Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Inference Using DC Programming For Collective Graphical Models

Collective graphical models (CGMs) provide a framework for reasoning about a population of independent and identically distributed individuals when only noisy and aggregate observations are given. Previous approaches for inference in CGMs work on a junction-tree representation, thereby highly limiting their scalability. To remedy this, we show how the Bethe entropy approximation naturally arises for the inference problem in CGMs. We reformulate the resulting optimization problem as a difference-of-convex functions program that can capture different types of CGM noise models. Using the concave-convex procedure, we then develop a scalable messagepassing algorithm. Empirically, we show our approach is highly scalable and accurate for large graphs, more than an orderof-magnitude faster than a generic optimization solver, and is guaranteed to converge unlike the previous message-passing approach NLBP that fails in several loopy graphs.
Fully Automated 24/7 Behavioral Screening for Mutations in Targeted Cognitive Mechanisms in the Mouse

Current screens for memory defects in mutant mice, such as the Morris water maze, generally proceed on the assumption that memory impairment will be manifest in an altered rate of learning. The rate of learning in mutant and wild-type strains is estimated by plotting a group average measure of performance--for example, the mean latency to find the platform--as a function of trials. A problem with this approach is that the gradual approach to asymptote seen in groupaverage plots is an artifact of the averaging [1-3]. Thus, the measured quantity, the learning rate, does not reflect a meaningful quantity within the individual subjects. A second problem is that the trial-by-trial handling of the mice is stressful to them and wasteful of the experimenter’s time. The results strongly reflect response to handling stress, and the paradigms cannot be scaled up to allow for large scale screening. We believe there is a large latent demand for the large-scale behavioral screening of mutant mice for heritable malfunctions in the mechanisms of cognition. Seymour Benzer and his students, in their seminal use of genetics to get at the molecular biology of the circadian clock [4] have shown
Fast word-level power models for synthesis of FPGA-based arithmetic

This paper presents power models for multiplication and addition components on FPGAs which can be used at a high-level design description stage to estimate their logic and intra-component routing power consumption. The models presented are parameterized by the word-length of the component and the word-level statistics of its input signals. A key feature of these power models is the ability to handle both zero mean and non-zero mean signals. A method for measuring intra-component routing power consumption is presented, enabling the power models to account for both logic and routing power in components. The resulting models are equations which can be used to estimate the power consumed in an arithmetic component in a fraction of a second at the pre-placement stage of the design flow. The models have a mean relative error of 7.2% compared to bit-level power simulation of the placed-and-routed design
Optimal protocols for Hamiltonian and Schrödinger dynamics

For systems in an externally controllable time dependent potential, the optimal protocol minimizes the mean work spent in a finite time transition between given initial and final values of a control parameter. For an initially thermalized ensemble, we consider both Hamiltonian evolution for classical systems and Schrödinger evolution for quantum systems. In both cases, we show that for harmonic potentials, the optimal work is given by the adiabatic work even in the limit of short transition times. This result is counter-intuitive because the adiabatic work is substantially smaller than the work for an instantaneous jump. We also perform numerical calculations for the optimal protocol for Hamiltonian dynamics in an anharmonic quartic potential. For a two-level spin system, we give examples where the adiabatic work can be reached in either a finite or an arbitrarily short transition time depending on the allowed parameter space.
Social language processing: A framework for analyzing the communication of terrorists and authoritarian regimes

Social Language Processing (SLP) is introduced as an interdisciplinary approach to assess social features in communications by terrorist organizations and authoritarian regimes. The SLP paradigm represents a rapprochement of theories, tools and techniques from cognitive science, communications, computational linguistics, discourse processing, language studies and social psychology. The SLP paradigm consists of three broad stages: (1) linguistic feature identification; (2) linguistic feature extraction; and (3) classifier development. In this paper, we detail the SLP paradigm and review several linguistic features that are especially amenable to uncovering the social dynamics of groups that are difficult to assess directly (i.e. through questionnaires, interviews or direct observation). We demonstrate the application of SLP to identify status, cohesion and deception in the case of Saddam Hussein’s regime. Specifically, we analyzed the memoranda, letters and public communiqués distributed within and from Saddam Hussein’s administration in a recently recovered corpus called the Iraqi Perspectives Project, along with several related projects. We conclude with a discussion of the challenges that SLP faces for assessing social features across cultures in public and captured communications of terrorists and political regimes, along with responses to these organizations.
The object oriented document model of a meta system for existing digital libraries

