Miracle-dz/UniFAR-FTUs huggingface dataset:
The complex duration perception of emotional faces: effects of face direction

The perceived duration of emotional face stimuli strongly depends on the expressed emotion. But, emotional faces also differ regarding a number of other features like gaze, face direction, or sex. Usually, these features have been controlled by only using pictures of female models with straight gaze and face direction. Doi and Shinohara (2009) reported that an overestimation of angry faces could only be found when the model’s gaze was oriented toward the observer. We aimed at replicating this effect for face direction. Moreover, we explored the effect of face direction on the duration perception sad faces. Controlling for the sex of the face model and the participant, female and male participants rated the duration of neutral, angry, and sad face stimuli of both sexes photographed from different perspectives in a bisection task. In line with current findings, we report a significant overestimation of angry compared to neutral face stimuli that was modulated by face direction. Moreover, the perceived duration of sad face stimuli did not differ from that of neutral faces and was not influenced by face direction. Furthermore, we found that faces of the opposite sex appeared to last longer than those of the same sex. This outcome is discussed with regards to stimulus parameters like the induced arousal, social relevance, and an evolutionary context.
MSSS : Most Significant Single-keyword Search over Encrypted Cloud Data

Cloud Computing is a popular computing technique via the Internet. The data owner outsources local data to the public cloud server to reduce the cost of the data management . Sensitive data has to be encrypted to ensure privacy before o utsourcing. All traditional Searchable Symmetric Encryption (SSE) schemes search only over encrypted data through keywords,h ence they do not provide effective data utilisation for large dataset files in cloud. In this paper, we propose a Most Significant Single-keyword Search (MSSS), that supports efficient sear ch using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secur e search without the overhead of learning from the attacker/cloud provider. The proposed scheme reduces the computation over head. Through numerical analysis, it is shown that the MSSS scheme can reduce the computation cost of data on owner side to O(NT × 3). The time complexity of search time is reduced to O(B) for bucket size B. The proposed scheme is highly secure and efficient in comparison to the state of the art works.
Early catheter removal following transurethral prostatectomy--impact on length of hospital stay.

OBJECTIVE To investigate whether early catheter removal following transurethral prostatectomy (TURP) is safe and whether it has any effect on the length of hospital stay. PATIENTS AND METHODS Following transurethral prostatectomy 59 patients were randomized into one of two groups: those whose catheter was removed on day 1 after surgery and those whose catheter was removed on day 2. The incidence of complications and the duration of post-operative hospital stay were assessed. RESULTS Catheter removal on day 1 led to a significantly shorter post-operative hospital stay (2.3 days versus 3.3 days) and did not incur a higher incidence of complications. CONCLUSIONS Removal of the catheter on the first day following TURP is safe in selected patients and leads to a shorter post-operative hospital stay.
Coastal dune hydrology and its influence on maritime occupation sites (12–0.2 ka) in a semiarid island, San Miguel, California, USA

Maritime occupation sites in upland dune settings (10–150 m elevation mean sea level) in San Miguel Island (37 km2 in size), located 40 km offshore of the south‐central California mainland, were analyzed for reported ages, component types, and distances from paleo‐shorelines around the island’s shelf platform. The occupation sites (dated ~12,200 to 200 cal B.P.) include numerous shell middens and lithic scatters. Some sites contain Paleocoastal stemmed points and chipped stone crescents, the latter believed to be used for hunting waterfowl. What prompted the site occupations in the semiarid dune‐covered coastal bluffs and interior‐plateaus that were located 10–150 m above and 1–4 km distant from age‐correlative paleo‐shorelines? Eolianite dune settings in San Miguel include ephemeral freshwater sources from: (a) vernal pond/wetlands in interior plateaus; (b) gullies or creeks in the dune‐covered bluff slopes; (c) springs exposed in current sea cliffs or canyons; and (d) pond/wetlands barraged by sand ramps on the windward bluff slopes and gully drainages. These freshwater features are proposed to have attracted humans and their hunting, shellfish processing, and plant gathering activities to upland localities, as now preserved above the island’s shelf platform that was submerged by the Holocene marine transgression.
Learning for Task-Oriented Grasping

