Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel screen using the Reck tumor suppressor gene promoter detects both conventional and metastasis-suppressing anticancer drugs

The membrane-anchored matrix metalloproteinase-regulator RECK is often down-regulated in various types of cancers; the levels of residual RECK in resected tumors often correlate with better prognosis. Forced expression of RECK in cancer cells suppresses tumor angiogenesis, invasion, and metastasis in xenograft models. RECK is therefore a promising marker for benignancy and a potential effector in cancer therapy. We established a cell line containing two transgene systems: (1) the secreted alkaline phosphatase (SEAP) gene fused to Reck promoter and (2) the HRAS12V oncogene driven by the Tet-off promoter system. This cell line exhibits transformed phenotype in regular medium and flat morphology with increased SEAP activity in the presence of doxycycline, allowing the assessment of RECK-inducing activity of chemicals in the contexts of both transformed and untransformed cells. Our pilot experiments with 880 known bioactive compounds detected 34 compounds that activate RECK promoter; among these, 10 were authentic anticancer drugs. Four selected compounds up-regulated endogenous RECK protein in several human cancer cell lines. The top-ranking compound, disulfiram, strongly suppressed spontaneous lung-metastasis of human fibrosarcoma cells in nude mice. Our data demonstrate the value of this screen in discovering effective cancer therapeutics.
Weakly supervised neural networks for Part-Of-Speech tagging

We introduce a simple and novel method for the weakly supervised problem of Part-Of-Speech tagging with a dictionary. Our method involves training a connectionist network that simultaneously learns a distributed latent representation of the words, while maximizing the tagging accuracy. To compensate for the unavailability of true labels, we resort to training the model using a Curriculum: instead of random order, the model is trained using an ordered sequence of training samples, proceeding from “easier” to “harder” samples. On a standard test corpus, we show that without using any grammatical information, our model is able to outperform the standard EM algorithm in tagging accuracy, and its performance is comparable to other state-of-the-art models. We also show that curriculum learning for this setting significantly improves performance, both in terms of speed of convergence and in terms of generalization.
A Context for Developing Structural Knowledge for Academic Writing: Teaching and Learning Analytic Reading and Writing in an Intermediate English as a Second Language Composition Course

This study examined the teaching and learning of analytic writing in response to literary and informational read ings in an intermediate, underg raduate English a s a S econd Language (ESL) composition course. An ESL com position instructor and her two sections of an ESL composition course (N=29) w ere selected to collaborate with the researcher. For a period of ten w eeks observational and c a se study techniques were employed to d escribe and analyze the instructional context. Twelve of the 29 students w ere se lected for ex tended analyses of their written work in order to docum ent their developm ent a s analytic writers, especially through their th ree major writing assignm ents. Also, th ree students were selected for c a se studies from th ese tw elve to study the studen ts ' experiences in the writing course Four types of a n a ly se s and their corresponding m easures w ere used to study knowledge for analytic writing at both the local and global level of text: (a) syntactic complexity (T-unit). (b) classification of statem ents of content (type of statem ent), (c) e s sa y gen re (structural analysis), and (d) level of ab strac tness (focused and unfocused analysis). D ata from classroom observations, tutorials, interviews, declarative know ledge probes, questionnaires, and e ssay analyses were u sed to describe thoroughly the p rocesses involved in teaching and learning to write analytically, and to describe change over time in the writing in response to reading of a sam ple of ESL writers. Results indicate that daily classroom interaction sh ap es the studen ts ' interpretations of what it m eans to write analytically, and of their expectations for th is writing course. The students ' personal and educational backgrounds also influence these interpretations. In this particular classroom , activities relating to procedural knowledge about issues of content w ere excluded, while th e only expectations enacted in the classroom w ere those shared by all the a g e n ts involved in the conceptualization of the targeted course. Results from the essay analyses offer limited support to the idea that the students w ere developing as analytic writers. Although studen ts had internalized the g en re conventions for analytic writing, there w as only limited developm ent of their interpretive skills n ecessa ry for analytic thinking and reasoning. Two m ajor implications from this study concern (a) the im portance of integrating both d isco u rse structure and studen ts ' thinking to enab le s tu d en ts to write analytically in a second language, and (b) the realization that learning to write analytically is a com plex process that necessita tes institutional support at all levels of its im plem entation and conceptualization
Highlighting the Relations between Interaction Types in Meetings and Group Performance