We describe an object oriented document model of a meta system for existing digital library systems. We show how we solve heterogenity of existing systems in the context of linking and annotating existing digital library documents with a slim and simple approach. Our approach is already implemented in the system OMNIS/2, which is an advanced meta system for existing digital library systems and enhances existing digital library systems or retrieval systems by additional storing and indexing of user-defined multimedia documents, automatic and personal linking concepts, annotations, filtering and personalisation.
Hemodynamic Features in Stenosed Coronary Arteries: CFD Analysis Based on Histological Images

Histological images from the longitudinal section of four diseased coronary arteries were used, for the first time, to study the pulsatile blood flow distribution within the lumen of the arteries by means of computational fluid dynamics (CFD). Results indicate a strong dependence of the hemodynamics on the morphology of atherosclerotic lesion. Distinctive flow patterns appear in different stenosed regions corresponding to the specific geometry of any artery. Results show that the stenosis affects the wall shear stress (WSS) locally along the diseased arterial wall as well as other adjacent walls. The maximum magnitude of WSS is observed in the throat of stenosis. Moreover, high oscillatory shear index (OSI) is observed along the stenosed wall and the high curvature regions. The present study is capable of providing information on the shear environment in the longitudinal section of the diseased coronary arteries, based on the models created from histological images. The computational method may be used as an effective way to predict plaque forming regions in healthy arterial walls.
Deep learning for tumour classification in homogeneous breast tissue in medical microwave imaging

Deep learning has become the state-of-the-art in the area of biomedical imaging, leading to a large boost in performance that approaches human levels. Medical microwave imaging is an emerging technology that has great potential especially in the area of breast cancer diagnosis. Moreover, the obtained backscatter signals have also been shown to be a good basis for differentiating malignant and benign tumour type. We further analyse these results by applying deep learning methods to a dataset of Finite Difference Time Domain (FDTD) numerical simulations of tumour models embedded in homogeneous breast adipose tissue. Specifically we use Deep and Convolutional Neural Networks and obtain an accuracy of 93.44% which outperforms conventional machine learning previously used on the analysed dataset.
FEASIBILITY OF USING DIGITAL PHOTOGRAPHY FOR ENVIRONMENTAL MONITORING OF ANIMALS IN AN ARTIFICIAL REEF

Both acoustic and optical methods can be used for environmental monitoring of marine ranches. However, acoustic methods are more commonly used for underwater environmental surveillance. This paper presents the results of a study of the feasibility of using digital photography for monitoring the animals in an artificial reef. Animal monitoring studies enable us to characterize the behavior of aquatic animals and to derive parameters essential to further studies, such as fork length, swimming speed, and the relative orientation of fish, in addition to providing a quantitative acoustic assessment. A charge-coupled device (CCD) camera was calibrated in a laboratory tank by direct linear transformation (DLT), and then the accuracy of the three-dimensional (3D) reconstruction was tested. Grass carp (Ctenopharyngodon idella) and common carp (Cyprinus carpio) were studied to determine the parameters of fish behavior. The mean absolute deviations in x, y, and z directions were 2.0 mm, 2.2 mm, and 2.2 mm, respectively, with maximum values of 3.9 mm, 4.3 mm, and 3.5 mm, respectively. The error in estimation of fork length was about 1 cm. Our results indicated that it is possible to use digital photography for optical monitoring of a reef.
Presence and Reality Judgment in Virtual Environments: A Unitary Construct?

Presence and reality judgment are two important variables to take into account in the virtual reality field. So far, scientific literature has paid (and pays) attention to the construct of presence, trying to offer assessment measures that could seize such an elusive concept. However, the concept of reality judgment has received less attention, and, frequently, it has been subsumed into the concept of presence. Not much effort has been dedicated to test whether or not both constructs refer to the same domain. Most likely there are relationships between both variables, but it is also possible that they have differentiated domains. The aim of the present work is to design a self-report measure that assesses both constructs, and to carry out the validation process with Spanish and North American samples.
The Effect of Maternal Parenting Style and Sensitivity on Infant Development

A total of 31 mothers of 5-month-old infants(18 boys and 13 girls) were observed and interviewed at home via a questionnaire format. Results revealed that perceived maternal parenting style had an effect on general infant general development. As mother exhibited more didactic and limit setting behaviors, the infants showed more receptive, expressive, and language development characteristics. Furthermore, the effects of maternal sensitivity on infant general development and language skills were also ascertained. The infants who showed more positive general development had mothers who interacted sensitively with them. Maternal sensitivity was the only factor that had a major effect on infant general development. The overall findings of this study indicate that perceived maternal parenting style and observed maternal sensitivity were found to have positive effects on general infant general development and language skills.
A distributed range query framework for the internet of things

