Miracle-dz/UniFAR-FTUs huggingface dataset:
New methods for relevance feedback: improving information retrieval performance

Relevance feedback has proven to be a highly effective technique for improving information retrieval (IR) resuits. After presenting the basic IR models and their associated relevance feedback techniques, we show how new computational models such as genetic algorithms and connection& processing can be used to further improve relevance feedback techniques and ultimately, retrieval performance. 1 Information Retrieval Systems Information retrieval has been the subject of much research over the last several years, largely due to the imprecise nature of determining which textual records are relevant to user queries. Prior research has been involved with indexing, determining which terms to use for top ica.lly. There is consensus in the information retrieval field that relevance itself is au imprecise concept. Recent research has identified factors other than topically that h&e influenced subjects’ evaluations of the relevance of information fBarrv. 1994). includinn readability, that might be qua&tied-&d indiuded in &e indexing process. One can view an information retrieval system in a variety of ways. One way is to consider it ss a set of records that are identified, acquired, indexed, and stored and as a set of user queries for information that are matched to the index to determine which subset of the stored records should be retrieved and presented to the user. The index can involve descriptive information (i.e., bibliograiphic information in the case of textual documents, such as author, title, publisher, or year of publication), and content indicators (such as keywords or subject headings) to indicate the nature of what the document is about. We can be following way ‘n to model the retrieval system in the & raft. , 1985): D = a set of documents or records, T = a set of index terms (single words or phrases), F = an indexing function, where F : D x T [0, l] Q = a set of user queries for documents, a = a,;u,e,ry term weighting function, where 4 : Q x T --) “Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commerical advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To cop:y 482 otherwise, or to republish, requires a fee and/or specific permission. ”
Ethics Desk Reference for Psychologists

The Ethics Desk Reference for Psychologists is an easy-to-use pocket guide that aides psychologists in identifying and avoiding ethical dilemmas. Editors Barnett and Johnson provide a jargon-free summary of each section of the APA Ethics Code (2002), note common dilemmas and conflicts that are associated with each section, and offer practical suggestions for preventing conflict and promoting positive practice. Additional resources are provided that will help psychologists to respond to ethical conflicts effectively if they do arise. These include a model for ethical decision making, a detailed checklist of what to do if you are facing an ethics complaint, and a list of ethics resources. This compact desk reference is an essential resource for all psychologists, trainees, and graduate students.
Using Signature Genes as Tools To Assess Environmental Viral Ecology and Diversity

ABSTRACT Viruses (including bacteriophages) are the most abundant biological entities on the planet. As such, they are thought to have a major impact on all aspects of microbial community structure and function. Despite this critical role in ecosystem processes, the study of virus/phage diversity has lagged far behind parallel studies of the bacterial and eukaryotic kingdoms, largely due to the absence of any universal phylogenetic marker. Here we review the development and use of signature genes to investigate viral diversity, as a viable strategy for data sets of specific virus groups. Genes that have been used include those encoding structural proteins, such as portal protein, major capsid protein, and tail sheath protein, auxiliary metabolism genes, such as psbA, psbB, and phoH, and several polymerase genes. These marker genes have been used in combination with PCR-based fingerprinting and/or sequencing strategies to investigate spatial, temporal, and seasonal variations and diversity in a wide range of habitats.
Characterizing Cancer-Specific Networks by Integrating TCGA Data

The Cancer Genome Atlas (TCGA) generates comprehensive genomic data for thousands of patients over more than 20 cancer types. TCGA data are typically whole-genome measurements of multiple genomic features, such as DNA copy numbers, DNA methylation, and gene expression, providing unique opportunities for investigating cancer mechanism from multiple molecular and regulatory layers. We propose a Bayesian graphical model to systemically integrate multi-platform TCGA data for inference of the interactions between different genomic features either within a gene or between multiple genes. The presence or absence of edges in the graph indicates the presence or absence of conditional dependence between genomic features. The inference is restricted to genes within a known biological network, but can be extended to any sets of genes. Applying the model to the same genes using patient samples in two different cancer types, we identify network components that are common as well as different between cancer types. The examples and codes are available at https://www.ma. utexas.edu/users/yxu/software.html.
Making Tangible the Intangible: Hybridization of the Real and the Virtual to Enhance Learning of Abstract Phenomena

