Miracle-dz/UniFAR-FTUs huggingface dataset:
Compression of Space for Low Visibility Probes

Stimuli briefly flashed just before a saccade are perceived closer to the saccade target, a phenomenon known as perisaccadic compression of space (Ross et al., 1997). More recently, we have demonstrated that brief probes are attracted towards a visual reference when followed by a mask, even in the absence of saccades (Zimmermann et al., 2014a). Here, we ask whether spatial compression depends on the transient disruptions of the visual input stream caused by either a mask or a saccade. Both of these degrade the probe visibility but we show that low probe visibility alone causes compression in the absence of any disruption. In a first experiment, we varied the regions of the screen covered by a transient mask, including areas where no stimulus was presented and a condition without masking. In all conditions, we adjusted probe contrast to make the probe equally hard to detect. Compression effects were found in all conditions. To obtain compression without a mask, the probe had to be presented at much lower contrasts than with masking. Comparing mislocalizations at different probe detection rates across masking, saccades and low contrast conditions without mask or saccade, Experiment 2 confirmed this observation and showed a strong influence of probe contrast on compression. Finally, in Experiment 3, we found that compression decreased as probe duration increased both for masks and saccades although here we did find some evidence that factors other than simply visibility as we measured it contribute to compression. Our experiments suggest that compression reflects how the visual system localizes weak targets in the context of highly visible stimuli.
BB-GC: basic-block level garbage collection

Memory space limitation is a serious problem for many embedded systems from diverse application domains. While circuit/packaging techniques are definitely important to squeeze large quantities of data/instruction into small size memories typically employed by embedded systems, software can also play a crucial role in reducing memory space demands of embedded applications. This paper focuses on a software-managed two-level memory hierarchy and instruction accesses. Our goal is to reduce on-chip memory requirements of a given application as much as possible, so that the memory space saved can be used by other simultaneously-executing applications. The proposed approach achieves this by tracking the lifetime of instructions. Specifically, when an instruction is dead (i.e. it could not be visited again in the rest of execution), we deallocate the on-chip memory space allocated to it. Working on the control flow graph representation of an embedded application, our approach performs basic block-level garbage collection for on-chip memories.
Improved Information Structure Analysis of Scientific Documents Through Discourse and Lexical Constraints

Inferring the information structure of scientific documents is useful for many downstream applications. Existing feature-based machine learning approaches to this task require substantial training data and suffer from limited performance. Our idea is to guide feature-based models with declarative domain knowledge encoded as posterior distribution constraints. We explore a rich set of discourse and lexical constraints which we incorporate through the Generalized Expectation (GE) criterion. Our constrained model improves the performance of existing fully and lightly supervised models. Even a fully unsupervised version of this model outperforms lightly supervised feature-based models, showing that our approach can be useful even when no labeled data is available.
Control in a 3D reconstruction system using selective perception

This paper presents a control structure for general purpose image understanding that addresses both the high level of uncertainty in local hypotheses and the computational complexity of image interpretation. The control of vision algorithms is performed by an independent subsystem that uses Bayesian networks and utility theory to compute the marginal value of information provided by alternative operators and selects the ones with the highest value. We have implemented and tested this control structure with several aerial image datasets. The results show that the knowledge base used by the system can be acquired using standard learning techniques and that the value-driven approach to the selection of vision algorithms leads to performance gains. Moreover, the modular system architecture simplifies the addition of both control knowledge and new vision algorithms.
Relighting human locomotion with flowed reflectance fields

We present an image-based approach for capturing the appearance of a walking or running person so they can be rendered realistically under variable viewpoint and illumination. In our approach, a person walks on a treadmill at a regular rate as a turntable slowly rotates the person's direction. As this happens, the person is filmed with a vertical array of high-speed cameras under a time-multiplexed lighting basis, acquiring a seven-dimensional dataset of the person under variable time, illumination, and viewing direction in approximately forty seconds. We process this data into a flowed reflectance field using an optical flow algorithm to correspond pixels in neighboring camera views and time samples to each other, and we use image compression to reduce the size of this data. We then use image-based relighting and a hardware-accelerated combination of view morphing and light field rendering to render the subject under user-specified viewpoint and lighting conditions. To composite the person into a scene, we use an alpha channel derived from back lighting and a retroreflective treadmill surface and a visual hull process to render the shadows the person would cast onto the ground. We demonstrate realistic composites of several subjects into real and virtual environments using our technique.
Asymptotics and Approximation of the SIR Distribution in General Cellular Networks

