Miracle-dz/UniFAR-FTUs huggingface dataset:
FRIGHT: A flexible rule-based intelligent ghost team for Ms. Pac-Man

FRIGHT is a rule-based intelligent agent for playing the ghost team in the Ms. Pac-Man vs Ghosts Competition held at the 2012 IEEE Conference on Computational Intelligence and Games. FRIGHT uses rule sets with high-level abstractions of the game state and actions, and employs evolutionary computation to learn rule sets; a distributed homogenous-agent approach is used. We compare the performance of a hand-coded rule set to one learned by the system and find that the rule set learned by the system outperforms the hand-coded rules.
Peripheral artery disease: potential role of ACE-inhibitor therapy

Subjects with peripheral arterial disease (PAD) of the lower limbs are at high risk for cardiovascular and cerebrovascular events and the prevalence of coronary artery disease in such patients is elevated. Recent studies have shown that regular use of cardiovascular medications, such as therapeutic and preventive agents for PAD patients, seems to be promising in reducing long-term mortality and morbidity. The angiotensin-converting-enzyme (ACE) system plays an important role in the pathogenesis and progression of atherosclerosis, and ACE-inhibitors (ACE-I) seem to have vasculoprotective and antiproliferative effects as well as a direct anti-atherogenic effect. ACE-I also promote the degradation of bradykinin and the release of nitric oxide, a potent vasodilator; further, thay have shown important implications for vascular oxidative stress. Other studies have suggested that ACE-I may also improve endothelial dysfunction. ACE-I are useful for reducing the risk of cardiovascular events in clinical and subclinical PAD. Particularly, one agent of the class (ie, ramipril) has shown in many studies to able to significantly reduce cardiovascular morbidity and mortality in patients with PAD.
Regular exercise coupled to diet regimen accelerates reduction of hepatic steatosis and associated pathological conditions in nonalcoholic fatty liver disease.

BACKGROUND A diet regimen focusing on weight loss is still the most efficient treatment for nonalcoholic fatty liver disease (NAFLD). Recently, specific benefits of exercise against NAFLD independent of weight loss have been reported. Hence, combining exercise with diet-induced weight loss can be expected to have an additive benefit for NAFLD management. We evaluated the effectiveness of diet in conjunction with exercise (DE) compared with that of diet alone (D) on hepatic steatosis and its underlying pathophysiology. METHODS Data obtained from 72 obese, middle-aged men with NAFLD who completed a 3-month program of DE or D in 2011 and 2012 were analyzed. Subjects went through a comprehensive parameters analysis for the pathophysiology of NAFLD. RESULTS Subjects in the DE group, compared with those in the D group, elicited additive effects on the degree of hepatic steatosis (-82.6% vs. -60.0%) and body weight (-13.3% vs. -8.9%) accompanied by an improvement in serum marker levels: inflammation, ferritin (-16.1% vs. -2.1%); oxidative stress, lipid peroxidation (-31.8% vs. +4.8%); adipokine imbalance, adiponectin, and leptin (+27.4% vs. +2.6% and -74.4% vs. -30.2%). Consequently, subjects in the DE group achieved further attenuation of insulin resistance [homeostatsis model assessment of insulin resistance (HOMA-IR) (-63.6% vs. -40.0%)]. These observed additive benefits in the DE group were closely associated with the increased volume of physical activity. CONCLUSIONS The addition of exercise to a diet regimen potentiates the benefits in NAFLD management through further improvement of hepatic steatosis, inflammatory and oxidative stress levels, and adipokine imbalance, thereby attenuating insulin resistance independent of detectable weight loss.
Enabling Scalable Social Group Analytics via Hypergraph Analysis Systems

With the rapid growth of large online social networks, the ability to analyze large-scale social structure and behavior has become critically important, and this has led to the development of several scalable graph processing systems. In reality, social interaction takes place not just between pairs of individuals as in the common graph model, but rather in the context of multi-user groups. Research has shown that such group dynamics can be better modeled through hypergraphs: a generalization of graphs. There are not yet, however, scalable systems to support hypergraph computation, and several challenges and opportunities arise in their design and implementation. In this paper, we present an initial attempt at building a scalable hypergraph analysis framework based on the GraphX/Spark framework. We use this prototype to examine several programmability and implementation issues through experiments with two real-world datasets on a 6-node cluster.
Acoustic parameters optimised for recognition of phonetic features

