Miracle-dz/UniFAR-FTUs huggingface dataset:
Can S100B Predict Cerebral Vasospasms in Patients Suffering from Subarachnoid Hemorrhage?

Background: Protein S100B has proven to be a useful biomarker for cerebral damages. Increased levels of serum and cerebrospinal fluid (CSF) S100B have been shown in patients suffering subarachnoid hemorrhage (SAH), severe head injury and stroke. In patients with SAH, the course of S100B levels has been correlated with neurological deficits and outcome. Cerebral vasospasm is a major contributor to morbidity and mortality. The primary aim of this study was to investigate the potential of S100B protein as a predictor of cerebral vasospasm in patients with severe SAH. Materials and Methods: Patients with SAH, Fisher grade 3 and 4, were included in the study. Five samples of CSF and serum S100B were collected from each patient. The first sample (baseline sample) was drawn within the first 3 days following ictus and the following four samples, once a day on days 5–8, with day of ictus defined as day 1. Clinical suspicion of cerebral vasospasm confirmed by computed tomography angiography was used to diagnose cerebral vasospasm. Results: A total of 18 patients were included. Five patients (28%) developed cerebral vasospasm, two (11%) developed ventriculitis. There were no significant differences between S100B for those with and without vasospasm. Serum S100B levels in patients with vasospasm were slightly lower within the first 5 days following ictus, compared to patients without vasospasm. Two out of five patients had elevated and increasing serum S100B prior to vasospasm. Only one showed a peak level of S100B 1 day before vasospasm could be diagnosed. Due to the low number of patients in the study, statistical significance could not be reached. Conclusion: Neither serum nor CSF S100B can be used as predictor of cerebral vasospasm in patients suffering from SAH.
Comparative Analysis of Scientific Publications of Research Entities Using Multiple Disciplinary Classifications

This study attempts a comparative disciplinary analysis of a Web of Science (WoS) recorded dataset (ca. 15,000 core journal articles and reviews) of real publications for 5-years period by the research departments, dedicated to science, technology, engineering, mathematics, and medicine (STEMM), of a comprehensive university using different disciplinary schemes: Essential Science Indicators (ESI) 22 research fields, SCOPUS 27 subject areas and OECD Frascati 38 subordinate research fields. It demonstrates that assigning the publications to departments and disciplines followed by correspondence analysis and clustering of the contingency table comprising disciplinary share of publications results in enhanced understanding and visualization of the research output at different levels of disciplinary or research entities. Such exercise improves creation of publication subsets (for different research entities and disciplinary schemes) for further processing, visualization and interpretation with sophisticated bibliometric/scientometric tools including the science mapping techniques.
Individual and population variation in invertebrates revealed by Inter-simple Sequence Repeats (ISSRs)

Abstract PCR-based molecular markers are well suited for questions requiring large scale surveys of plant and animal populations. Inter-simple Sequence Repeats or ISSRs are analyzed by a recently developed technique based on the amplification of the regions between inverse-oriented microsatellite loci with oligonucleotides anchored in microsatellites themselves. ISSRs have shown much promise for the study of the population biology of plants, but have not yet been explored for similar studies of animals. The value of ISSRs is demonstrated for the study of animal species with low levels of within-population variation. Sets of primers are identified which reveal variation in two aphid species, Acyrthosiphon pisum and Pemphigus obesinymphae, in the yellow fever mosquito Aedes aegypti, and in a rotifer in the genus Philodina.
A parallel true motion estimation method based on binarized cross correlation

Block based recursive search for motion estimation is already suitable for real time applications thanks to its wide utilization, but the increase of resolution in modern displays and the need for real time software implementations demand for faster approaches. In this paper a new motion estimation procedure that has been developed for a parallel implementation is presented. The parallel implementation was achieved by avoiding the spatial recursion which is typical for the recursive search systems, while the convergence can still be obtained by means of the temporal recursion only. In particular the use of a binarized cross correlation as a matching cost criteria instead of the standard SAD permits an improved motion vector field and a faster convergence without increasing the computational cost. The experimental results show a similar vector field estimation in comparison to the standard SAD based recursive search method with the considerable advantage of a possible parallel implementation.
Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context

Music recommendation systems based on Collaborative Filtering methods have been extensively developed over the last years. Typically, they work by analyzing the past user-song relationships, and provide informed guesses based on the overall information collected from other users. Although the music listening behavior is a repetitive and time-dependent process, these methods have not taken this into account and only consider user-song interaction for recommendation. In this work, we explore the usage of temporal context and session diversity in Session-based Collaborative Filtering techniques for music recommendation. We compared two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions, the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns. We evaluated the developed algorithms by measuring the Hit Ratio, and the Mean Reciprocal Rank. Results reveal that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF.
How developer communication frequency relates to bug introducing changes