The range query referring to the Internet of Things is a tough challenge since the data information is fully distributed. In order to support efficient range query, most existing approaches focused on designing the data management mechanism, which ensures that the similar data are stored nearby within the network. However, it will introduce large extra overhead to each peer in the Internet of Things especially when the peers generate data frequently. In this paper, a distributed range query framework is proposed, which consists of three core modules, reporting and indexing module, along with a query executor. The reporting module learns the sensed data and predicts a data range in which the coming future data is likely to be. Only the abnormal data that exceeds the data range will be detected, which greatly reduces the frequency and quantity of data migration in these data management mechanism. The indexing module is responsible for collecting reported data information and establishing data index used for responding to query request. Based on the above two modules, the range query is processed by the query executor. The experimental results show that this proposal could support range query effectively and efficiently, with load balance among the peers at the same time.
Ranking in multi label classification of text documents using quantifiers

In today's world, many real world examples are based on multi label classification. A single document may belong to a set of class labels simultaneously. The process of ranking i.e. strict ordering of class labels is of great concern here. We have used the concept of quantifiers for ranking of class labels. We have proposed eight new quantifiers, which calculate the degree of membership of class labels of a particular text document. As a result, we are able to perform ranking of class labels in multi label learning. The proposed approach is shown with the help of a case study.
A Faithful Integration of Description Logics with Logic Programming

Integrating description logics (DL) and logic programming (LP) would produce a very powerful and useful formalism. However, DLs and LP are based on quite different principles, so achieving a seamless integration is not trivial. In this paper, we introduce hybrid MKNF knowledge bases that faithfully integrate DLs with LP using the logic of Minimal Knowledge and Negation as Failure (MKNF) [Lifschitz, 1991]. We also give reasoning algorithms and tight data complexity bounds for several interesting fragments of our logic.
A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation

The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF). The proposed method utilizes four different features: RR interval irregularity, absence of P waves, presence of f-waves and noise level. The obtained features are applied to the Mamdani-type fuzzy inference method for decision-making. The performance was evaluated on one hundred 90 s long surrogate ECG signals with brief PAF episodes (5-30 beats). The robustness to noise in ECGs where noise level in each set is incremented in steps of 0.01 mV from 0 to 0.2 mV was examined as well. When compared to the coefficient of sample entropy, our method showed considerably better performance for low and moderate noise levels (<; 0.06 mV) with an area under the receiver operating characteristic curve of 0.9 and 0.94, respectively. Similar performance is expected for higher noise levels as atrial activity is less used in the detection process. Finally, the results suggest that our method is more robust to false alarms due to ectopic beats or other irregular rhythms than the method under comparison.
Turbid Scene Enhancement Using Multi-Directional Illumination Fusion

Ambient light is strongly attenuated in turbid media. Moreover, natural light is often more highly attenuated in some spectral bands, relative to others. Hence, imaging in turbid media often relies heavily on artificial sources for illumination. Scenes irradiated by an off-axis single point source have enhanced local object shadow edges, which may increase object visibility. However, the images may suffer from severe nonuniformity, regions of low signal (being distant from the source), and regions of strong backscatter. On the other hand, simultaneously illuminating the scene from multiple directions increases the backscatter and fills-in shadows, both of which degrade local contrast. Some previous methods tackle backscatter by scanning the scene, either temporally or spatially, requiring a large number of frames. We suggest using a few frames, in each of which wide field scene irradiance originates from a different direction. This way, shadow contrast can be maintained and backscatter can be minimized in each frame, while the sequence at large has a wider, more spatially uniform illumination. The frames are then fused by post processing to a single, clearer image. We demonstrate significant visibility enhancement underwater using as little as two frames.
Hypothesis in Silico Characterization of Putative Drug Targets in Staphylococcus Saprophyticus, Causing Bovine Mastitis