Speaker variability is a major problem in today’s state-of-the-art speech recognition systems. Parameterisation of speech in terms of Acoustic Parameters (APs) motivated by phonetic feature theory has shown to be more robustness to speaker variability as compared to cepstral coefficients when tested on the task of broad-class recognition [1]. Also APs has been successfully applied for identification of semivowels [2,3]. The aim of the present study is to investigate the use of APs for phoneme recognition. An extended set of features is used to distinguish between all phonemes in the TIMIT database and APs related to the extended feature set are found in literature. A separability measure is calculated to investigate the importance of the suggested APs for the separation of phonemes and feature classes. Results show that the APs that are the most important for separation of classes of phonetic features are also the most important for separation of phonemes classes. This indicates that phonemes can be recognised on the basis of phonetic features captured by the use of APs. However much work still needs to be done to understand and reliably extract all of the acoustic correlates of the phonetic features applied.
Long‐Term Retention Rates of Lamotrigine, Gabapentin, and Topiramate in Chronic Epilepsy

PURPOSE We sought to determine the long-term retention rates of lamotrigine (LTG), gabapentin (GBP), and topiramate (TPM) therapy for patients at a tertiary referral clinic for chronic, refractory epilepsy. METHODS We analyzed 424 consecutive patients with chronic, refractory partial and/or generalized epilepsy who were started on LTG, 158 patients who were started on GBP, and 393 patients who were started on TPM. The percentages of patients who continued therapy with LTG, GBP, and TPM were estimated with the use of Kaplan-Meier survival analysis. Factors that influence retention were analyzed with the use of Cox regression analysis. RESULTS Kaplan-Meier survival analysis showed that at 3 years, 30% continued therapy on TPM compared with 29% on LTG and fewer than 10% on GBP. Adverse events resulted in therapy withdrawal in 40% of patients on TPM compared with GBP (37%) and LTG (22%). Perceived lack of efficacy led to treatment withdrawal in 39% of patients on GBP compared with 34% on LTG and 19% on TPM. Cox regression estimated that a fourth or fewer of patients with chronic partial epilepsy are likely to continue therapy with a new antiepileptic drug beyond 5 years. CONCLUSIONS The impact of these new antiepileptic drugs on the long-term course of chronic partial epilepsy is likely to be small, as approximately three of four patients will discontinue therapy. More patients appear to continue on TPM compared with LTG or GBP, with a possible reason being better perceived efficacy of TPM, despite having the highest incidence of adverse events.
A 1.35GHz all-digital fractional-N PLL with adaptive loop gain controller and fractional divider

A 1.35GHz all-digital phase-locked loop (ADPLL) with an adaptively controlled loop filter and a 1/3rd-resolution fractional divider is presented. The adaptive loop gain controller (ALGC) effectively reduces the nonlinear characteristics of the bang-bang phase-frequency detector (BBPFD). The fractional divider partially compensates for the input phase error which is caused by the fractional-N frequency synthesis operation. A prototype ADPLL using a BBPFD with a dead zone free retimer, an ALGC, and a fractional divider is fabricated in 0. 13μm CMOS. The core occupies 0.19mm2 and consumes 13.7mW from a 1.2V supply. The measured RMS jitter was 4.17ps at a 1.35GHz clock output.
Using sequence classification for filtering web pages

Web pages often contain text that is irrelevant to their main content, such as advertisements, generic format elements, and references to other pages on the same site. When used by automatic content-processing systems, e.g., for Web indexing, text classification, or information extraction, this irrelevant text often produces substantial amount of noise. This paper describes a trainable filtering system based on a feature-rich sequence classifier that removes irrelevant parts from pages, while keeping the content intact. Most of the features the system uses are purely form-related: HTML tags and their positions, sizes of elements, etc. This keeps the system general and domain-independent. We also experiment with content words and show that while they perform very poorly alone, they can slightly improve the performance of pure-form features, without jeopardizing the domain-independence. Our system achieves very high accuracy (95% and above) on several collections of Web pages. We also do a series of tests with different features and different classifiers, comparing the contribution of different components to the system performance, and comparing two known sequence classifiers, Robust Risk Minimization (RRM) and Conditional Random Fields (CRF), in a novel setting.
Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation

Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors.
Kashvi: Process Mining Software Repositories

