Miracle-dz/UniFAR-FTUs huggingface dataset:
The Integer Approximation of Undirected Graphical Models

Machine learning on resource constrained ubiquitous devices suffers from high energy consumption and slow execution time. In this paper, it is investigated how to modify machine learning algorithms in order to reduce the number of consumed clock cyclesnot by reducing the asymptotic complexity, but by assuming a weaker execution platform. In particular, an integer approximation to the class of undirected graphical models is proposed. Algorithms for inference, maximum-a-posteriori prediction and parameter estimation are presented and approximation error is discussed. In numerical evaluations on synthetic data, the response of the model to several influential properties of the data is investigated. The results on the synthetic data are confirmed with a natural language processing task on an open data set. In addition, the runtime on low-end hardware is regarded. The overall speedup of the new algorithms is at least 2× while overall loss in accuracy is rather small. This allows running probabilistic methods on very small devices, even if they do not contain a processor that is capable of executing floating point arithmetic at all.
Exploring the Impact of Stimulus Events on Intern Career Intentions and Well-Being

Internships are crucial in many sport management students’ path to the sport industry. The purpose of this sequential mixed-methods study was to understand the nature of stimulus events occurring within sport management internships, and the impact of stimulus events on two main outcomes: student career decision making and well-being. Pre-post internship surveys (n=23) and follow-up interviews (n=21) were used to identify stimulus events, if intern expectations were met, and if career intentions or well-being changed (i.e., increased or decreased). Stimulus events occurred related to many areas of the internship (e.g., tasks, supervisor, social interactions, inclusivity, and the environment) and had a range of impacts on the outcomes of study. Importantly, contributing to the Unfolding Model of Employee Turnover, participants outlined that these events influenced well-being not only as a result of the influence on career intentions, but also on its own. At other times, the impact on well-being was discussed in isolation or in the opposite direction of the impact on career intentions. The findings here have important theoretical and practical implications for both sport management educators and organizational internship supervisors in sport.
On the Whittle Index for Restless Multiarmed Hidden Markov Bandits

We consider a restless multiarmed bandit in which each arm can be in one of two states. When an arm is sampled, the state of the arm is not available to the sampler. Instead, a binary signal with a known randomness that depends on the state of the arm is available. No signal is available if the arm is not sampled. An arm-dependent reward is accrued from each sampling. In each time step, each arm changes state according to known transition probabilities, which, in turn, depend on whether the arm is sampled or not sampled. Since the state of the arm is never visible and has to be inferred from the current belief and a possible binary signal, we call this the hidden Markov bandit. Our interest is in a policy to select the arm(s) in each time step to maximize the infinite horizon discounted reward. Specifically, we seek the use of the Whittle index in selecting the arms. We first analyze the single-armed bandit and show that, in general, it admits an approximate threshold-type optimal policy when there is a positive reward for the “no-sample” action. We also identify several special cases for which the threshold policy is indeed the optimal policy. Next, we show that such a single-armed bandit also satisfies an approximate-indexability property. For the case when the single-armed bandit admits a threshold-type optimal policy, we perform the calculation of the Whittle index for each arm. Numerical examples illustrate the analytical results.
Improving Multi-Step Prediction of Learned Time Series Models

Most typical statistical and machine learning approaches to time series modeling optimize a single-step prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, breaking the train-test i.i. d assumption common in supervised learning. We present an approach that reuses training data to make a no-regret learner robust to errors made during multi-step prediction. Our insight is to formulate the problem as imitation learning; the training data serves as a "demonstrator" by providing corrections for the errors made during multi-step prediction. By this reduction of multi-step time series prediction to imitation learning, we establish theoretically a strong performance guarantee on the relation between training error and the multi-step prediction error. We present experimental results of our method, DaD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction.
An efficient and accurate distance based algorithm to reconstruct tandem duplication trees

UNLABELLED The problem of reconstructing the duplication tree of a set of tandemly repeated sequences which are supposed to have arisen through unequal recombination, was first introduced by Fitch (1977, Genetics, 86, 93-104), and has recently received a lot of attention. In this paper, we describe DTSCORE, a fast distance based algorithm to reconstruct tandem duplication trees, which is statistically consistent. As a cousin of the ADDTREE algorithm (Sattath and Tversky, 1977, Psychometrika, 42, 319-345), the raw DTSCORE has a time complexity in O(n(5)), where n is the number of observed repeated sequences. Through a series of algorithmic refinements, we improve its complexity to O(n(4)) in the worst case, but stress that the refined DTSCORE algorithm should perform faster with real data. We assess the topological accuracy of DTSCORE using simulated data sets, and compare it to existing reconstruction methods. The results clearly show that DTSCORE is more accurate than all the other methods we studied. Finally, we report the results of DTSCORE on a real dataset. SUPPLEMENTARY INFORMATION http://www.lirmm. fr/w3ifa/MAAS/
A Parallel Implementation of the Network Identification by Multiple Regression (NIR) Algorithm to Reverse-Engineer Regulatory Gene Networks