The bovine mastitis caused by coagulase negative staphylococci (CNS) has increased in many herds of urban and rural areas of India. Emergence of multi drug resistant bacteria has further made its management more complex and serious. Therefore, innovation of novel specific drug for the treatment of disease caused by particular organism remained to be a challenge. Hence, in the present study a bacterium was isolated from milk of the cow with bovine mastitis and was identified as S. saprophyticus, 44 pathways of S. saprophyticus retrieved (KEGG) from web server were found to be non homologous to the host Bos taurus, out of which 39 pathways were found to be in cytoplasm, 2 in cell wall and 3 in the cell membrane. The knowledge of the present study could make the drug discovery easier which have high affinity to the target site of the causative organism. Background: Mastitis is one of the serious diseases causing huge loss to the dairy industry worldwide. Bacteria involved in bovine mastitis infections are S. aureus, Coagulase negative staphylococci (CNS), E. coli, Serretia marcescens, Bacillus subtilis, Streptococcus species etc [1]. Bovine mastitis has remained a major infectious disease which is difficult to manage and control it. Further emergence of multi drug resistant bacteria causing mastitis has made its management more complex. It is more serious in the countries like India which is the highest milk producer in the world which requires an urgent attention and novel methods of intervention to control the disease. In this context that all the drugs selected for the treatment of bovine mastitis against a particular infectious agent has to be screened through the in silico study by using the bioinformatics tools and will have to be notified through a database dedicated for this purpose which help in identifying the high prevalence of bovine mastitis. Coagulase negative staphylococcus is one of the most important gram positive bacteria, which have a potential to infect both
Acinetobacter baumannii Isolated from Lebanese Patients: Phenotypes and Genotypes of Resistance, Clonality, and Determinants of Pathogenicity

Introduction: Acinetobacter baumannii is a nosocomial pathogen that usually affects critically ill patients. High mortality rates have been associated with MDR A. baumannii infections. Carbapenem resistance among these isolates is increasing worldwide and is associated with certain International Clones (ICs) and oxacillinases (OXAs). Moreover, this organism possesses a wide range of virulence factors, whose expression is not yet fully understood. In this study, clinical A. baumannii isolates are characterized in terms of antibiotic resistance, mechanisms of carbapenem resistance, clonality, and virulence. Materials and Methods: A. baumannii clinical isolates (n = 90) where obtained from a tertiary care center in Beirut, Lebanon. API 20NE strips in addition to the amplification of blaOXA−51−like were used for identification. Antibiotic susceptibility testing by disk diffusion was then performed in addition to PCRs for the detection of the most commonly disseminated carbapenemases. Clonality was determined by tri-locus PCR typing and doubling times were determined for isolates with varying susceptibility profiles. Biofilm formation, hemolysis, siderophore production, proteolytic activity, and surface motility was then determined for all the isolates. Statistical analysis was then performed for the determination of associations. Results and Discussion: 81 (90%) of the isolates were resistant to carbapenems. These high rates are similar to other multi-center studies in the country suggesting the need of intervention on a national level. 74 (91.3%) of the carbapenem resistant isolates harbored blaOXA−23−like including two that also harbored blaOXA−24−like. 88.9% of the A. baumannii isolates pertained to ICII and three other international clones were detected, showing the wide dissemination of clones into geographically distinct locations. Virulence profiles were highly diverse and no specific pattern was observed. Nevertheless, an association between motility, siderophore production, and biofilm formation was detected (p < 0.05). Conclusions: A very high rate of carbapenem resistance was detected, showing the need for immediate intervention. IC II and OXA-23-like were the most disseminated, reflecting their international dissemination. No specific associations were made between virulence and resistance, but instead associations among certain virulence factors were found. Investigating a more clonally diverse pool of isolates could help in the determination of associations between virulence and resistance.
Web 3DNA—a web server for the analysis, reconstruction, and visualization of three-dimensional nucleic-acid structures

The w3DNA (web 3DNA) server is a user-friendly web-based interface to the 3DNA suite of programs for the analysis, reconstruction, and visualization of three-dimensional (3D) nucleic-acid-containing structures, including their complexes with proteins and other ligands. The server allows the user to determine a wide variety of conformational parameters in a given structure—such as the identities and rigid-body parameters of interacting nucleic-acid bases and base-pair steps, the nucleotides comprising helical fragments, etc. It is also possible to build 3D models of arbitrary nucleotide sequences and helical types, customized single-stranded and double-helical structures with user-defined base-pair parameters and sequences, and models of DNA ‘decorated’ at user-defined sites with proteins and other molecules. The visualization component offers unique, publication-quality representations of nucleic-acid structures, such as ‘block’ images of bases and base pairs and stacking diagrams of interacting nucleotides. The w3DNA web server, located at http://w3dna.rutgers. edu, is free and open to all users with no login requirement.