Software Process Intelligence (SPI) is an emerging and evolving discipline involving mining and analysis of software processes. This is modeled on the lines of Business Process Intelligence (BPI), but with the focus on software processes and its applicability in software systems. Process mining consists of mining event log and process trace data for the purpose of process discovery (run-time process model), process verification or compliance checking (comparison between design-time and run-time process model), process enhancement and recommendation. Software Process Mining or Intelligence is a new and emerging discipline which falls at the intersection of Software Process & Mining, and Software & Process Mining. Software Process Mining is integral to discovering and verifying the processes in a software system. Software Process Mining is a three word phrase which can be viewed from two perspectives: Software + Process Mining and Software Process + Mining. Software development and evolution involves usage of several workflow management and information systems and tools such as Issue Tracking Systems (ITS), Version Control Systems (VCS), Peer Code Review Systems (PCR) and Continuous Integration Tools (CIT). Such information systems log data consisting of events, activities, time-stamp, user or actor and context specific data. Such events or trace data generated by information systems used during software construction (as part of the software development process) contains valuable information which can be mined for gaining useful insights and actionable information. In this paper, we present Kashvi: A Framework for Software Process Intelligence
Low-Overhead Architecture for Security Tag

A security-tagged architecture is one that applies tags on data to detect attack or information leakage, tracking data flow.The previous studies using security-tagged architecture mostly focused on how to utilize tags, not how the tags are implemented. A naive implementation of tags simply adds a tag field to every byte of the cache and the memory. Such technique, however, results in a huge hardware overhead.This paper proposes a low-overhead tagged architecture. We achieve our goal by exploiting some properties of tag, the non-uniformity and the locality of reference. Our design includes a use of uniquely designed multi-level table and various cache-like structures, all contributing to exploit these properties. Under simulation, our method was able to limit the memory overhead to 1.8%, where a naive implementation suffered 12.5% overhead.
Temporal causal inference with stochastic audiovisual sequences

Integration of sensory information across multiple senses is most likely to occur when signals are spatiotemporally coupled. Yet, recent research on audiovisual rate discrimination indicates that random sequences of light flashes and auditory clicks are integrated optimally regardless of temporal correlation. This may be due to 1) temporal averaging rendering temporal cues less effective; 2) difficulty extracting causal-inference cues from rapidly presented stimuli; or 3) task demands prompting integration without concern for the spatiotemporal relationship between the signals. We conducted a rate-discrimination task (Exp 1), using slower, more random sequences than previous studies, and a separate causal-judgement task (Exp 2). Unisensory and multisensory rate-discrimination thresholds were measured in Exp 1 to assess the effects of temporal correlation and spatial congruence on integration. The performance of most subjects was indistinguishable from optimal for spatiotemporally coupled stimuli, and generally sub-optimal in other conditions, suggesting observers used a multisensory mechanism that is sensitive to both temporal and spatial causal-inference cues. In Exp 2, subjects reported whether temporally uncorrelated (but spatially co-located) sequences were perceived as sharing a common source. A unified percept was affected by click-flash pattern similarity and the maximum temporal offset between individual clicks and flashes, but not on the proportion of synchronous click-flash pairs. A simulation analysis revealed that the stimulus-generation algorithms of previous studies is likely responsible for the observed integration of temporally independent sequences. By combining results from Exps 1 and 2, we found better rate-discrimination performance for sequences that are more likely to be integrated than those that are not. Our results support the principle that multisensory stimuli are optimally integrated when spatiotemporally coupled, and provide insight into the temporal features used for coupling in causal inference.
Hand tracking and segmentation via graph cuts and dynamic model in sign language videos

In this paper, we propose a new method for hands tracking and segmentation based on augmented graph cuts and dynamic model in sign language videos. We focus on resolving three problems which are fast hand motion capture, hand over face and hand occlusions. At first, an effective dynamic model for state prediction is used. This dynamic model can correctly predict the location of hand which has a rapid movement and quick shape deformation. Then, new energy terms are augmented into the energy function in graph cuts. The additional terms are inspired by multi cues, such as color, motion and spatial-temporal information. Finally, we construct the graph and achieve the hand segmentation in successive frames using min-cut/max-flow algorithm. We evaluate our algorithm in a real American Sign Language video from Purdue ASL Database. Besides, our method can be easily extended to track objects with similar color.
A Distributed Support Vector Machine Learning Over Wireless Sensor Networks