Interactive systems based on Augmented Reality (AR) and Tangible User Interfaces (TUI) hold great promise for enhancing the learning and understanding of abstract phenomena. In particular, they enable to take advantage of numerical simulation and pedagogical supports, while keeping the learner involved in true physical experimentations. In this paper, we present three examples based on AR and TUI, where the concepts to be learnt are difficult to perceive. The first one, Helios, targets K-12 learners in the field of astronomy. The second one, Hobit is dedicated to experiments in wave optics. Finally, the third one, Teegi, allows one to get to know more about brain activity. These three hybrid interfaces have emerged from a common basis that jointly combines research and development work in the fields of Instructional Design and Human-Computer Interaction, from theoretical to practical aspects. On the basis of investigations carried out in real context of use and on the grounding works in education and HCI which corroborate the design choices that were made, we formalize how and why the hybridization of the real and the virtual enables to leverage the way learners understand intangible phenomena in Sciences education.
Nutritional Considerations in Clinical Treatment : The Perspective of a Neurosurgeon

Traumatic brain injury (TBI) remains a highly lethal injury with mortality ranging from 20-50 percent (Bulger et al., 2002; Demetriades et al., 2004; Jiang et al., 2002). Approximately 52,000 patients die from TBI each year (Sosin et al., 1995; Thurman et al., 1999) with approximately 85 percent of the deaths occurring within the first two weeks (Roberts et al., 2004). TBI pharmaceutical trials have failed to demonstrate any efficacy in reducing deaths (Narayan et al., 2002). Proper trauma transport systems and maintenance of cerebral perfusion and oxygenation by avoidance of hypoxemia, arterial hypotension and intracranial hypertension reduces mortality and improves outcome (Brain Trauma Foundation, 2000; Chesnut et al., 1993; Härtl et al., 2006; Sampalis et al., 1999; Smith et al., 1990). Currently, the metabolic status and nutritional needs of TBI patients are less of a priority than maintaining cerebral perfusion. However, TBI results in a hypermetabolic and catabolic state that increases systemic and cerebral energy requirements (Clifton et al., 1986; Deutschman et al., 1986; Hovda et al., 1995; Weekes and Elia, 1996). A recent review from the Cochrane Collaboration states that early feeding may be associated with a trend towards better outcomes after TBI (Perel et al., 2006). The “Guidelines for the Management of Severe Traumatic Brain Injury” recommend that the patient’s feeding requirements should be met by the end of the first week after TBI (Brain Trauma Foundation, 2000). These recommendations were based on two small, randomized trials (Rapp et al., 1983; Taylor et al., 1999). There are no studies on the relationship of mortality to the amount and frequency of feeding in TBI patients. In the few studies done, none controlled for factors known to affect mortality from TBI, such as hypotension, age, pupillary status, and CT scan findings. The Brain Trauma Foundation (BTF) prospectively collects data on preand inhospital TBI management in 20 Level I and 2 Level II trauma centers in New York State as part of a TBI quality improvement program. An analysis was conducted examining the effect of timing and quantity of nutritional support on early mortality. Early onset of nutritional support and amount of nutritional support was hypothesized to be associated with a reduced mortality at two weeks. In addition, a feeding compliance implementation program was undertaken at one of the participating hospitals to increase the net caloric intake of patients.
Real-time collision avoidance for redundant manipulators