Task-oriented grasping refers to the problem of computing stable grasps on objects that allow for a subsequent execution of a task. Although grasping objects in a task-oriented manner comes naturally to humans, it is still very challenging for robots. Take for example a service robot deployed in a household. Such a robot should be able to execute complex tasks that might include cutting a banana or ﬂipping a pancake. To do this, the robot needs to know what and how to grasp such that the task can be executed. There are several challenges when it comes to this. First, the robot needs to be able to select an appropriate object for the task. This pertains to the theory of af-fordances . Second, it needs to know how to place the hand such that the task can be executed, for example, grasping a knife on the handle when performing cutting. Finally, algorithms for task-oriented grasping should be scalable and have high generalizability over many object classes and tasks. This is challenging since there are no available datasets that contain information about mutual relations between objects, tasks and grasps. In this thesis, we present methods and algorithms for task-oriented grasping that rely on deep learning. We use deep learning to detect object af-fordances , predict task-oriented grasps on novel objects and to parse human activity datasets for the purpose of transferring this knowledge to a robot. For learning aﬀordances, we present a method for detecting functional parts given a visual observation of an object and a task. We utilize the detected aﬀor-dances together with other object properties to plan for stable, task-oriented grasps on novel objects. For task-oriented grasping, we present a system for predicting grasp scores that take into account both the task and the stability. The grasps are then executed on a real-robot and reﬁned via bayesian optimization. Finally, for parsing human activity datasets, we present an al-gorithm for estimating 3D hand and object poses and shapes from 2D images so that the information about the contacts and relative hand placement can be extracted. We demonstrate that we can use the information obtained in this manner to teach a robot task-oriented grasps by performing experiments with a real robot on a set of novel objects.
Discriminative Sample Selection for Statistical Machine Translation

Production of parallel training corpora for the development of statistical machine translation (SMT) systems for resource-poor languages usually requires extensive manual effort. Active sample selection aims to reduce the labor, time, and expense incurred in producing such resources, attaining a given performance benchmark with the smallest possible training corpus by choosing informative, nonredundant source sentences from an available candidate pool for manual translation. We present a novel, discriminative sample selection strategy that preferentially selects batches of candidate sentences with constructs that lead to erroneous translations on a held-out development set. The proposed strategy supports a built-in diversity mechanism that reduces redundancy in the selected batches. Simulation experiments on English-to-Pashto and Spanish-to-English translation tasks demonstrate the superiority of the proposed approach to a number of competing techniques, such as random selection, dissimilarity-based selection, as well as a recently proposed semi-supervised active learning strategy.
Characteristics of shock attenuation during fatigued running

The aim of this study was to examine shock attenuation before and after completing a maximal effort graded exercise test while running on a treadmill. Ten individuals ran before and after a maximal graded exercise test with running speed controlled between conditions. Transfer functions were calculated using surface-mounted accelerometers to represent shock attenuation. An accelerometer was mounted on the distal aspect of the tibia and another on the anterior aspect of the forehead. Ten strides were analysed in each condition for all participants. Paired t-tests were used to compare each dependent variable (shock attenuation, stride length, rate of oxygen consumption) between conditions (running before vs after the exercise test). Oxygen consumption was 16% greater when running after the graded exercise test (47.9±5.0 ml · kg–1 · min–1; mean±s) than when running before it (41.1±2.7 ml · kg–1 · min–1) (P <0.05). Stride length was similar during running before (2.71±0.15 m) and after (2.75±0.17 m) the graded exercise test (P >0.05). Shock attenuation was, on average, 12% lower during running after (–9.8±2.6 dB) than before (–11.3±2.7 dB) the graded exercise test (P <0.05). We conclude that less shock was attenuated during fatigued than non-fatigued running and that only subtle changes in stride length were made while fatigued.
Personality patterns in patients with myotonic dystrophy.

