Miracle-dz/UniFAR-FTUs huggingface dataset:
MAEPER: Matching Access and Error Patterns With Error-Free Resource for Low Vcc L1 Cache

Large SRAMs are the practical bottleneck to achieve a low supply voltage, because they suffer from process variation-induced bit errors at a low supply voltage. In this paper, we present an error-resilient cache architecture that resolves the drawback of previous approaches, i.e., the performance degradation at a low supply voltage which is caused by cache misses in accesses to faulty resources. We utilize cache access locality and error-free resources in a cost-effective manner. First, we classify cache lines into fully and partially accessed groups and apply appropriate methods to each group. For the partially accessed group, we propose a method of matching memory access behavior and error locations with intra-cache line word-level remapping. In order to reduce the area overhead used to store the cache access information history, we present an access pattern-learning line-fill buffer (LFB). For the fully accessed group, we propose the utilization of error-free assist functions in the cache, i.e., a LFB and victim cache with no process variation-induced error at the target minimum supply voltage. We also present an error-aware prefetch method that allows us to utilize the error-free victim cache to achieve a further reduction in cache misses due to faulty resources. Experimental results show that the proposed method gives an average 32.6% reduction in cycles per instruction at an error rate of 0.2% with a small area overhead of 8.2%.
Deep Representation of Facial Geometric and Photometric Attributes for Automatic 3D Facial Expression Recognition

In this paper, we present a novel approach to automatic 3D Facial Expression Recognition (FER) based on deep representation of facial 3D geometric and 2D photometric attributes. A 3D face is firstly represented by its geometric and photometric attributes, including the geometry map, normal maps, normalized curvature map and texture map. These maps are then fed into a pre-trained deep convolutional neural network to generate the deep representation. Then the facial expression prediction is simplyachieved by training linear SVMs over the deep representation for different maps and fusing these SVM scores. The visualizations show that the deep representation provides a complete and highly discriminative coding scheme for 3D faces. Comprehensive experiments on the BU-3DFE database demonstrate that the proposed deep representation can outperform the widely used hand-crafted descriptors (i.e., LBP, SIFT, HOG, Gabor) and the state-of-art approaches under the same experimental protocols.
The Role of Wind Forecasting in Grid Operations &amp; Reliability

Wind energy is increasingly viewed as a desirable energy source, but it must be integrated into power grids and electric utility systems that are understandably oriented around issues of power, capacity and reliability. It is important to know the actual impact costs of significant wind penetration levels, and the paper first describes a study approach and the results of a specific wind integration study in North America. Once the impact costs are understood, it is then possible to design tools and operating practices to minimize these impact costs. Wind forecasting tools are a critical element in minimizing the impact costs and ensuring reliability with larger amounts of wind energy on the grid, and the approaches and reasonable expectations for state-of-the-art wind forecasting methods are described. The value of wind energy is increased, and larger amounts of wind energy can be used, if it can be scheduled using wind forecasting methods
Automation cueing modulates cerebral blood flow and vigilance in a simulated air traffic control task

Automation cueing of impending hazards or critical signals can have both beneficial and deleterious effects on the human operator, depending on automation reliability. To assess these effects, transcranial Doppler sonography (TCD), a low-cost non-invasive procedure that allows continuous monitoring of blood flow in the left and right cerebral hemispheres, was used to examine the influence of automation cues of varying reliability on vigilance performance in a 40-min simulated air traffic control task. Four levels of automation cue reliability (100, 80 and 40% reliable cueing and a no-cue control) were combined factorially with two levels of critical signal salience (high and low). For both levels of signal salience, the detection rate of critical signals was very high and remained stable over time with 100% cue-reliability, but declined over time in the remaining cue conditions, so that, by the end of the task, performance efficiency was best in the 100% condition followed in order by the 80%, 40%, and no-cue conditions. These performance effects for cueing were very closely mirrored by changes in cerebral blood flow in the right (but not the left) hemisphere in conjunction with low salience signals. The results fit well with an attentional resource model of vigilance and show that cerebral blood flow provides a metabolic index of the utilization of information-processing resources during sustained attention. The demonstration of systematic modulation of cerebral blood flow with time on task, automation cueing and signal salience also provides strong evidence for a right hemispheric brain system that is involved in the functional control of vigilance performance over time. Measurement of the activation of this system, as a reflection of operator mental workload, can, therefore, inform the design of optimal automation cueing.
Automatic Segmentation of Capillary Non-Perfusion in Retinal Angiograms

