Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Dynamic Programming on Graphics Processing Units via Adaptive Thread-Level Parallelism

Dynamic programming (DP) is an important computational method for solving a wide variety of discrete optimization problems such as scheduling, string editing, packaging, and inventory management. In general, DP is classified into four categories based on the characteristics of the optimization equation. Because applications that are classified in the same category of DP have similar program behavior, the research community has sought to propose general solutions for parallelizing each category of DP. However, most existing studies focus on running DP on CPU-based parallel systems rather than on accelerating DP algorithms on the graphics processing unit (GPU). This paper presents the GPU acceleration of an important category of DP problems called nonserial polyadic dynamic programming (NPDP). In NPDP applications, the degree of parallelism varies significantly in different stages of computation, making it difficult to fully utilize the compute power of hundreds of processing cores in a GPU. To address this challenge, we propose a methodology that can adaptively adjust the thread-level parallelism in mapping a NPDP problem onto the GPU, thus providing sufficient and steady degrees of parallelism across different compute stages. We realize our approach in a real-world NPDP application -- the optimal matrix parenthesization problem. Experimental results demonstrate our method can achieve a speedup of 13.40 over the previously published GPU algorithm.
Probing dark matter haloes with satellite kinematics

Using detailed mock galaxy redshift surveys (MGRSs) we investigate to what extent the kinematics of large samples of satellite galaxies extracted from flux-limited surveys can be used to constrain halo masses. Unlike previous studies, which focused only on satellites around relatively isolated host galaxies, we try to recover the average velocity dispersion of satellite galaxies in all haloes, as a function of the luminosity of the host galaxy. We show that previous host-satellite selection criteria (SC) yield relatively large fractions of interlopers and with a velocity distribution that, contrary to what has been assumed in the past, differs strongly from uniform. We show that with an iterative, adaptive selection criterion one can obtain large samples of hosts and satellites, with strongly reduced interloper fractions, that allow an accurate measurement of a sat (L host ) over 2.5 orders of magnitude in host luminosity. We use the conditional luminosity function (CLF) to make predictions and show that satellite weighting, which occurs naturally when stacking many host-satellite pairs to increase the signal-to-noise ratio, introduces a bias towards higher a s at (L host ) compared to the true, host-averaged mean. A further bias, in the same direction, is introduced when using flux-limited, rather than volume-limited, surveys. We apply our adaptive selection criterion to the Two Degree Field Galaxy Redshift Survey (2dFGRS) and obtain a sample of 12 569 satellite galaxies and 8132 host galaxies. We show that the kinematics of these satellite galaxies are in excellent agreement with the predictions based on the CLF, after taking account of the various biases. We thus conclude that there is independent dynamical evidence to support the mass-to-light ratios predicted by the CLF formalism.
Semantic-Based Web API Composition for Data Mashups

With the growing popularity of data mashups, the number of Web APIs has increased significantly. As a result, finding and composing the right APIs has become an increasingly complex task. Although several tools such as Yahoo's Pipes, IBM's Lotus Mashup, and Intel's Mashmaker have been developed to enable users to create data mashups without programming skills, there are several challenging issues when combining a large number of APIs into the data mashup. This paper proposes novel algorithms for the automatic discovery and composition of Web APIs. Our discovery algorithm adopts strategies that rapidly prune APIs that are guaranteed not to match the query. Our composition algorithm consists of constructing a composable similarity graph (CSG) and searching composition candidates. The CSG presents the semantic functional dependency between the inputs and the outputs of the Web APIs. Using this graph, we generate directed acyclic graphs (DAGs) that can produce the output satisfying the desired goal. We evaluate the algorithms on a real-world dataset from ProgrammableWeb.com, and show that they can produce the results satisfying the user's desired output.
Perceptual Interfaces

In recent years, perceptual interfaces have emerged as an increasingly important research direction. The general focus of this area is to integrate multiple perceptual modalities (such as computer vision, speech and sound processing, and haptic I/O) into the user interface. Broadly defined, perceptual interfaces are highly interactive, multimodal interfaces that enable rich, natural, and efficient interaction with computers. More specifically, perceptual interfaces seek to leverage sensing (input) and rendering (output) technologies in order to provide interactions not feasible with standard interfaces and the common triumvirate of I/O devices: the keyboard, mouse and monitor. In this report, we seek to communicate the motivations and goals of perceptual interfaces, to enumerate the relevant technologies, to discuss the integration of multiple modalities, and to describe in more detail the role of computer vision in human-computer interaction. We cover vision problems, constraints, and approaches that are apropos to the area, survey the state of the art in computer vision research and multi-modal interfaces, and take a look at other perceptual technologies such as brain-computer interfaces. We focus on their application to perceptual interfaces, describe several near-term applications, and suggest promising research directions.
Need for automatically generated narration