Are some types of interactions beneficial or harmful to team performance? Should conflict among members be promoted or avoided in teams to achieve better results? Many questions arise when trying to understand how group dynamics impact its performance. The present study intends to highlight the kinds of verbal interactions which influence the performance of a team during a meeting. A two-phase (design and building) videotaped decision-making experiment was organised with 41 teams. All interactions were then coded with the INSIDE TEAMS3D (IT3D) coding system. Three dimensions of interactions were considered: their functional meaning, the convergence among interactions (including content or process conflict) and their interpersonal emotional meaning (including indications of relational conflict). Performance was measured for each group as an assignment result. It was positively associated with the total number of interactions shared. The results support that some functional meaning categories are positively associated with performance: thus, groups which shared more interactions aiming at offering an opinion or a suggestion on the content, get on average better outcomes. Socialization categories (making jokes, fostering the conversation) were also positively correlated with performance. The study of interactions convergence within groups also provides significant results, showing that disagreements on the content have positive relations with team performance. Negative interpersonal emotional meaning is also showing positive relations with performance, which are discussed in the paper. On top of these considerations on interaction types, the research reveals that the groups with balanced contributions between members during the exercise got higher performance than those facing great heterogeneity. More generally, this paper provides methodological insights about a way to study group dynamics and gather data on interactions and performance.
Frequency estimation of harmonics with multiplicative noise using ESPRIT

This paper investigates the parameter estimation of harmonics with multiplicative noise. We presented a frequency estimation method based on ESPRIT to estimate the frequency of harmonics with multiplicative noise. A cyclic covariance matrix using a class of cyclic covariance of the harmonics is constructed. By analyzing the eigen structure of cyclic covariance matrix, we extend ESPRIT to estimate the frequency of harmonics with multiplicative noise. The proposed ESPRIT-based frequency estimation method has high-resolution and can directly estimate the frequency without peak searching. The effectiveness of the proposed ESPRIT-based method has been verified by simulation results.
An Invitation to Live: Insights from an Older, Long-Term Practitioner of Tai Chi

Background: Hundreds of randomized controlled studies have documented the effectiveness of Tai Chi (TC) to alleviate symptoms from varied medical conditions. Researchers, however, have called for alternate methods to discern participants’ perspectives and to provide additional information regarding the TC protocols, TC teachers’ qualifications, and the social contexts for learning and practicing TC. Purpose: This naturalistic study was designed to explore the perspectives of an experienced TC practitioner and those of her TC teachers as they reflected upon her 9-year journey with TC as she faced varied medical crises. Methods: Using an ethnographic approach, participant observation, the researcher observed participant’s TC classes, conducted in-depth, semi-structured interviews with the participant and her teachers and informal interviews in the field. Context: Participant was a 72-year-old female, who had suffered from spinal stenosis, hip arthritis, and bladder cancer. The participant attended two types of TC classes: a therapeutic Tai Chi (TTC) including a modified short form (10 forms) taught by a physical therapist and a traditional TC class including the Yang style short form taught by a Tai Chi practitioner with 50 years of experience. Findings: 1) TCC provided explicit cues and links between TC and functional fitness, improving the participant’s balance, daily life activities, and increased exercise efficacy and resilience; 2) TC provided a novel challenge motivating the participant’s continued practice while learning complex TC movements. 3) Traditional TC classes provided a supportive community, facilitating a meditative stance, and enabled the participant to subdue the “monkey mind” of a terminal cancer diagnosis.
Performance of Digital filters for noise removal from ECG signals in Time domain

3 Abstract: Electrocardiogram (ECG) is the cheapest and noninvasive method of depicting the heart activity and abnormalities. It provides valuable information about the functional aspects of the heart and cardiovascular system. It is the record of variation of bioelectric potential with respect to time as the human heart beats. The classification of ECG signals is an important application since the early detection of heart diseases/abnormalities can prolong life and enhance the quality of living through appropriate treatment. Since the ECG signals, while recording are contaminated by several noises it is necessary to preprocess the signals prior to classification. The ECG signal can be processed in time domain as well as in frequency domain. Digital filters are used to remove noise from the signal. The present paper shows the performance of removal of noise like baseline wander and power line interference from the signal using Band Pass filter and Notch filter. The performance is tested on the Arrhythmia signals from MIT-BIH and Myocardial infarction signals from PTB Diagnostic database.
A database of local masking thresholds in natural images