The reverse engineering of gene regulatory networks using gene expression profile data has become crucial to gain novel biological knowledge. Large amounts of data that need to be analyzed are currently being produced due to advances in microarray technologies. Using current reverse engineering algorithms to analyze large data sets can be very computational-intensive. These emerging computational requirements can be met using parallel computing techniques. It has been shown that the Network Identification by multiple Regression (NIR) algorithm performs better than the other ready-to-use reverse engineering software. However it cannot be used with large networks with thousands of nodes - as is the case in biological networks - due to the high time and space complexity. In this work we overcome this limitation by designing and developing a parallel version of the NIR algorithm. The new implementation of the algorithm reaches a very good accuracy even for large gene networks, improving our understanding of the gene regulatory networks that is crucial for a wide range of biomedical applications.
A comparison of ANFIS, MLP and SVM in identification of chemical processes

This paper presents a comparison of Adaptive Neuro Fuzzy Inference Systems (ANFIS),Multilayer Perceptron (MLP) and Support VectorMachines (SVMs) in identification of a chemical process displaying a rich set of dynamical responses under different operating conditions. The methods considered are selected carefully as they are the foremost approaches exploiting the linguistic representations in ANFIS, connectionist representations in MLP and machine learning based on structural risk minimization in SVM. The comparison metrics are the computational complexity measured by the propagation delay, realization performance and design simplicity. It is seen that SVM algorithm performs better in terms of providing an accurate fit to the desired dynamics but a very close performance result can also be obtained with ANFIS with significantly lower computational cost. Performance with MLP is comparably lower that the other two algorithms yet MLP structure has the lowest computational complexity.
Social-Emotional Assessment, Performance, and Standards

Summary:In the push to boost young people's social and emotional learning (SEL), assessment has lagged behind policy and practice. We have few usable, feasible, and scalable tools to assess children's SEL. And without good assessments, teachers, administrators, parents, and policymakers can't get the data they need to make informed decisions about SEL.Some existing SEL assessments, writes Clark McKown, are appropriate for some purposes, such as keeping teachers abreast of their students' progress or evaluating SEL interventions. But too few high-quality SEL assessments are able to serve a growing range of purposes—from formative assessment to accountability, and from prekindergarten through high school.McKown recommends proceeding along two paths. First, he writes, educators should become familiar with existing SEL assessments so that they can learn their appropriate uses and limits in a low-stakes context. At the same, we need to invest money and talent to create assessment systems that can be used to meet important assessment goals at all grade levels.McKown walks us through definitions of SEL, identifying three broad areas of SEL skills—thinking, behavior, and self-control. Each area encompasses skills that are associated with important life and academic outcomes, that are feasible to assess, and that can be influenced by children's experiences. Such meaningful, measurable, and malleable skills, McKown argues, should form the basis of SEL assessments.The next generation of SEL assessments should follow six principles, he concludes. First, assessments should meet the highest ethical and scientific standards. Second, developers should design SEL assessment systems specifically for educational use. Third, assessments should measure dimensions of SEL that span the three categories of thinking, behavioral, and self-control skills. Fourth, assessment methods should be matched to what's being measured. Fifth, assessments should be developmentally appropriate—in other words, children of different ages will need different sorts of assessments. Last, to discourage inappropriate uses, developers should clearly specify the intended purpose of any SEL assessment system, beginning from the design stage.
Toward an Integrative Model for Consumer Behavior regarding Mobile Commerce Adoption

With the substantial growth of mobile phone users, mobile commerce (m-commerce) has been predicated to become a powerful tool in electronic commerce. To achieve success in m-commerce, companies must first understand consumer behaviors toward m-commerce adoption. The objective of this study is to provide a comprehensive review of the information system (IS) literature related to consumer perspectives of m-commerce, integrate research findings across studies in order to enhance the knowledge of consumer behavior regarding m-commerce adoption, and provide a clear direction for further discussion. An integrative framework based on finding according to a coherent and comprehensive review related studies in m-commerce field was proposed. This integrative framework proposed system quality, information quality, service quality, and bizware characteristics as critical factors that influence consumer behaviors toward adopting m-commerce through the perceptions of easy of use, usefulness, risk, and convenience.
Sveriges Riksbank Working Paper Series The Empirical Relevance of Simple Forward-and Backward-looking Models : A View from a Dynamic General Equilibrium Model