Capillary Non-Perfusion (CNP) is a condition in diabetic retinopathy where blood ceases to flow to certain parts of the retina, potentially leading to blindness. This paper presents a solution for automatically detecting and segmenting CNP regions from fundus fluorescein angiograms (FFAs). CNPs are modelled as valleys, and a novel multiresolution technique for trough-based valley detection is presented. The proposed algorithm has been tested on 40 images and validated against expert-marked ground truth. Obtained results are presented as a receiver operating characteristic (ROC) curve. The area under this curve is 0.842 and the distance of ROC from the ideal point (0,1) is 0.31.
The Escherichia coli Methyl-Directed Mismatch Repair System Repairs Base Pairs Containing Oxidative Lesions

ABSTRACT A major role of the methyl-directed mismatch repair (MMR) system of Escherichia coli is to repair postreplicative errors. In this report, we provide evidence that MMR also acts on oxidized DNA, preventing mutagenesis. When cells deficient in MMR are grown anaerobically, spontaneous mutation frequencies are reduced compared with those of the same cells grown aerobically. In addition, we show that a dam mutant has an increased sensitivity to hydrogen peroxide treatment that can be suppressed by mutations that inactivate MMR. In a dam mutant, MMR is not targeted to newly replicated DNA strands and therefore mismatches are converted to single- and double-strand DNA breaks. Thus, base pairs containing oxidized bases will be converted to strand breaks if they are repaired by MMR. This is demonstrated by the increased peroxide sensitivity of a dam mutant and the finding that the sensitivity can be suppressed by mutations inactivating MMR. We demonstrate further that this repair activity results from MMR recognition of base pairs containing 8-oxoguanine (8-oxoG) based on the finding that overexpression of the MutM oxidative repair protein, which repairs 8-oxoG, can suppress the mutH-dependent increase in transversion mutations. These findings demonstrate that MMR has the ability to prevent oxidative mutagenesis either by removing 8-oxoG directly or by removing adenine misincorporated opposite 8-oxoG or both.
A methodology for sizing primary frequency control in function of grid inertia

Large increase in wind and photovoltaics will lead to lower and more volatile grid inertia but also to the lack of primary frequency control, i.e. frequency containment reserve (FCR) providers in the Continental Europe Grid. This will therefore impact negatively the frequency dynamics. One potential solution for ensuring the frequency to stay in reasonable bounds is to take the dynamics of potential providers into consideration. From this observation, we develop a novel methodology for having a linear programming problem to select the most suited FCR providers. It is based on the minimization of the overall cost while respecting static and dynamic constraints which are respectively ensuring a sufficient amount of FCR and avoiding too large frequency deviations after a large disturbance. The methodology has been tested on a case with 200 potential FCR providers in function of various grid inertia and load damping factors.
Supervised and Unsupervised Web Document Filtering Techniques to Improve Text-Based Music Retrieval

We aim at improving a text-based music search engine by applying different techniques to exclude misleading information from the indexing process. The idea of the original approach is to index music pieces by “contextual” information, more precisely, by all texts to be found on Web pages retrieved via a common Web search engine. This representation allows for issuing arbitrary textual queries to retrieve relevant music pieces. The goal of this work is to improve precision of the retrieved set of music pieces by filtering out Web pages that lead to irrelevant tracks. To this end we present two unsupervised and two supervised filtering approaches. Evaluation is carried out on two collections previously used in the literature. The obtained results suggest that the proposed filtering techniques can improve results significantly but are only effective when applied to large and diverse music collections with millions of Web pages associated.
Adult Vampire Bats Produce Contact Calls When Isolated: Acoustic Variation by Species, Population, Colony, and Individual

Background Bat pups produce individually distinct isolation calls to facilitate maternal recognition. Increasing evidence suggests that, in group-living bat species, adults often use similar calls to maintain contact. We investigated if isolated adults from all three species of the highly cooperative vampire bats (Phyllostomidae: Desmodontinae) would produce vocally distinct contact calls when physically isolated. Methods/Principal Findings We assessed variation in contact calls recorded from isolated captive and wild-caught adult common vampire bats (Desmodus rotundus), white-winged vampire bats (Diaemus youngi) and hairy-legged vampire bats (Diphylla ecaudata). We compared species-typical contact call structure, and used information theory and permuted discriminate function analyses to examine call structure variation, and to determine if the individuality of contact calls is encoded by different call features across species and populations. We found that isolated adult vampire bats produce contact calls that vary by species, population, colony, and individual. However, much variation occurred within a single context and individual. We estimated signature information for captive Diaemus (same colony), captive Desmodus (same colony), and wild Desmodus (different colonies) at 3.21, 3.26, and 3.88 bits, respectively. Contact calls from a captive colony of Desmodus were less individually distinct than calls from wild-caught Desmodus from different colonies. Both the degree of individuality and parameters encoding individuality differed between the bats from a single captive colony and the wild-caught individuals from different groups. This result is consistent with, but not sufficient evidence of, vocal convergence in groups. Conclusion Our results show that adult vampire bats of all three species produce highly variable contact calls when isolated. Contact calls contain sufficient information for vocal discrimination, but also possess more intra-individual variation than is required for the sole purpose of identifying individuals.
Theoretical and Analytical Issues in Studying Organizational Processes