It has recently been observed that the SIR distributions of a variety of cellular network models and transmission techniques look very similar in shape. As a result, they are well approximated by a simple horizontal shift (or gain) of the distribution of the most tractable model, the Poisson point process (PPP). To study and explain this behavior, this paper focuses on general single-tier network models with nearest-base station association and studies the asymptotic gain both at 0 and at infinity. We show that the gain at 0 is determined by the so-called mean interference-to-signal ratio (MISR) between the PPP and the network model under consideration, while the gain at infinity is determined by the expected fading-to-interference ratio (EFIR). The analysis of the MISR is based on a novel type of point process, the so-called relative distance process, which is a one-dimensional point process on the unit interval [0,1] that fully determines the SIR. A comparison of the gains at 0 and infinity shows that the gain at 0 indeed provides an excellent approximation for the entire SIR distribution. Moreover, the gain is mostly a function of the network geometry and barely depends on the path loss exponent and the fading. The results are illustrated using several examples of repulsive point processes.
Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014

The article describes our system submitted to the SemEval-2014 task on Aspect-Based Sentiment Analysis. The methods based on distributed representations of words for the aspect term extraction and aspect term polarity detection tasks are presented. The methods for the aspect category detection and category polarity detection tasks are presented as well. Well-known skip-gram model for constructing the distributed representations is briefly described. The results of our methods are shown in comparison with the baseline and the best result.
On-road vehicle detection during dusk and at night

The video-based on-road detection of vehicles at daytime allows driver assistance systems to avoid collisions and thereby improve safety, and realize comfort functions, like the well known adaptive cruise control. However, at nighttime, common video sensor based vehicle detection algorithms can't be used, because most state-of-the-art features, like shadows, symmetry and others, cannot be measured. The on-road detection of vehicles at night is an obligatory feature for modern driver assistance systems, because those systems have to provide assistance functionality at day-time and at night-time, either. In this work, vehicles in front of the own car are recognized by detection of their front or rear lights, using a perspective blob filter and subsequently searching for corresponding light pairs. For preceding vehicles, the activity of the third break light is estimated, to distinguish the maneuver state of the vehicle. Experiments show the robustness of the approach during dusk and at night sequences.
Vector GA: a novel enhancement of genetic algorithms for efficient multi-variable or multi-dimensional search

Many software engineering problems can be viewed as a large multidimensional searching problem. This paper presents an enhancement of conventional Genetic Algorithms (GA) for more efficient multi-variable or multi-dimensional searches. The concept relies upon expressing chromosomes as vectors in the required multidimensional frame of reference. Usual GA operators are also defined as vector operators. Comparison with conventional genetic algorithm is made to illustrate its superior performance.
The Index System Construction of Province Initial Water Rights Allocation System Based on the Algorithm of Attribute Reduction

On the basis of the pre-research of index system of province initial water rights allocation system, a s well as the complexity and differences of the allocation system, the index system of province initial water rights allocation system in this paper is c omprehensively designed first, and the index framework divided into three layers of target, criteria and index angles is established. Taking advantages of the algorithm of attribute reduction based on Rough Sets theory in data processing and data screening, this paper constructs the index selection model of province initial water rights allocation system. Then the primary index is screened to determine the key evaluation index, with the algorithm of attribute reduction based on Rough Sets, finally forming the index system of province initial water rights allocation system.
Future trend of Si power device

As a FOM (Figure Of Merit), the power density of a power module has grown in an almost constant ratio for the past 20 years in the field of power electronics. This progress has been achieved by the joint development of power chips and a package. A combination of an IGBT (Insulated Gate Bipolor Transistor) and an FWD (Free Wheeling Diode) is the most frequent combination of power chips for a power module. The progress of both IGBT and diode is based not only upon the development of device structures but also upon the progress of the wafer process technology, which is derived from a LSI (Large Scale Integrated circuit) process and optimised for the power device. The package technology for power devices is uniquely developed for high reliability even for high power density application. This paper reports both the power chip and the package technologies up to the latest generation for the IGBT power module.
A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes

Genome wide association (GWA) studies, which test for association between common genetic markers and a disease phenotype, have shown varying degrees of success. While many factors could potentially confound GWA studies, we focus on the possibility that multiple, rare variants (RVs) may act in concert to influence disease etiology. Here, we describe an algorithm for RV analysis, RareCover. The algorithm combines a disparate collection of RVs with low effect and modest penetrance. Further, it does not require the rare variants be adjacent in location. Extensive simulations over a range of assumed penetrance and population attributable risk (PAR) values illustrate the power of our approach over other published methods, including the collapsing and weighted-collapsing strategies. To showcase the method, we apply RareCover to re-sequencing data from a cohort of 289 individuals at the extremes of Body Mass Index distribution (NCT00263042). Individual samples were re-sequenced at two genes, FAAH and MGLL, known to be involved in endocannabinoid metabolism (187Kbp for 148 obese and 150 controls). The RareCover analysis identifies exactly one significantly associated region in each gene, each about 5 Kbp in the upstream regulatory regions. The data suggests that the RVs help disrupt the expression of the two genes, leading to lowered metabolism of the corresponding cannabinoids. Overall, our results point to the power of including RVs in measuring genetic associations.
The Role of MKP-1 in the Anti-Proliferative Effects of Glucocorticoids in Primary Rat Pre-Osteoblasts