This paper argues that the best current text-to-speech (TTS) synthesis systems are approaching the quality necessary to provide effective automated narration of audio books. Currently, nearly all audio books and audio journals are recorded by professional voice actors at great expense with significant lead times. These cost and time constraints mean that fewer than than 2% of the new titles published each year are available in "Talking Book" editions, leaving the visually-impaired and print-disabled community of users with few options when seeking material in digital libraries. State-of-the-art TTS systems now can reproduce human voice prosody of sufficient quality to make listening to long narrative reading both pleasant and comprehensible. Such technology is relatively compact and inexpensive; it is time to deploy it widely as an alternative means of accessing digital texts. This would not only directly benefit the reading-disabled community, but also enable "digital natives" and other users to listen to texts on platforms on which reading may not be practical.
Chinese-English SMT for cross-language dialogue agent support

Advances in Statistical Machine Translation (SMT) for breaking language barrier have been seen in recent years, and there is huge demand on cross-language dialog communication between people. In this paper we propose to leverage SMT for supporting cross-language dialog communication. Several techniques are applied to improve the performance on a dialog domain, including rescoring, system combination, post-editing etc. Furthermore, we develop a SMT system targeting dialog management. Various kinds of features specific to dialog modeling, such as reiteration, synonyms in one dialog, are proposed to capture the coherent property of dialog translation. We use travel domain Chinese-English dialog corpus to study the proposed method. Experiments show that the proposed machine translation system provides better translation quality on dialog translation than common phrase-based machine translation system.
Data-Driven Modeling to Assess Receptivity for Rift Valley Fever Virus

Rift Valley Fever virus (RVFV) is an enzootic virus that causes extensive morbidity and mortality in domestic ruminants in Africa, and it has shown the potential to invade other areas such as the Arabian Peninsula. Here, we develop methods for linking mathematical models to real-world data that could be used for continent-scale risk assessment given adequate data on local host and vector populations. We have applied the methods to a well-studied agricultural region of California with 1 million dairy cattle, abundant and competent mosquito vectors, and a permissive climate that has enabled consistent transmission of West Nile virus and historically other arboviruses. Our results suggest that RVFV outbreaks could occur from February–November, but would progress slowly during winter–early spring or early fall and be limited spatially to areas with early increases in vector abundance. Risk was greatest in summer, when the areas at risk broadened to include most of the dairy farms in the study region, indicating the potential for considerable economic losses if an introduction were to occur. To assess the threat that RVFV poses to North America, including what-if scenarios for introduction and control strategies, models such as this one should be an integral part of the process; however, modeling must be paralleled by efforts to address the numerous remaining gaps in data and knowledge for this system.
Getting Closer to MIMO Capacity with Non-Binary Codes and Spatial Multiplexing

In this paper, we discuss the combination of non-binary channel coding with higher-order modulation and MIMO transmission in the form of spatial multiplexing. In addition to the benefits of non-binary LDPC codes on the AWGN channel, we identify an inherent advantage for non-binary coding in multiple antenna schemes. By comparing binary and q-ary information processing at the receiver, we highlight the intrinsic advantages of non-binary coding. A performance comparison based on simulation results shows that, at similar system complexity, non-binary information processing can actually outperform the Shannon limit of its binary counterpart.
Tuning Neural Networks by Both Connectivity and Size

This paper presents a new tuning algorithm for a neural network which not only considers the weighting but also the size and connectivity of the network. The approach is done by two parts: the addition of the switch-based hidden nodes and the application of a modified fitness function. The new model is tested by using two simple logic functions. The results show that the modifications lead to the creation of simpler networks, without sacrificing any accuracy or training time in the process. In addition, the lessened human interaction aspect of the new algorithm is also significant in real-time applications.
Lognormality and oscillations in the coverage of high-throughput transcriptomic data towards gene ends