This paper is about fully-distributed support vector machine (SVM) learning over wireless sensor networks. With the concept of the geometric SVM, we propose to gossip the set of extreme points of the convex hull of local data set with neighboring nodes. It has the advantages of a simple communication mechanism and finite-time convergence to a common global solution. Furthermore, we analyze the scalability with respect to the amount of exchanged information and convergence time, with a specific emphasis on the small-world phenomenon. First, with the proposed naive convex hull algorithm, the message length remains bounded as the number of nodes increases. Second, by utilizing a small-world network, we have an opportunity to drastically improve the convergence performance with only a small increase in power consumption. These properties offer a great advantage when dealing with a large-scale network. Simulation and experimental results support the feasibility and effectiveness of the proposed gossip-based process and the analysis.
Knowledge-Based System for Port Placement and Robot Setup Optimization in Minimally Invasive Surgery

Abstract A new knowledge-based planning system is presented, that optimizes the positions of ports for the instruments and the endoscopic camera in manual and robot assisted laparoscopic surgery. For robot assisted interventions, the initial robot configuration of the da Vinci ® Surgical System is included in the optimization. Modeling of the patient, the intervention and the telemanipulator are described. The patient model is including the pneumoperitoneum. The intervention model is inferred from recorded trajectories of the surgical instruments. The optimization is performed with the Seed Throwing Optimization meta-heuristic. Criteria for optimization are reachability of target areas, collision avoidance and dexterity as well as ergonomic factors. These criteria are modeled as fuzzy sets. The optimized port positions are projected on the patient's abdomen with a new laser-based projection system. The error of the laser-based projection was measured. The advantages and disadvantages of the system are discussed and compared to an existing augmented reality system. First results show that the computed configuration leads to maximum access in the operation field in the narrow pelvis, maximum separation of the telemanipulator's arms and allows an ergonomic completion of the intervention.
Loss of Arf4 causes severe degeneration of the exocrine pancreas but not cystic kidney disease or retinal degeneration

Arf4 is proposed to be a critical regulator of membrane protein trafficking in early secretory pathway. More recently, Arf4 was also implicated in regulating ciliary trafficking, however, this has not been comprehensively tested in vivo. To directly address Arf4’s role in ciliary transport, we deleted Arf4 specifically in either rod photoreceptor cells, kidney, or globally during the early postnatal period. Arf4 deletion in photoreceptors did not cause protein mislocalization or retinal degeneration, as expected if Arf4 played a role in protein transport to the ciliary outer segment. Likewise, Arf4 deletion in kidney did not cause cystic disease, as expected if Arf4 were involved in general ciliary trafficking. In contrast, global Arf4 deletion in the early postnatal period resulted in growth restriction, severe pancreatic degeneration and early death. These findings are consistent with Arf4 playing a critical role in endomembrane trafficking, particularly in the pancreas, but not in ciliary function.
Realtime Storefront Logo Recognition

Storefront brand image (logo) recognition has many useful applications such as location recognition and advertisement. Various methods have been proposed using local and global feature matching. However, it continues to be a challenging area; unlike nature scene images, which are rich in texture details, brand images often lack texture variation, and therefore provide fewer feature points for matching. In addition, reference and test images may be acquired with different rotation, resolution, size, quality, and illumination parameters. In this paper, we propose a highly efficient and accurate approach for recognizing logos in storefront images. We separate our algorithm into two primary phases representation and recognition, each of which deals with a separate portion of the logo recognition classification algorithm. After training our classifier with 257 pre-segmented storefront logos, we were able to get an 86.0% classification accuracy on 100 test images taken from a wide variety
A Multi-Contrast, Multi-Resolution Phantom for Radionuclide Imaging Using a Single Activity Concentration Fill

The goal of the work is to design and prototype radionuclide phantom inserts constructed using a machinable porous material of water-like attenuation. The porous material enables multiple activity concentration `contrasts' while using a single concentration phantom fill. Further, features of multiple sizes are present within each of the contrasts. Available resolution phantoms for radionuclide imaging only allow for a single high-contrast measurement (1:0) and do not mimic typical patient imaging conditions that include a background activity. A hot rod phantom pattern was made using two porous materials and imaged alongside a non-porous equivalent inside a uniform cylinder. The phantom with inserts was imaged using [18F]-FDG on a PET/CT system. Using geometrically-defined image masks, mean values for the hot rod features and between-rod spaces were derived and the contrast recovery ratios compared for the non-porous and porous inserts. One of the prototype materials (EXP-1826) demonstrated reduced feature contrast ( ~ 40%) between the rods and the background. This material, with hydrophilic treatment, became fully saturated during phantom filling-simplifying the imaging process. Measurements of the rod contrast recovery in the various phantom sections demonstrated the phantom's utility for comparing and potentially optimizing image reconstruction parameter settings under varying imaging conditions at reduced contrast.