BACKGROUND Myotonic dystrophy (DM) is a multisystemic disease. The central nervous system is affected by cognitive, affective, and personality disturbances. A characteristic behavior was noted from the first clinical descriptions, but no definitive conclusions have been drawn despite extensive debate. As DM is a genetic disease of well-defined abnormality, it may be a good model for understanding the relative contributions of nature and nurture in the building of personality traits. OBJECTIVE To investigate the possibility that there is a personality pattern that is characteristic of patients with DM. SUBJECTS AND METHODS The personalities of 15 adult (age range, 20-53 years) patients with DM with no, or minimal, muscle weakness were studied by means of the International Personality Disorder Examination. The results were compared with those from 14 matched healthy subjects (age range, 20-54 years) and 12 patients (age range, 22-50 years) with a mild form of facioscapulohumeral dystrophy. SETTING The Department of Neuromuscular Diseases, Hôpital de la Salpêtrière, Paris, France. RESULTS Patients with DM exhibited a homogeneous personality profile, with statistically significant differences (P<.005) for avoidant, obsessive-compulsive, passive-aggressive, and schizotypic traits. In both groups of controls, the personality profiles were extremely heterogeneous. Personality disorders (avoidant personality) were found in 4 of 15 patients with DM. CONCLUSIONS The findings of this study suggest that the personality pattern of patients with DM is related to their disease. Their personality disorders are not attributable to their adjustment to a disabling condition. By contrast, among the patients with DM, the high incidence of avoidant personality, a phenotype poorly represented in the general population, supports the idea that it is the expression of a primary phenomenon related to a genetic mutation.
A novel role for Cdc5p in DNA replication

DNA replication initiates from specific chromosomal sites called origins, and in the budding yeast Saccharomyces cerevisiae these sites are occupied by the origin recognition complex (ORC). Dbf4p is proposed to play a role in targeting the G1/S kinase Cdc7p to initiation complexes late in G1. We report that Dbf4p may also recruit Cdc5p to origin complexes. Cdc5p is a member of the Polo family of kinases that is required for the completion of mitosis. Cdc5p and Cdc7p each interact with a distinct domain of Dbf4p. cdc5-1 mutants have a plasmid maintenance defect that can be suppressed by the addition of multiple origins. cdc5-1 orc2-1 double mutants are synthetically lethal. Levels of Cdc5p were found to be cell cycle regulated and peaked in G2/M. These results suggest a role for Cdc5p and possibly Polo-like kinases at origin complexes.
A power-efficient reconfigurable architecture using PCM configuration technology

Promising advantages offered by resistive NonVolatile Memories (NVMs) have brought great attention to replace existing volatile memory technologies. While NVMs were primarily studied to be used in the memory hierarchy, they can also provide benefits in Field-Programmable Gate Arrays (FPGAs). One major limitation of employing NVMs in FPGAs is significant power and area overheads imposed by the Peripheral Circuitry (PC) of NVM configuration bits. In this paper, we investigate the applicability of different NVM technologies for configuration bits of FPGAs and propose a power-efficient reconfigurable architecture based on Phase Change Memory (PCM). The proposed PCM-based architecture has been evaluated using different technology nodes and it is compared to the SRAM-based FPGA architecture. Power and Power Delay Product (PDP) estimations of the proposed architecture show up to 37.7% and 35.7% improvements over SRAM-based FPGAs, respectively, with less than 3.2% performance overhead.
Cognitive multiple-antenna network in outage-restricted primary system