High-throughput transcriptomics experiments have reached the stage where the count of the number of reads alignable to a given position can be treated as an almost-continuous signal. This allows us to ask questions of biophysical/biotechnical nature, but which may still have biological implications. Here we show that when sequencing RNA fragments from one end, as is the case on most platforms, an oscillation in the read count is observed at the other end. We further show that these oscillations can be well described by Kolmogorov’s 1941 broken stick model. We investigate how the model can be used to improve predictions of gene ends (3′ transcript ends), but conclude that with present data the improvement is only marginal. The results highlight subtle effects in high-throughput transcriptomics experiments which do not have a biological origin, but which may still be used to obtain biological information.
Ultrastructure of Plant Leaf Cuticles in relation to Sample Preparation as Observed by Transmission Electron Microscopy

The leaf cuticular ultrastructure of some plant species has been examined by transmission electron microscopy (TEM) in only few studies. Attending to the different cuticle layers and inner structure, plant cuticles have been grouped into six general morphological types. With the aim of critically examining the effect of cuticle isolation and preparation for TEM analysis on cuticular ultrastructure, adaxial leaf cuticles of blue-gum eucalypt, grey poplar, and European pear were assessed, following a membrane science approach. The embedding and staining protocols affected the ultrastructure of the cuticles analysed. The solubility parameter, surface tension, and contact angles with water of pure Spurr's and LR-White resins were within a similar range. Differences were however estimated for resin : solvent mixtures, since Spurr's resin is combined with acetone and LR-White resin is mixed with ethanol. Given the composite hydrophilic and lipophilic nature of plant cuticles, the particular TEM tissue embedding and staining procedures employed may affect sample ultrastructure and the interpretation of the results in physicochemical and biological terms. It is concluded that tissue preparation procedures may be optimised to facilitate the observation of the micro- and nanostructure of cuticular layers and components with different degrees of polarity and hydrophobicity.
Empirically-derived Methodology for Crowdsourcing Ground Truth

The main challenge for cognitive computing systems, and specifically for their natural language processing, video and image analysis components, is to be provided with large amounts of training and evaluation data. The traditional process for gathering ground truth data is lengthy, costly, and time consuming: (i) expert annotators are not always available; (ii) automated methods generate data with a quality that is affected by noise and ambiguity-specific semantics. Typically, these practices use inter-annotator agreement as a measure of quality. However, in many domains, such as event detection, ambiguity and a multitude of perspectives of the information examples are continuously present. Crowdsourcing-based approaches are gaining popularity in the attempt to solve the issues related to volume of data and lack of annotators. The majority of those approaches also use inter-annotator agreement as a quality measure by assuming that there is only one correct answer for each example. In this paper we present an empirically derived methodology for efficiently gathering of ground truth data in a number of diverse use cases that cover a variety of domains and annotation tasks. Central to our approach is the use of CrowdTruth disagreement-based quality metrics (1) to achieve efficiency in terms of time and cost and (2) to achieve an optimal quality of results in terms of capturing the variety of interpretations in each example. Experimental results show that this methodology can be adapted to a variety of tasks; that it is a time and cost efficient method for gathering ground truth data; and that inter-annotation disagreement is an effective signal in distinguishing with high accuracy good workers from spammers and clear examples from ambiguous ones.
Ieee Transactions on Systems, Man, and Cybernetics—part C: Applications and Reviews 1 Personal Autonomy Rehabilitation in Home Environments by Portable Assistive Robot

—More and more disabled and elderly people with mobility problems want to live autonomously in their home environment. They are motivated to use robotic aids to perform tasks by themselves, avoiding permanent nurse or family assistant supervision. They must find means to rehabilitate their abilities for performing DLA's 1 such as eating, shaving, or drinking. These means may be provided by robotic aids that incorporate possibilities and methods to accomplish common tasks, aiding the user in recovering partial or complete autonomy. Results are highly conditioned by the system's usability and potential. The developed portable assistive robot ASIBOT helps users perform most of these tasks in common living environments. Minimum adaptations are needed to provide the robot with mobility throughout the environment. The robot can autonomously climb from one surface to another, fixing itself to the best place to perform each task. When the robot is attached to its wheelchair, it can move along with it as a bundle. This paper presents the work performed with the ASIBOT in the area of rehabilitation robotics. First, a brief description of the ASIBOT system is given. A description of tests that have been performed with the robot and several impaired users is given in Section III. Insight on how these experiences have influenced our research efforts, especially in home environments, is also included. A description of the test-bed that has been developed to continue research on performing DLA's using robotic aids, a kitchen environment, is given in Section IV. Relevant conclusions are included in final Section V.
A novel statistical analysis and interpretation of flow cytometry data