The paper presents a joints trajectories planning strategy for a redundant manipulator. The control system modifies, in real-time, the robot manipulator joints references positions, during a transition, when an unexpected moving obstacle is getting closer to one of the robot components (links and joints). The position of the moving objects is determined using a digital image processing system. The control system determines if the moving obstacle and the arm robot are in collision using an interference detection method based on oriented bounding boxes. When the arm robot control system detects a collision, it modifies the arm robot joints trajectories in order to increase the distance between the robot and the obstacle and, in the same time, maintain the initial trajectory of the end-effecter. Computer simulation and experimental results are given for a 7 DOF redundant arm and demonstrate the collision avoidance capability.
Dynamic inference of likely data preconditions over predicates by tree learning

We present a technique to infer likely data preconditions forprocedures written in an imperative programming language. Given a procedure and a set of predicates over its inputs, our technique enumerates different truth assignments to the predicates, deriving test cases from each feasible truth assignment. The predicates themselves are derived automatically using simple heuristics. The enumeration of truth assignments is performed using a propositional SAT solver along with a theory satisfiability checker capable of generating unsatisfiable cores. For each assignment of truth values, a corresponding set of test cases are generated and executed. Based on the result of the execution, the truth assignment is classified as being safe or buggy. Finally, a decision tree classifier is used to generate a Boolean formula over the input predicates that explains the data obtained from the test cases. The resulting Boolean formula is, in effect, a likely data precondition for the procedure under consideration. We apply our techniques on a wide variety of functions from the standard C library. Our experiments show that the proposed technique is quite robust. For most cases, it successfully learns a precondition that captures a safe and permissive calling environment.
Complete Genome Sequence of KPC-Producing Klebsiella pneumoniae Strain CAV1193

ABSTRACT Carbapenem resistance in Klebsiella pneumoniae, frequently conferred by the blaKPC gene, is a major public health threat. We sequenced a blaKPC-containing strain of K. pneumoniae belonging to the emergent lineage ST941, in order to better understand the evolution of blaKPC within this species.
Where are the bottlenecks in software packet processing and forwarding?: Towards high-performance network operating systems

We analyze the performance bottlenecks in packet processing and forwarding with general-purpose computers to achieve high-throughput and low-latency, and to guarantee service quality. In this paper, we first demonstrate that the performance bottleneck in software packet processing and forwarding is a blocking operating to access to PCIe registers with memory mapped I/O that are used to operate the head and tail pointers of ring buffers of NICs through two experiments. We then summarize the strategies to achieve high-performance software packet processing and forwarding against the bottlenecks, and develop and evaluate a prototype system with routing functionality to illustrate these strategies are effective.
Impacts of Objective and Subjective Social Inclusion on the Quality of Life of Individuals with Schizophrenia Spectrum Disorders and Major Depressive Disorder

Increased social inclusion and enhanced quality of life for individuals with severe mental illnesses (SMIs) are goals of the recovery movement. The present study examined the differences in reported subjective social inclusion (SubSI) and objective social inclusion (ObjSI) between individuals diagnosed with schizophrenia spectrum disorders (SSDs) and those diagnosed with Major Depressive Disorder (MDD). Furthermore, the amount of variance in quality of life (QOL) which can be predicted by type of diagnosis, SSDs or MDD, symptom severity, and SubSI and ObjSi was determined. An archival data set was used. Participants were 337 individuals whose primary diagnosis was an SSD or MDD. Overall, participants diagnosed with an SSD were found to report less social inclusion than participants diagnosed with MDD; specifically, participants diagnosed with an SSD reported significantly lower SubSI than participants diagnosed with MDD. ObjSI, SubSI, symptom severity, and diagnosis were found to significantly predict QOL and accounted for 31.3% of the variance in QOL. Higher scores on the ObjSI and SubSI measures predicted higher QOL scores. Fewer symptoms indicated predicted higher QOL scores. Finally, a diagnosis of SSD was also predictive of higher QOL scores than a diagnosis of MDD. Utilizing the knowledge gained through this study, clinicians can work to tailor treatment goals, treatment planning, and therapeutic milieu more appropriately for their clients with SMIs. Clinical researchers can utilize QOL as an outcome variable for determining treatment effects in a more robust manner. Other implications and limitations of the study are also explored.
Temperature-aware scheduler based on thermal behavior grouping in multicore systems