Formulation of dynamic theories and process hypotheses is a crucial component in longitudinal research. This paper describes a framework for developing dynamic theory and hypotheses. The procedure require the theorist to address six dimensions of process in each variable: continuity, magnitude of change, rate of change, trend, periodicity and duration. Further, theorists are encouraged to explore the dynamic relations between sets of variables, including rate of change, magnitude of change, lag, and permanence. Consideration is given to the problem of feedback loops. A typology of analytical alternatives for studying dynamic processes and longitudinal research data is provided.
POND: The Power of Zone Overlapping in DHT Networks

Distributed hash table (DHT) based algorithms have been proposed to solve the routing issues in large-scale peer-to-peer (P2P) applications. DHT systems create an elegant peer-to-peer service model and work perfect in a homogeneous environment. Workload are evenly distributed on every participating peer and system is supposed to achieve the optimal performance. However, in the real world, the complicated scenarios such as peerspsila capability heterogeneity, diverse file popularities, and mismatching problem between physical network structure and logical routing data structure, jeopardize the efficiency of DHT infrastructure and may result in serious load imbalance problem. In this paper, we propose -- POND, a novel and efficient DHT algorithm to relieve the above problem. In POND, peers are divided into two types: main peers (MP) and shadow peers(SP). As other DHT systems, POND splits the name space into several disjointed zones. However, unlike other systems which only have one peer in charge of each zone, in POND, we use zone overlapping technique. In each zone, a MP and several SPs are used for services. Any of them is capable to handle service requests fallen into this zone. When a new request is coming,the peer with the minimal load has the high possibility to be chosen to provide the service. Thus, the risk of overload problem is reduced. With the introduction of shadow peers, POND also provides better failure tolerance and relieves the hot spot problem. Moreover, POND can improve the routing performance by reducing the average number of routing hops per request. We conduct extensive simulation experiments and the results show POND is superior to other DHT algorithms.
The Team Personality–Team Performance Relationship Revisited: The Impact of Criterion Choice, Pattern of Workflow, and Method of Aggregation

Using meta-analytic evidence, this study tested trait- and task-based theoretical approaches to team personality management, using both team behaviors and team outcomes as criteria. Trait theories state that maximization of the team trait is harmful for Extroversion (complementary team fit) but beneficial for Conscientiousness, Agreeableness, and Emotional Stability (supplementary fit). Task-based theories state that tasks with few work exchanges are best reflected by mean trait scores, whereas tasks with frequent work exchanges are best represented by other types of scores (e.g., minimum score). Correlations between different aggregations of team personality and team performance were coded, as well as the study criterion choice and the pattern of workflow (as moderators). Partial support for both trait and task theories were found. Team Conscientiousness and Agreeableness provided supplementary fit primarily with team behaviors, but there was mixed evidence that Extroversion provided complementary fit. Furthermore, minimum and variance measures of the team trait related to team performance in tasks with frequent work exchanges, but not in tasks with few work exchanges. Results suggest several limitations with existing measurement methods, which are discussed.
Modeling and Control Strategies of Fuzzy Logic Controlled Inverter System for Grid Interconnected Variable Speed Wind Generator

Today, variable speed operation of a permanent magnet synchronous generator (PMSG) is becoming popular in the wind power industry (PI). A variable speed wind turbine (VSWT)-driven PMSG, in general, is connected to the grid using a fully controlled frequency converter (FC). Along with the generator side converter, the FC necessitates the grid side inverter system that has a great impact on the stability issue of the VSWT-PMSG, especially in the case of network disturbance. The well-known cascaded-controlled inverter system has widely been reported in much of the literature, where multiple PI controllers are used in inner and outer loops. However, a fuzzy logic controller deals well with the nonlinearity of the power system, compared to a PI controller. This paper presents a simple fuzzy logic controlled inverter system for the control of a grid side inverter system, which suits well for VSWT-PMSG operation in a wide operating range. This is one of the salient features of this paper. Detailed modeling and control strategies of the overall system are demonstrated. Both dynamic and transient performances of VSWT-driven PMSG are analyzed to show the effectiveness of the control strategy, where simulation has been done using PSCAD/EMTDC.
On the system F as a glue language for natural-language compositional-semantics

In order to model in compositional framework some phenomena of lexical pragmatics and in particular the ones studied by Nicholas Asher several contributions developed in our team did use the system F of Jean-Yves Girard to construct logical formulae expressing the meaning of sentences --- while other authors prefer to use Per Martin-Lof's type theory In this note we explain the motivations supporting our preference for system F.
Task-aware search personalization