Glucocorticoid (GC)-induced osteoporosis has been attributed to a GC-induced suppression of pre-osteoblast proliferation. Our previous work identified a critical role for mitogen-activated protein kinase (MAPK) phosphatase-1 (MKP-1) in mediating the anti-proliferative effects of GCs in immortalized pre-osteoblasts, but we subsequently found that MKP-1 null mice were not protected against the pathological effects of GCs on bone. In order to reconcile this discrepancy, we have assessed the effects of GCs on proliferation, activation of the MAPK ERK1/2 and MKP-1 expression in primary adipose-derived stromal cells (ADSCs) and ADSC-derived pre-osteoblasts (ADSC-OBs). ADSCs were isolated by means of collagenase digestion from adipose tissue biopsies harvested from adult male Wistar rats. ADSC-OBs were prepared by treating ADSCs with osteoblast differentiation media for 7 days. The effects of increasing concentrations of the GC dexamethasone on basal and mitogen-stimulated cell proliferation were quantified by tritiated thymidine incorporation. ERK1/2 activity was measured by Western blotting, while MKP-1 expression was quantified on both RNA and protein levels, using semi-quantitative real-time PCR and Western blotting, respectively. GCs were strongly anti-proliferative in both naïve ADSCs and ADSC-OBs, but had very little effect on mitogen-induced ERK1/2 activation and did not upregulate MKP-1 protein expression. These findings suggest that the anti-proliferative effects of GCs in primary ADSCs and ADSC-OBs in vitro do not require the inhibition of ERK1/2 activation by MKP-1, which is consistent with our in vivo findings in MKP-1 null mice.
Bone-anchored Hearing Aids in Unilateral Inner Ear Deafness

In nine patients with unilateral deafness and normal hearing in the contralateral ear, measurements of sound localization and speech perception were obtained before intervention, with a conventional contralateral routing of sound (CROS) hearing aid and later with a bone-anchored hearing aid (BAHA) implanted in the deaf ear. Sound localization did not show any differences between the three conditions. Speech perception using short, everyday sentences showed a reduction in the head-shadow effect of 2 dB for both the conventional CROS hearing aid and the BAHA in comparison to the unaided condition. Patients' real-life experiences of the three conditions were evaluated using the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire. The results showed a significant benefit with the BAHA in situations involving background noise and reverberation and a reduced aversion to loud sounds in comparison to the unaided and conventional CROS conditions.
Performance study of a multithreaded superscalar microprocessor

This paper describes a technique for improving the performance of a superscalar processor through multithreading. The technique exploits the instruction-level parallelism available both inside each individual stream, and across streams. The former is exploited through out-of-order execution of instructions within a stream, and the latter through execution of instructions from different streams simultaneously. Aspects of multithreaded superscalar design, such as fetch policy, cache performance, instruction scheduling, and functional unit utilization are studied. We analyze performance based on the simulation of a superscalar architecture and show that it is possible to provide support for multiple streams with minimal extra hardware, yet achieving significant performance gain (20-55%) across a range of benchmarks.
Effects of heat stress on carbohydrate and lipid metabolism in growing pigs

Heat stress (HS) jeopardizes human and animal health and reduces animal agriculture productivity; however, its pathophysiology is not well understood. Study objectives were to evaluate the direct effects of HS on carbohydrate and lipid metabolism. Female pigs (57 ± 5 kg body weight) were subjected to two experimental periods. During period 1, all pigs remained in thermoneutral conditions (TN; 20°C) and were ad libitum fed. During period 2, pigs were exposed to: (1) constant HS conditions (32°C) and fed ad libitum (n = 7), or (2) TN conditions and pair‐fed (PFTN; n = 10) to minimize the confounding effects of dissimilar feed intake. All pigs received an intravenous glucose tolerance test (GTT) and an epinephrine challenge (EC) in period 1, and during the early and late phases of period 2. After 8 days of environmental exposure, all pigs were killed and tissue samples were collected. Despite a similar reduction in feed intake (39%), HS pigs tended to have decreased circulating nonesterified fatty acids (NEFA; 20%) and a blunted NEFA response (71%) to the EC compared to PFTN pigs. During early exposure, HS increased basal circulating C‐peptide (55%) and decreased the insulinogenic index (45%) in response to the GTT. Heat‐stressed pigs had a reduced T3 to T4 ratio (56%) and hepatic 5′‐deiodinase activity (58%). After 8 days, HS decreased or tended to decrease the expression of genes involved in oxidative phosphorylation in liver and skeletal muscle, and ATGL in adipose tissue. In summary, HS markedly alters both lipid and carbohydrate metabolism independently of nutrient intake.
An efficient algorithm for the entropy rate of a hidden Markov model with unambiguous symbols