The ability of an image region to hide or mask a target signal continues to play a key role in the design of numerous image-processing and vision applications. However, one of the challenges in designing an effective model of masking for natural images is the lack of ground-truth data. To address this issue, this paper describes a psychophysical study designed to obtain local contrast detection thresholds (masking maps) for a database of natural images. Via a three-alternative forced-choice experiment, we measured the thresholds for detecting 3.7 cycles/deg vertically oriented log-Gabor targets placed within each 85×85-pixel patch (1.9 deg patch) of 15 natural images from the CSIQ image database [Larson and Chandler, JEI, 2010]. Thus, for each image, we obtained a masking map in which each entry in the map denotes the RMS contrast threshold for detecting the log-Gabor target at the corresponding spatial location in the image. Here, we describe the psychophysical procedures used to collect the thresholds, we provide analyses of the results, and we provide some outcomes of predicting the thresholds via basic low-level features, a computational masking model, and two modern imagequality assessment algorithms.
MCBIST : A New On-Line BIST Scheme

Built-In-Self-Test (BIST) is an efficient and practical technique for testing VLSI circuits and systems. Among the different BIST techniques, the Comparative Concurrent BIST (CBIST) has many advantages since it can provide on-line and off-line testing with the same hardware. However, in CBIST when the width of the circuit lines increases, on-line testing becomes inefficient. The problem is that finding a match between input data and test sequence has a low chance when the input line width increases. In this paper, we propose MCBIST (Multi CBIST) a new on-line BIST scheme that addresses the problem of concurrent on-line testing in CBIST. In addition, our method improves the performance of non-concurrent online testing. A distributed version of MCBIST is also discussed in this paper.
Frequency Offset Estimation in OFDM Systems using Threshold based Scheme

The Performance of the Orthogonal Frequency Division Multiplexing (OFDM) systems degrade due to Doppler frequency or frequency drift between transmitter and receiver oscillator which causes frequency offset and hence leads to a number of impairments in the received signal, thereby making its detection incorrect. In this paper, a threshold-based frequency offset estimation algorithm for orthogonal frequency division multiplexing (OFDM) systems is further analyzed. The algorithm shows robust performance in the presence of timing offset. The threshold used in correlation and coherence phase bandwidth lowers the computation complexity when compared with the existing schemes. Through a detailed analysis and simulation over AWGN channel, it has been shown that the scheme analyzed maintains the same level of estimation performance with lower complexity than the existing schemes. KeywordsFrequency offset, OFDM, synchronization, coherence phase bandwidth (CPB)
Automatic target recognition using boundary partitioning and invariant features in forward-looking infrared images

We propose an automatic target recognition (ATR) algorithm for recognizing nonoccluded and partially occluded military vehicles in natural forward-looking infrared (FLIR) images. The proposed algorithm consists of global and local feature extraction from partitioned boundaries of a target, and a new classification method using multiple multilayer perceptrons (MLPs). After segmenting a target, the target contour is partitioned into four local boundaries. Radial and distance functions are defined from the target contour and local boundaries, and are used to define global and local shape features, respectively. The global and local shape features are more invariant to similarity transform than traditional feature sets. Four feature vectors are composed of the global and local shape features, and are used as inputs of MLPs. The outputs of MLPs are combined to recognize nonoccluded and partially occluded targets. In the experiments, we show that the proposed features are superior to the traditional feature sets with respect to invariance and recognition performance.
Multimodal approaches for emotion recognition: a survey

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.
Two‐phase scale‐based reduction method for fulfilling monitoring service on mobile devices

One of the general location‐based services (LBSs) is the monitoring of real‐time locations of moving objects. When the number of moving objects is large and the task of monitoring is carried out on mobile devices, the monitoring service suffers from constraints of screen size, computing speed, and network bandwidth. In the present paper, a two‐phase scale‐based reduction method (SRM) consisting of a zoom phase and a mosaic phase, is proposed to overcome these constraints. The zoom phase reduces the original monitoring area which, in turn, undergoes further reduction in the mosaic phase. The performance was measured with the use of two ratios: the reduction ratio (RRatio) and the transmission ratio (TRatio). From the experimental results, the lowest RRatio was 52%, i.e. almost half of the original data size was reduced. The lowest average TRatio was also 52% for the worst case, i.e. the entire original monitoring area was displayed on the mobile device. Moreover, the display time was shortened from 14.3 to 0.7 s. These results show that the use of the two‐phase SRM is practical and efficient when applied to the monitoring service on mobile devices. Copyright © 2006 John Wiley & Sons, Ltd.
NTrace: Function Boundary Tracing for Windows on IA-32