Search personalization has been pursued in many ways, in order to provide better result rankings and better overall search experience to individual users [5]. However, blindly applying personalization to all user queries, for example, by a background model derived from the user's long-term query-and-click history, is not always appropriate for aiding the user in accomplishing her actual task. User interests change over time, a user sometimes works on very different categories of tasks within a short timespan, and history-based personalization may impede a user's desire of discovering new topics. In this paper we propose a personalization framework that is selective in a twofold sense. First, it selectively employs personalization techniques for queries that are expected to benefit from prior history information, while refraining from undue actions otherwise. Second, we introduce the notion of tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics, and base our reasoning selectively on query-relevant user tasks. These considerations are cast into a statistical language model for tasks, queries, and documents, supporting both judicious query expansion and result re-ranking. The effectiveness of our method is demonstrated by an empirical user study.
Genetic variation, nucleotide diversity, and linkage disequilibrium in seven telomere stability genes suggest that these genes may be under constraint

To maintain chromosomal integrity and to protect the ends of chromosomes against recognition as damaged DNA, end‐to‐end fusion, or recombination, a coordinated set of genes is required to stabilize the telomere. We surveyed common genetic variation in seven genes that are vital to telomere stability (TERT, POT1, TNKS, TERF1, TINF2, TERF2, and TERF2IP) and validated single nucleotide polymorphisms (SNPs) in four different ethnic groups (n=118 total). Overall, our data show limited degrees of nucleotide diversity in comparison with data from other gene families. We observed that these genes are highly conserved in sequence between species, and that for nearly all of the coding SNPs the most common allele is ancestral (i.e., it is observed in primate sequences). Our findings support the hypothesis that genetic variation in a pathway that is critical for telomere stability may be under constraint. These data establish a foundation for further investigation of these genes in population‐genetics, evolution, and disease‐association studies. Hum Mutat 26(4), 343–350, 2005. Published 2005, Wiley‐Liss, Inc.
Exploring the supervisor role as a facilitator of knowledge sharing in teams

This paper explores the themes and implications, concerning the role of the supervisor as a facilitator of knowledge sharing in teams. After describing the strategic context for devolving human resource responsibilities to line managers, the paper defines and discusses the line manager/supervisor role. The barriers to learning in the workplace are considered. It is suggested that supervisors, through their devolved responsibility for people (which, by implication, includes learning and development) have an influence as facilitators promoting knowledge sharing within their teams. The paper examines whether the supervisor's intervention as a facilitator, could provide the important link leading to the sharing of individual and collective tacit knowledge in teams.
A Dual-Use Speech CAPTCHA: Aiding Visually Impaired Web Users while Providing Transcriptions of Audio Streams

This work focuses on development of an alternative aural CAPTCHA technology based on a speech transcription task. The principal goals are (1) to provide a better quality of service for visually impaired web users who currently have few alternatives for accessing web services protected by CAPTCHAs based on visual perception tasks, (2) as a side effect of CAPTCHA use, collect meaning data, such as transcriptions of speech found in online audio streams, and (3) deploy the CAPTCHA publicly to increase the general awareness of accessibility concerns on the web for users with disabilities. Toward these ends, an experimental corpus of human transcriptions of short audio clips was created to support aural CAPTCHA experimentation. Results suggest that the techniques investigated represent viable aural reCAPTCHA mechanisms.
Screening for Psychosis in the General Population with a Self‐Report Interview

This analysis applies methods of screening to the problem of psychosis. A probability sample of 810 individuals from the Eastern Baltimore Mental Health Survey was interviewed in the self-report modality with the Diagnostic Interview Schedule and shortly thereafter by a psychiatrist. It is shown that a configuration of responses in the self-report modality can screen moderately well for psychosis, as measured by psychiatrists in the clinical modality.
Non-local Color Image Denoising with Convolutional Neural Networks

We propose a novel deep network architecture for grayscale and color image denoising that is based on a non-local image model. Our motivation for the overall design of the proposed network stems from variational methods that exploit the inherent non-local self-similarity property of natural images. We build on this concept and introduce deep networks that perform non-local processing and at the same time they significantly benefit from discriminative learning. Experiments on the Berkeley segmentation dataset, comparing several state-of-the-art methods, show that the proposed non-local models achieve the best reported denoising performance both for grayscale and color images for all the tested noise levels. It is also worth noting that this increase in performance comes at no extra cost on the capacity of the network compared to existing alternative deep network architectures. In addition, we highlight a direct link of the proposed non-local models to convolutional neural networks. This connection is of significant importance since it allows our models to take full advantage of the latest advances on GPU computing in deep learning and makes them amenable to efficient implementations through their inherent parallelism.