Dynamic Thermal Management techniques have been widely accepted as a thermal solution for their low cost and simplicity. The techniques have been used to manage the heat dissipation and operating temperature to avoid thermal emergencies, but are not aware of application behavior in Chip Multiprocessors (CMPs). In this paper, we propose a temperature-aware scheduler based on applications' thermal behavior groups classified by a K-means clustering method in multicore systems. The application's thermal behavior group has similar thermal pattern as well as thermal parameters.With these thermal behavior groups, we provide thermal balances among cores with negligible performance overhead. We implement and evaluate our schemes in the 4-core (Intel Quad Core Q6600) and 8-core (two Quad Core Intel XEON E5310 processors) systems running several benchmarks. The experimental results show that the temperature-aware scheduler based on thermal behavior grouping reduces the peak temperature by up to 8°C and 5°C in our 4-core system and 8-core system with only 12% and 7.52% performance overhead, respectively, compared to Linux standard scheduler.
Effects of the Total Alkaloidal Extract of Murraya koenigii Leaf on Oxidative Stress and Cholinergic Transmission in Aged Mice

Alzheimer's disease (AD) is characterized by signs of major oxidative stress and the loss of cholinergic cells. The present study was designed to investigate the role of the total alkaloidal extract from Murraya koenigii (MKA) leaves on age related oxidative stress and the cholinergic pathway in aged mice. Ascorbic acid (100 mg/kg, p.o.) was used as a standard drug. The MKA improved the level of protective antioxidants such as glutathione peroxidase (GPx), reduced glutathione (GSH), glutathione reductase (GRD), superoxide dismutase (SOD) and catalase (CAT) in brain homogenate at higher doses (20 and 40 mg/kg, p.o.). Moreover, a dose dependent decline was noted in lipid peroxidation (LPO) and the nitric oxide assay (NO) at all doses of MKA (10, 20 and 40 mg/kg, p.o.). Interestingly, significant progress was noted with the supplementation of MKA by an improvement of the acetylcholine (ACh) levels and a reduction in the acetylcholinesterase (AChE) activity in aged mouse brain. In addition, a significant elevation of serum albumin (ALBU), alkaline phosphatase (ALP), alanine transaminase (ALT), aspartate transaminase (AST) and total protein as well as a decline in creatinine, total cholesterol, urea nitrogen and glucose levels with MKA also ameliorated the hepatic and renal functions in normal ageing process. The results showed the possible utility of Murraya koenigii leaves in neuroprotection against neurodegenerative disorders such as Alzheimer's disease. Copyright © 2012 John Wiley & Sons, Ltd.
Fundamentals of speech recognition

1. Fundamentals of Speech Recognition. 2. The Speech Signal: Production, Perception, and Acoustic-Phonetic Characterization. 3. Signal Processing and Analysis Methods for Speech Recognition. 4. Pattern Comparison Techniques. 5. Speech Recognition System Design and Implementation Issues. 6. Theory and Implementation of Hidden Markov Models. 7. Speech Recognition Based on Connected Word Models. 8. Large Vocabulary Continuous Speech Recognition. 9. Task-Oriented Applications of Automatic Speech Recognition.
Efficient Feature Extraction for Image Classification

In many image classification applications, input feature space is often high-dimensional and dimensionality reduction is necessary to alleviate the curse of dimensionality or to reduce the cost of computation. In this paper, we extract discriminant features for image classification by learning a low-dimensional embedding from finite labeled samples. In the new feature space, intra-class compactness and extra-class separability are achieved simultaneously. Target dimensionality of the embedding is selected by spectral analysis. Our method is designed suitable for data with both uni- and multi-modal class distributions. We also develop its two-dimensional variant which makes use of the matrix representation of images. Experimental results on three real image datasets demonstrate the efficacy of our method compared to the state of the art.
Anticipating Knowledge to Inform Species Management: Predicting Spatially Explicit Habitat Suitability of a Colonial Vulture Spreading Its Range