Recent research have provided evidence that backward-looking models fit the data well while purely forward-looking models seem to be inconsistent with data. Consequently, many recent papers in the monetary policy rule literature have used “hybrid” models, which contain both backwardand forward-looking components. In this paper, I demonstrate that a dynamic general equilibriummodel with flexible prices and forward-looking properties cannot account for the empirical findings, i.e. that backward-looking behavior seems more important than forward-looking behavior, and that backward-looking models fit the data better than purely forward-looking models. The results also show that the equilibrium model cannot replicate the estimated high weight on backward-looking behavior on US data for the hybrid model.
Information Theory Tutorial Communication over Channels with memory

A general capacity formula C = sup X I(X; Y), which is correct for arbitrary single-user channels without feedback, is introduced in this tutorial. This new capacity formula is obtained by using a general channel model without any assumptions of the channel, introducing the notion of inf/sup-information/entropy rates, and finding a new tight converse bound on the error probability. In Section 1 we will show the error of the conventional channel capacity formula. Then in section2, we will give some definitions used in the proofs of the capacity formula. Section 3 is devoted to the direct coding theorem. Section 4 will focus on the new converse bound and the general capacity formula. We will see some useful results of the new converse bound in section 5. In section 6, we will see how feedback can increase the channel capacity when the channel has memory.
Development of Differentiated Performance Assessment Tasks for Middle School Classrooms.

Educational reform efforts since the 1980s have all emphasized accountability in terms of student achievement and learning outcomes rather than process. As of this year, 49 out of 50 states (excluding Iowa) have mandated the implementation of statewide testing. As a result, high-stakes testing has become the focal point for evaluating student learning, with nearly all of the evaluative efforts dominated by the use of traditional objective assessments. Much debate surrounds the effectiveness of using high-stakes tests as a tool for accountability purposes in terms of improved student achievement and performance. Some literature affirms that using tests for accountability purposes is one avenue for enhancing student performance. However, other literature indicates that the widespread use of statewide mandated tests negatively affects students, teachers, schools, and the quality of curriculum and instruction in the classroom. While the use of high-stakes testing has focused teacher planning on specified, agreed upon state-level objectives, exclusive use of traditional assessment, often in the form of multiple-choice tests, has been judged to be a negative in middle school classrooms. In response to these criticisms, some measurement experts advocate the use of authentic assessments for their potential for increased validity. The National Research Center on the Gifted and Talented (NRC/GT) at the University of Virginia undertook the development of differentiated authentic assessments for classroom use that embodied key concepts, principles, generalizations, and processes critical to understanding in the disciplines of English/language arts, mathematics, science, and social studies. In addition to the development of the assessments, a small-scale study was designed to investigate the psychometric attributes of the authentic assessments. The results of the study provide evidence that authentic assessments for classroom purposes can be developed to provide reliable and valid information about student learning. In addition, results suggest that the authentic assessment can provide an accurate assessment of students' success in achieving academic learning standards, with positive responses of both teachers and students to the authentic assessment experience.
An extensible digital ink segmentation and classification framework for natural notetaking

With the emergence of digital pen and paper technologies, we have witnessed an increasing number of enhanced paper-digital notetaking solutions. However, the natural notetaking process includes a variety of individual work practices that complicate the automatic processing of paper notes and require user intervention for the classification of digital ink data. We present an extensible digital ink processing framework that simplifies the classification of digital ink data in natural notetaking applications. Our solution deals with the manual as well as automatic ink data segmentation and classification based on Delaunay triangulation and a strongest link algorithm. We further highlight how our solution can be extended with new digital ink classifiers and describe a paper-digital reminder application that has been realised based on the presented digital ink processing framework.
A new genus and two new species of saccamminid foraminiferans (Protista, Rhizaria) from the deep Southern Ocean*