A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.
Cognitive antenna selection for optimal source localization

Performance of array processing algorithms is directly determined by the number of array elements. Processing of data from a large number of receive array elements involves a large number of receivers, resulting in high cost and complex implementations. In many practical applications, a large number of receiving antenna elements is available, however, the number of receivers is limited due to their high cost. In this work, we address the problem of direction-of-arrival (DOA) estimation with a large number of antenna array elements and a small number of receivers, where the receivers are connected to the array elements via a reconfigurable switching matrix. A cognitive approach, named cognitive antenna selection (CASE), for sequentially switching the elements of the array based on previous observations is proposed. The criterion for antenna selection is based on minimization of the conditional Bobrovski-Zakai bound (BZB) given history observations on the mean-squared-error (MSE) of the DOA estimate. The performance of the CASE algorithm is evaluated via simulations and compared to two other non-adaptive approaches. It is shown that the CASE algorithm outperforms the other compared algorithms in terms of MSE both asymptotically and in the threshold region.
Face Recognition Using 2 D and 3 D Facial Data

Results are presented for the largest experimental study to date that investigates the comparison and combination of 2D and 3D face recognition. To our knowledge, this is also the only such study to incorporate significant time lapse between gallery and probe image acquisition, and to look at the effect of depth resolution. Recognition results are obtained in (1) single gallery and a single probe study, and (2) a single gallery and multiple probe study. A total of 275 subjects participated in one or more data acquisition sessions. Results are presented for gallery and probe datasets of 200 subjects imaged in both 2D and 3D, with one to thirteen weeks time lapse between gallery and probe images of a given subject yielding 951 pairs of 2D and 3D images. Using a PCA-based approach tuned separately for 2D and for 3D, we find that 3D outperforms 2D. However, we also find a multi-modal rank-one recognition rate of 98.5% in a single probe study and 98.8% in multi-probe study, which is statistically significantly greater than either 2D or 3D alone.
Power-law weighted networks from local attachments

This letter introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on extended power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble steady state properties of the empirical citation distributions of i) publications indexed by the Institute for Scientific Information from 1981 to 1997; ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.
Impact of Culture on Human Resource Management Practices: A 10-Country Comparison Влияние культуры на практику HR-менеджмента: сравнение 10 стран

Модель культурного соответствия объясняет, как социокультурное окружение влияет на внутреннюю рабочую культуру и практику HR-менеджмента. Эта модель проверялась в ходе опроса 1954 работников из бизнес-организаций 10 стран. Участники заполняли опросник из 57 вопросов, которыми измерялись мнения менеджеров по 4 социокультурным параметрам, 6 параметрам внутренней рабочей культуры и по практике HR-менеджмента в трех сферах. Умеренные множественные регрессии на индивидуальном уровне анализа показали, что менеджеры, характеризовавшие свое социокультурное окружение как фаталистское, считали также, что работники по своей природе не способны к гибкому реагированию. Эти менеджеры не применяли таких методов, как обогащение работы, делегирование надзорных полномочий, или премирование за превышение плана. Менеджеры, ценившие высокую лояльность, предполагали, что работники должны выполнять обязательства друг перед другом, и практиковали делегирование HR-полномочий. Менеджеры - сторонники патернализма и удаленности сильной власти ожидали от работников реакционности и не практиковали обогащение работы или делегирование полномочий. В статье обсуждаются культурно-специфичные паттерны связей между тремя наборами переменных, а также значение этой работы для кросс-культурной индустриально-организационной психологии.
Option Hedging with Stochastic Volatility

The purpose of this paper is to analyse different implications of the stochastic behavior of asset prices volatilities for option hedging purposes. We present a simple stochastic volatility model for option pricing and illustrate its consistency with financial stylized facts. Then, assuming a stochastic volatility environment, we study the accuracy of Black and Scholes implied volatility-based hedging. More precisely, we analyse the hedging ratios biases and investigate different hedging schemes in a dynamic setting.
Maximizing system throughput in multi-user cooperative relay networks

In this paper we address the resource allocation problem in cooperative relay network. We provide a comparative analysis of two basic protocols namely Amplify and Forward (AF) and Decode and Forward (DF), by implementing different sub-carrier allocation techniques with optimized power allocation. Our analysis is focused on total achievable system throughput and achievable individual throughput for each user.