Background The knowledge of both potential distribution and habitat suitability is fundamental in spreading species to inform in advance management and conservation planning. After a severe decline in the past decades, the griffon vulture (Gyps fulvus) is now spreading its breeding range towards the northwest in Spain and Europe. Because of its key ecological function, anticipated spatial knowledge is required to inform appropriately both vulture and ecosystem management. Methodology/Findings Here we used maximum entropy (Maxent) models to determine the habitat suitability of potential and current breeding distribution of the griffon vulture using presence-only data (N = 124 colonies) in north-western Spain. The most relevant ecological factors shaping this habitat suitability were also identified. The resulting model had a high predictive performance and was able to predict species' historical distribution. 7.5% (∼1,850 km2) of the study area resulted to be suitable breeding habitat, most of which (∼70%) is already occupied by the species. Cliff availability and livestock density, especially of sheep and goats, around 10 km of the colonies were the fundamental factors determining breeding habitat suitability for this species. Conclusions/Significance Griffon vultures could still spread 50–60 km towards the west, increasing their breeding range in 1,782 km2. According to our results, 7.22% of the area suitable for griffon vulture will be affected by wind farms, so our results could help to better plan wind farm locations. The approach here developed could be useful to inform management of reintroductions and recovery programmes currently being implemented for both the griffon vulture and other threatened vulture species.
A Survey on Performance Improvement inPower Heterogeneous MANETs

In MANETs, network may consist of devices with heterogeneous characteristics in terms of transmission power, energy, capacity etc. especially in VANETs. In such heterogeneous networks, nodes are likely to transmit at different power levels, thereby causing communication links of varying range. This causes link asymmetry problem. Also, due to interference raised by high power nodes, the throughput of such networks will be severely affected. Literature survey of current works, suggest ways to only detect the unidirectional links and to avoid transmissions through such links. They do not consider the benefits of high-power nodes. This paper highlights methods to improve the routing performance of power heterogeneous MANETs by efficiently exploiting the advantages and avoiding the disadvantages of high-power nodes.
A Comparative Study of Rotation Invariant Classification and Retrieval of Texture Images

This paper presents a detailed comparative study of 4 rotation invariant texture analysis methods. Human subjects are included as a benchmark for the computational methods. Experiments are conducted on two databases of 450 and 1320 images taken from 10 and 44 Brodatz texture classes respectively. Classification and content based retrieval experiments are used in the comparison, which includes the effect of Gaussian noise on each method (including the human subjects). The methods tested are: the multichannel Gabor filtering method; an edge attribute processing method; the circular simultaneous autoregressive (CSAR) model method and a method based on hidden Markov models with wavelet decomposition. The test conditions in the study are kept both general and constant so direct comparisons of the results obtained can be made.
Multi-Cell MIMO Downlink With Cell Cooperation and Fair Scheduling: A Large-System Limit Analysis

We consider the downlink of a cellular network with multiple cells and multi-antenna base stations. Our model includes distance-dependent pathloss, arbitrary clusters of cooperating cells, and general “fairness” requirements. Beyond Monte Carlo simulation, no efficient computation method to evaluate the ergodic throughput of such systems has been presented, yet. Furthermore, for systems of practical size with tens of cells and hundreds of users per cell, even simulation becomes challenging. We develop an analytic framework based on the combination of results from large random matrix theory and convex optimization. This allows computationally efficient calculation of the system performance in the so-called “large system limit”, i.e., in the limit of a large number of antennas per base station and a large number of users per cell, while the ratio of antennas per user is kept constant. In particular, the system ergodic throughput, subject to per-base station power constraints and to general fairness criteria, is obtained via the iterative solution of a system of fixed-point equations. Comparisons with finite-dimensional simulation results show that the large-system analysis provides remarkably accurate approximations for the actual finite-dimensional systems, even for a small number of users and base station antennas.