We describe two new species of spherical single-chambered ('saccamminid') foraminifera from the bathyal and abyssal Weddell Sea (Southern Ocean), collected in epibenthic sledge and Agassiz trawl samples obtained during the 2005 ANDEEP III campaign. Both are assigned to Leptammina gen. nov. The new genus is similar in overall test morphology to Saccammina Carpenter, 1869; it is distinguished mainly by its test wall, which is delicate, flexible and composed of fine mineral grains, rather than being rigid and coarsely agglutinated. In Leptammina grisea gen. et sp. nov., the test wall is relatively thick, grayish with a violet tinge and a dull surface; the cytoplasm is dark greenish. In Leptammina flavofusca gen. et sp. nov., the test is yellowish brown, with a very finely, almost transluscent agglutinated wall; the cytoplasm is pale yellowish. Both species have prominent circular apertures. Maximum likelihood phylogenetic analysis of SSU rRNA gene data showed that both species group together with an undescribed shallow-water Antarctic species (“silver saccamminid”) in a very strongly supported clade (100 %). Leptammina grisea gen. et sp. nov. is a relatively uncommon species (29 specimens from 3 stations), found at 1580–4822 m depth in the central and north–western Weddell Sea; Leptammina flavofusca gen. et sp. nov. is common (398 specimens from 4 stations) at depths of 3138–4795 m in the central Weddell Sea and off Kapp Norvegia. Both species are presently known only from ANDEEP III samples.
A Movement Control Strategy Based on Virtual Repulsion and Aggregation Algorithm in Multi-Tier Mobile Sensor Network

Wireless Sensor Network (WSN) is the hot research topic. In order to adapt to the future development direction of WSN, this paper presented a movement control strategy based on virtual repulsion in Multi-tier Mobile Sensor Network. The strategy first used a hierarchical node aggregation algorithm to divide all sensor nodes into many communication clusters in the monitoring area. It combines virtual repulsive force to distribute nodes uniformity in every cluster. Finally, using aggregation algorithm again based on the nodes uniform distribution to design fusion nodes' optimal movement route for data collection. The simulation results show that the strategy can ensure the efficiency of network data collection, reduce data loss rate, and nodes power consumption.
Analysis of Biological Networks: Network Modules Identication * 1 Functional Annotation -global Methods @bullet Functional Score: Amount of Ow That Entered the Protein's Reservoir throughout the Algorithm. * Based on a Previous Scribes

In this lecture we complete the discussion of Global Methods of Functional Annotation and present Net-larity and Community Structure in Networks) and Biclustering(SAMBA) and CoIP data analysis(CODEC). In the previous lecture we saw several local methods of nding functions. Similar to local methods, global methods are based on the assumption that proteins involved in similar functions are more likely to interact. However, global methods use dierent strategies for the prediction of protein function and some of them will be described here. 1.1 Functional ow The local methods that we saw in the previous lecture [15, 10] do not consider the global topology of the protein interaction graphs. Therefore, considering the 2 instances shown in Figure 1, all local methods will produce the same result for protein a in both instances. These methods ignore information on the number of independent paths between protein a and the annotated proteins, in addition, these methods treat all distances equally. The method described in Nabieva et al. [13] is a network ow, where every protein with known function is a source of ow to the other proteins, and the edges are weighted according to their reliability. The functional ow algorithm generalized the principle of guilt by association to groups of proteins that may or may not interact with each other physically. A general outline of this algorithm is as follows: • Each protein with known function is treated as a source of ow for that function. • For each function, ow spread is simulated by an iterative algorithm using discrete time steps. • The capacity of an edge is dened to be its weight and will represent its reliability. • The reservoir of a node is the amount of ow the node can pass to its neighbors. Source nodes have innite reservoir; for all others it is initially zero. • The algorithm is run for several iterations. • Each iteration a node pushes ow residing in its reservoir such that: Flow is proportional to edge capacities of the node. Capacity constraints are satised. Flow only spreads from more lled to less lled reservoirs.
Population coding in a neural net for trajectory formation

In this study we investigate the time evolution of the activity in a topographically ordered neural network with external input for two types of neurons: one network with binary-valued neurons with a stochastic behaviour and one with deterministic neurons with a continuous output. We demonstrate that for a particular range of lateral interaction strengths, changes in external input give rise to gradual changes in the position of clustered neural activity.The theoretical results are illustrated by computer simulations in which we have simulated a neural network model for trajectory planning for a multi-joint manipulator. The model gives a collision-free trajectory by combining the sensory information about the position of target and obstacles. The position of the manipulator is uniquely related to the clustered activity of the population of neurons, the population vector. The movement of the manipulator from any initial position to the target position is the result of the intrinsic dynamics of the network.
Channel assignment schemes for infrastructure-based 802.11 WLANs: A survey

Efficient channel assignment is crucial for successful deployment and operation of IEEE 802.11-based WLANs. In this article we present a survey on the state of the art channel assignment schemes in IEEE 802.11-based WLANs. After detailing out all the schemes, we provide a qualitative comparison among different schemes in terms of algorithm execution behaviors, complexity, and scalability. We then conclude the survey with several research issues open for further investigation.