training data.
BioLQM: A Java Toolkit for the Manipulation and Conversion of Logical Qualitative Models of Biological Networks

Here we introduce bioLQM, a new Java software toolkit for the conversion, modification, and analysis of Logical Qualitative Models of biological regulatory networks. BioLQM provides core modeling operations as building blocks for the development of integrated modeling software, or for the assembly of heterogeneous analysis workflows involving several complementary tools. Based on the definition of multi-valued logical models, bioLQM implements import and export facilities, notably for the recent SBML qual exchange format, as well as for formats used by several popular tools, facilitating the design of workflows combining these tools. Model modifications enable the definition of various perturbations, as well as model reduction, easing the analysis of large models. Another modification enables the study of multi-valued models with tools limited to the Boolean case. Finally, bioLQM provides a framework for the development of novel analysis tools. The current version implements various updating modes for model simulation (notably synchronous, asynchronous, and random asynchronous), as well as some static analysis features for the identification of attractors. The bioLQM software can be integrated into analysis workflows through command line and scripting interfaces. As a Java library, it further provides core data structures to the GINsim and EpiLog interactive tools, which supply graphical interfaces and additional analysis methods for cellular and multi-cellular qualitative models.
Cognitive Control and Flexibility in the Context of Stress and Depressive Symptoms: The Cognitive Control and Flexibility Questionnaire

Cognitive control and (cognitive) flexibility play an important role in an individual’s ability to adapt to continuously changing environments. In addition to facilitating goal-directed behaviors, cognitive control and flexibility have been implicated in emotion regulation, and disturbances of these abilities are present in mood and anxiety disorders. In the context of stressful experiences, the reported studies examined processes related to cognitive control and flexibility, emotional regulation and depressive symptoms. To this end, a brief (18-item) self-report measure – the Cognitive Control and Flexibility Questionnaire (CCFQ) – was developed. This questionnaire measures an individual’s perceived ability to exert control over intrusive, unwanted (negative) thoughts and emotions, and their ability to flexibly cope with a stressful situation. In Study 1, the CCFQ was assessed among both university students (N = 300) and a community sample (N = 302). Preliminary analyses suggested a stable and reliable two-factor structure, that of cognitive control over emotion, and appraisal and coping flexibility. Scores on the CCFQ were strongly associated with greater depressive symptoms, even after controlling for other measures that had been taken to reflect cognitive control and (in)flexibility (e.g., the Ruminative Response Scale; Perseverative Thinking Questionnaire). In Study 2 (N = 368), lower scores on the CCFQ were related to more negative stressor appraisals (i.e., greater perceived threat and uncontrollability) of a personally meaningful stressful event. Perceptions of threat and uncontrollability, in turn, partially accounted for the association between CCFQ subscale scores and depressive symptoms. The relation between lower CCFQ scores and heightened depressive symptoms was also partially accounted for by less frequent engagement in problem-focused coping and more use of emotion-focused methods. In Study 3 (N = 47 females), lower scores on the cognitive control over emotion component of the CCFQ predicted elevated negative affect and an exacerbated cortisol response following an acute psychosocial stressor (Trier Social Stress Test). The present research points to the CCFQ as a useful self-report tool to identify ways through which cognitive control and flexibility might be manifested in stressful situations, and how reductions in flexibility might be accompanied by elevated symptoms of depression.
Image Pre-processing Using OpenCV Library on MORPH-II Face Database

This paper outlines the steps taken toward pre-processing the 55,134 images of the MORPH-II non-commercial dataset. Following the introduction, section two begins with an overview of each step in the pre-processing pipeline. Section three expands upon each stage of the process and includes details on all calculations made, by providing the OpenCV functionality paired with each step. The last portion of this paper discusses the potential improvements to this pre-processing pipeline that became apparent in retrospect.
Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps

In recent years, artificial intelligence (AI) decision-making and autonomous systems became an integrated part of the economy, industry, and society. The evolving economy of the human-AI ecosystem raising concerns regarding the risks and values inherited in AI systems. This paper investigates the dynamics of creation and exchange of values and points out gaps in perception of cost-value, knowledge, space and time dimensions. It shows aspects of value bias in human perception of achievements and costs that encoded in AI systems. It also proposes rethinking hard goals definitions and cost-optimal problem-solving principles in the lens of effectiveness and efficiency in the development of trusted machines. The paper suggests a value-driven with cost awareness strategy and principles for problem-solving and planning of effective research progress to address real-world problems that involve diverse forms of achievements, investments, and survival scenarios.
Subspace Clustering through Sub-Clusters

The problem of dimension reduction is of increasing importance in modern data analysis. In this paper, we consider modeling the collection of points in a high dimensional space as a union of low dimensional subspaces. In particular we propose a highly scalable sampling based algorithm that clusters the entire data via first spectral clustering of a small random sample followed by classifying or labeling the remaining out of sample points. The key idea is that this random subset borrows information across the entire data set and that the problem of clustering points can be replaced with the more efficient and robust problem of "clustering sub-clusters". We provide theoretical guarantees for our procedure. The numerical results indicate we outperform other state-of-the-art subspace clustering algorithms with respect to accuracy and speed.
Subtask Gated Networks for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.
Importance of the Window Function Choice for the Predictive Modelling of Memristors

Window functions are widely employed in memristor models to restrict the changes of the internal state variables to specified intervals. Here, we show that the actual choice of window function is of significant importance for the predictive modelling of memristors. Using a recently formulated theory of memristor attractors, we demonstrate that whether stable fixed points exist depends on the type of window function used in the model. Our main findings are formulated in terms of two memristor attractor theorems, which apply to broad classes of memristor models. As an example of our findings, we predict the existence of stable fixed points in Biolek window function memristors and their absence in memristors described by the Joglekar window function, when such memristors are driven by periodic alternating polarity pulses. It is anticipated that the results of this brief will contribute toward the development of more sophisticated models of memristive devices and systems.