In the commons model for the spectrum sharing, cognitive users can access the spectrum as long as the target performance in the legitimate primary system is not violated. In this paper, we consider a downlink primary multiple-input-single-output (MISO) system which operates under a controlled interference from the downlink MISO cognitive radio, also called secondary system. We derive exact expressions for outage probability of the primary user under Rayleigh fading, when the primary system is exposed to interference from a secondary base station. Moreover, in high-SNR scenario, a closed-form asymptotic formula for the outage probability is derived, which shows that the primary receiver achieves full spatial diversity under given interference from the secondary user. Next, the optimum transmit power in the secondary system is investigated for maximizing the ergodic capacity when there is an outage constraint at the primary system, and a simple solution is proposed. Finally, the analytical results are confirmed by simulations, in which we analyze the impact of different parameters, such as the number of antennas and the amount of the interference on the system performance; these could be used as system design guidelines.
Boosting Stochastic Newton with Entropy Constraint for Large-Scale Image Classification

Large scale image classification requires efficient scalable learning methods with linear complexity in the number of samples. Although Stochastic Gradient Descent is an efficient alternative to classical Support Vector Machine, this method suffers from slow convergence. In this paper, our contribution is two folds. First we consider the minimization of specific calibrated losses, for which we show how to reliably estimate posteriors, binary entropy and margin. Secondly we propose a Boosting Stochastic Newton Descent (BSN) method for minimization in the primal space of these specific calibrated loss. BSN approximates the inverse Hessian by the best low-rank approximation. The originality of BSN relies on the fact that it does perform a boosting scheme without computing iterative weight update over the examples. We validate BSN by benchmarking it against several variants of the state-of-the-art SGD algorithm on the large scale Image Net dataset. The results on Image Net large scale image classification display that BSN improves significantly accuracy of the SGD baseline while being faster by orders of magnitude.
TBIdoc: 3D content-based CT image retrieval system for traumatic brain injury

Traumatic brain injury (TBI) is a major cause of death and disability. Computed Tomography (CT) scan is widely used in the diagnosis of TBI. Nowadays, large amount of TBI CT data is stacked in the hospital radiology department. Such data and the associated patient information contain valuable information for clinical diagnosis and outcome prediction. However, current hospital database system does not provide an efficient and intuitive tool for doctors to search out cases relevant to the current study case. In this paper, we present the TBIdoc system: a content-based image retrieval (CBIR) system which works on the TBI CT images. In this web-based system, user can query by uploading CT image slices from one study, retrieval result is a list of TBI cases ranked according to their 3D visual similarity to the query case. Specifically, cases of TBI CT images often present diffuse or focal lesions. In TBIdoc system, these pathological image features are represented as bin-based binary feature vectors. We use the Jaccard-Needham measure as the similarity measurement. Based on these, we propose a 3D similarity measure for computing the similarity score between two series of CT slices. nDCG is used to evaluate the system performance, which shows the system produces satisfactory retrieval results. The system is expected to improve the current hospital data management in TBI and to give better support for the clinical decision-making process. It may also contribute to the computer-aided education in TBI.
Optimized Method for Preparation of DNA from Pathogenic and Environmental Mycobacteria

ABSTRACT Genomic studies on pathogenic and environmental mycobacteria are of growing interest for understanding of their evolution, distribution, adaptation, and host-pathogen interaction. Since most mycobacteria are slow growers, material from in vitro cultures is usually scarce. The robust mycobacterial cell wall hinders both experimental cell lysis and efficient DNA extraction. Here, we compare elements of several DNA preparation protocols and describe a method that is economical and practical and reliably yields large amounts—usually 10-fold increased compared to earlier protocols—of highly pure genomic DNA for sophisticated downstream applications. This method was optimized for cultures of a variety of pathogenic and environmental mycobacterial species and proven to be suitable for direct mycobacterial DNA extraction from infected insect specimens.
An analytical model for progressive mesh streaming