For a long time, dynamic tracing has been an enabling technique for reverse engineering tools. Tracing can not only be used to record the control flow of a particular component such as a piece of malware itself, it is also a way to analyze the interactions of a component and their impact on the rest of the system. Unlike Unix-based systems, for which several dynamic tracing tools are available, Windows has been lacking appropriate tools. From a reverse engineering perspective, however, Windows may be considered the most relevant OS, particularly with respect to malware analysis. In this paper, we present NTrace, a dynamic tracing tool for the Windows kernel, drivers, system libraries, and applications that supports function boundary tracing. NTrace incorporates 2 novel approaches: (1) a way to integrate with Windows Structured Exception Handling and (2) a technique to instrument binary code on IA-32 architectures that is both safe and more efficient than DTrace.
An error limit for the evolution of language

On the evolutionary trajectory that led to human language there must have been a transition from a fairly limited to an essentially unlimited communication system. The structure of modern human languages reveals at least two steps that are required for such a transition: in all languages (i) a small number of phonemes are used to generate a large number of words; and (ii) a large number of words are used to a produce an unlimited number of sentences. The first (and simpler) step is the topic of the current paper. We study the evolution of communication in the presence of errorsand show that this limits the number of objects (or concepts) that can be described by a simple communication system. The evolutionary optimum is achieved by using only a small number of signals to describe a few valuable concepts. Adding more signals does not increase the fitness of a language. This represents an error–limit for the evolution of communication. We show that this error limit can be overcome by combining signals (phonemes) into words. The transition from an analog to a digital system was a necessary step toward theevolution of human language.
Using Promethee methods for multi-criteria pull-based scheduling on DCIs

Scheduling tasks in distributed computing infrastructures (DCIs) is challenging mainly because the scheduler is facing a number of more or less dependent parameters that characterize the hosts coming from a particular computing environment and the tasks. In this paper we introduce a multi-criteria scheduling method for DCIs, aiming a better matching between hosts, and tasks waiting in a priority queue at a pull-based scheduler. The novelty of the approach consists in employing the Promethee [1] decision aid for selecting tasks. In the aim of computing preference relationships (priorities) among tasks, this approach performs pairwise comparisons of values that characterize tasks. The method exhibits interesting advantages, such as allowing the user to choose the values for the computation of the priorities, like the expected completion time (ECT) and cost. The approach is also very flexible, allowing through a set of parameters the specification of particular scheduling policies. To validate this method we built an XtrebWeb-like simulator, which is capable of running on real traces. We experiment on internet desktop grid (IDG), cloud and best effort grid (BEG), with various workloads. The results show that the Promethee-based scheduling method obtains good performance especially on IDG when certain fractions of the tasks fail. We also prove that multi-criteria scheduling using Promethee performs better than single-criterion scheduling, improving both makespan and cost. Also, a simple definition of ECT is the most efficient in terms of makespan. In this work we also explain the challenges of using Promethee for scheduling in DCIs.
Exploiting Neigborhood Similarity for Virtual Machine Migration over Wide-Area Network

Conventional virtual machine (VM) migration focuses on transferring a VM's memory and CPU states across host machines. The VM's disk image has to remain accessible to both the source and destination host machines through shared storage during the migration. As a result, conventional virtual machine migration is limited to host machines on the same local area network (LAN) since sharing storage across wide-area network (WAN) is inefficient. As datacenters are being constructed around the globe, we envision the need for VM migration across datacenter boundaries. We thus propose a system aiming to achieve efficient VM migration over wide area network. The system exploits similarity in the storage data of neighboring VMs by first indexing the VM storage images and then using the index to locate storage data blocks from neighboring VMs, as opposed to pulling all data from the remote source VM across WAN. The experiment result shows that the system can achieve an average 66% reduction in the amount of data transmission and an average 59% reduction in the total migration time.