We demonstrate an efficient formula to compute the entropy rate $H(\mu)$ of a hidden Markov process with $q$ output symbols where at least one symbol is unambiguously received. Using an approximation to $H(\mu)$ to the first $N$ terms we give a $O(Nq^3$) algorithm to compute the entropy rate of the hidden Markov model. We use the algorithm to estimate the entropy rate when the parameters of the hidden Markov model are unknown.In the case of $q =2$ the process is the output of the Z-channel and we use this fact to give bounds on the capacity of the Gilbert channel.
Rate-Per-Link Adaptation in Cooperative Wireless Networks with Multi-Rate Combining

Rate adaptation based on Signal-to-Noise Ratio (SNR) measurements is a common channel adaptation scheme to increase throughput in wireless communication systems. To use rate adaptation efficiently in cooperative wireless networks, an adaptation algorithm must consider multiple channels (source-destination, source-relays, and relays-destination) to select modulation and code rates that maximize throughput. In this paper we analyze the potential gains that combining cooperation with rate adaptation brings in three steps: (1) We derive the theoretical capacity bounds for ideal rate adaptation schemes for typical topologies. (2) We propose an offline heuristic for computing SNR thresholds aimed at reaching the derived bounds. (3) Using this heuristic, we compare rate adaptation for Maximal Ratio Combining (MRC), where links are equally adapted, with Soft-Bit MRC (SBMRC), where links are individually adapted. We find that adapting the rate per link is superior in terms of throughput.
Revising the Outputs of a Decision Tree with Expert Knowledge: Application to Intrusion Detection and Alert Correlation

Classifiers are well-known and efficient techniques used to predict the class of items descrided by a set of features. In many applications, it is important to take into account some extra knowledge in addition to the one encoded by the classifier. For example, in spam filtering which can be seen as a classification problem, it can make sense for a user to require that the spam filter predicts less than a given rate or number of spams. In this paper, we propose an approach allowing to combine expert knowledge with the results of a decision tree classifier. More precisely, we propose to revise the outputs of a decision tree in order to take into account the available expert knowledge. Our approach can be applied for any classifier where a probability distribution over the set of classes (or decisions) can be estimated from the output of the classification step. In this work, we analyze the advantage of adding expert knowledge to decision tree classifiers in the context of intrusion detection and alert correlation. In particular, we study how additional expert knowledge such as "it is expected that 80% of traffic will be normal" can be integrated in classification tasks. Our aim is to revise classifiers' outputs in order to fit the expert knowledge. Experimental studies on intrusion detection and alert correlation problems show that our approach improves the performances on different benchmarks.
Trajectory classification based on machine-learning techniques over tracking data

This work addresses the application of a machine-learning approach to classify ATC trajectory segments from recorded opportunity traffic. It is based on the mode probabilities estimated by an IMM tracking filter operating forward and backward over available data. A learning algorithm creates a rule base for classification from these data, once they have been properly prepared. Performance of this data-driven classification system is compared with a more conventional approach based on transition detection on simulated and real data of representative situations. The offline processing of real data allows an accurate classification of manoeuvring segments, with the possibility of synthesizing ground truth lines for performance evaluation
Topological semimetal in honeycomb lattice LnSI

Significance We show that fruitful topological states can be realized in a special honeycomb lattice model. Guided by this model, we find that GdSI, the long perceived ideal Weyl semimetal, has only two pairs of Weyl nodes and that LuSI (YSI) is a topological insulator with the right-handed helical surface states. Our work provides a mechanism to study topological semimetals and proposes a platform for exploring the physics of Weyl semimetals as well as related device designs. Recognized as elementary particles in the standard model, Weyl fermions in condensed matter have received growing attention. However, most of the previously reported Weyl semimetals exhibit rather complicated electronic structures that, in turn, may have raised questions regarding the underlying physics. Here, we report promising topological phases that can be realized in specific honeycomb lattices, including ideal Weyl semimetal structures, 3D strong topological insulators, and nodal-line semimetal configurations. In particular, we highlight a semimetal featuring both Weyl nodes and nodal lines. Guided by this model, we showed that GdSI, the long-perceived ideal Weyl semimetal, has two pairs of Weyl nodes residing at the Fermi level and that LuSI (YSI) is a 3D strong topological insulator with the right-handed helical surface states. Our work provides a mechanism to study topological semimetals and proposes a platform for exploring the physics of Weyl semimetals as well as related device designs.