3D triangular mesh is becoming an increasingly important data type for networked applications such as digital museums, online games, and virtual worlds. In these applications, a multi-resolution representation is typically desired for streaming large 3D meshes, allowing for incremental rendering at the viewers while data is still being transmitted. Such progressive coding, however, introduces dependencies between data. This paper quantitatively analyzes the effects of such dependency on the intermediate decoded mesh quality when the progressive mesh is transmitted over a lossy network, by modeling the distribution of decoding time as a function of mesh properties and network parameters. To illustrate the usefulness of our analytical model, we describe three of its applications. First, we show how it can be used to analytically compute the expected decoded mesh quality. Second, we study two extreme cases of dependency in progressive mesh and show that the effect of dependencies on decoded mesh quality diminishes with time. Finally, based on the model, we propose a packetization strategy that improves the decoded mesh quality during the initial stage of streaming.
Learning mixed graphical models from data with p larger than n

Structure learning of Gaussian graphical models is an extensively studied problem in the classical multivariate setting where the sample size n is larger than the number of random variables p, as well as in the more challenging setting when p>>n. However, analogous approaches for learning the structure of graphical models with mixed discrete and continuous variables when p>>n remain largely unexplored. Here we describe a statistical learning procedure for this problem based on limited-order correlations and assess its performance with synthetic and real data.
SideBySide: multi-user gestural interaction with handheld projectors

SideBySide is a system designed for ad-hoc multi-user interaction with handheld projectors. SideBySide does not require instrumentation of the environment and can be used almost anywhere. This paper examines the diverse ways that children interact with SideBySide when playing interactive games. Observations from a preliminary user study are presented along with quantitative evaluation of the SideBySide tracking approach.
Spatio-Temporal Analysis of Sea Surface Temperature in the East China Sea Using TERRA/MODIS Products Data

Sea surface temperature (SST) is an important parameter in determining the atmospheric and oceanic circulations, and satellite thermal infrared remote sensing can obtain the SST with very high spatio-temporal resolutions. The study first validated the accuracy of TERRA MODIS SST daytime and nighttime products with the timing SST measurements from the ships in the East China Sea (ECS) in February, May, August and November, 2001, and then the daily variation of daytime and nighttime SST difference was analyzed. Using 16-year MODIS SST monthly products data from February 2000 to January 2016, when all SST monthly products in February, May, August and November were averaged respectively, the seasonal spatial distribution pattern of SST in the ECS was discovered. After monthly sea surface temperature anomaly was finally processed by the empirical orthogonal function (EOF), the interannual variability of SST in the ECS was discussed. The results show that the MODIS SST daily products have a good accuracy with a mean absolute percentage error (MAPE) below 5%. The SST difference between day and night is the largest in winter, followed by spring, then for autumn and the smallest in summer, while the diurnal SST difference is very low for the same season in the different seas. The SST in the ECS displays the obvious seasonal spatial distribution pattern, in which the SST of winter is gradually increasing from north to south, while local temperature difference is the largest for 26.5°C in a year. In comparison, the SST in summer tends uniform and the difference is not more than 5°C in the whole sea. From the EOF analysis of SST anomaly, the interannual variability of SST in the ECS is affected by the East Asian monsoon, the latitudinal difference of solar radiation, the offshore circulation and the submarine terrain.
Unifying Within- and Between-Generation Bet-Hedging Theories: An Ode to J. H. Gillespie

In the 1970s, John Gillespie introduced two principles in which evolution selects for genotypes with lower variation in offspring numbers. First, if the variation in offspring number primarily occurs within generations, the strength of this selective force is inversely proportional to population size. Second, if this variation primarily occurs between generations, the strength of this selective force is proportional to the variance and independent of population size. These principles lie at the core of bet-hedging theory. Using the common currency of fixation probabilities, I derive a general principle for which within-generation correlation of individual fitness acts as a dial between Gillespie’s limiting cases. At low correlations, within-generation variation is the primary selective force. At high correlations, between-generation variation is the dominant selective force. As corollary of this general principle, selection for diversified bet-hedging strategies is shown to require higher within-generation environmental correlations in smaller populations.