Communication between developers plays a very central role in team-based software development for a variety of tasks such as coordinating development and maintenance activities, discussing requirements for better comprehension, assessing alternative solutions to complex problems, and like. However, the frequency of communication varies from time to time - sometimes developers exchange more messages with each other than at other times. In this paper, we investigate whether developer communication has any bearing with software quality by examining the relationship between communication frequency and number of bugs injected into the software. The data used for this study is drawn from the bug database, version archive, and mailing lists of the JDT sub-project in ECLIPSE. Our results show a statistically significant positive correlation between communication frequency and number of injected bugs in the software. We also noted that communication levels of key developers in the project do no correlate with number of injected bugs. Moreover, we show that defect prediction models can accommodate social aspects of the development process and potentially deliver more reliable results.
Combining Navigational Planning and Reactive

Traditional AI methods for navigational planning use qualitative spatial representations and reasoning. Traditional robotics techniques for this task are based on numerical representations and reasoning. Recent work on robotics posits mechanisms for reactive control that directly map perceptions of the world to actions on it. This in turn has given rise to hybrid robot ar-chitectures that combine navigational planning and reactive control. But following traditional robotics techniques, navigational planning in these hybrid ar-chitectures too uses numerical methods. This raises the following question: Given a hybrid robot architecture , are numerical methods really needed for naviga-tional planning? To explore this issue, we integrated a multistrategy qualitative navigational planner with a reactive-control mechanism. Then we embodied the integrated system on a physical robot. Next we gave the robot a series of navigation tasks in a visually structured spatial world containing discrete pathways, and monitored its actions as it executed the tasks in the presence of both static and moving obstacles. Our experiments show that for hybrid robots qualitative methods are suucient for navigational planning in at least one class of spatial worlds.
STOMP: Statistical Techniques for Optimizing and Modeling Performance of Blocked Sparse Matrix Vector Multiplication

Sparse-matrix vector multiplication (SpMV) is the core compute routine for several scientific and commercial codebases. Because of its extremely irregular memory accesses (low temporal locality), indirect memory referencing (low spatial locality), low arithmetic intensity, and the non-zero pattern and non-zero density of the matrix, SpMV achieves a mere 10% of peak system performance. Because sparse matrices have extremely varied non-zero patterns and densities, performance of SpMV is hard to predict. Blocking sparse matrices increases arithmetic intensity and spatial locality during SpMV operations, thereby improving SpMV performance. However, selection of an incorrect block size can produce performance degradation as high as 70%. In this study, we describe the STOMP approach of using statistical techniques to predict run time of SpMV in PETSc for new matrices with mean accuracy of 93.52%. We use these statistical prediction models to guide block size selection to achieve up to 100% of optimal performance, comparable to that attained through exhaustive block size search. Our block size selection results produce an average of 55.56% speedup over default SpMV options. On the same set of matrices used in the SPARSITY SpMV framework, STOMP yields a 54.46% speedup while SPARSITY yields a 31.62% speedup over the same default.
A fast maximum power point tracking strategy based on variable structure control for wind energy application

Nowadays, the wind energy due to its substantial advantages over fossil fuel based energy sources and other renewable energy sources, is growing significantly. In order to have a profitable and efficient wind energy harvesting system, wind turbines should continuously operate at maximum power point under wind velocity variations. This paper proposes a very fast and high-efficient Maximum Power Point Tracking (MPPT) control method for variable speed wind turbines. This control strategy possess a variable structure based on sliding mode control and could be simply implemented on a Wind Energy Generation System (WEGS). The main components of a WEGS are introduced and detailed theoretical analyses are presented. The verification of the theoretic analysis is done through simulating a 2.7 MW variable-speed PMSG based WEGS being controlled by the proposed controller.
The Wnt segment polarity pathway and TMED2 protein may interact via a lectin- and decoy-type mechanism

The Secl4-like protein (280-385 GOLD-domain) in this study scores highly with the carbohydrate-binding module (CBM) of the RetS domain from Pseudomonas aeruginosa. The thereupon modeled Cricetulus griseus p24-GOLD domain of p24, a member of intra-Golgi cargo receptors, is shown to interact with Wnt8 (wingless 8) of Xenopus laevis with a ΔG=-18.3 kcal/mol. Lower ranked models listed a smaller ΔG (PDBePISA) and energy of Del Phi interaction. Complex/hybrid N-glycans provide increasing energy of binding up to −7.1 kcal/mol to simulated p24-GOLD-ligand interaction. It is likely, that Wnt proteins and p24 cargo-receptors interact analogously to Wnt-Frizzled and that Wnt transport may involve early lectin binding. The possibly promiscuous interaction of p24-GOLD with ligands, including collagen, may shed light on cargo-receptor mediated traffic. Highlights p24s are usually described as type I transmembrane proteins containing a GOLD domain p24-GOLD can be modeled based on the Sec14-like protein with X-ray structure quality A concave patch on the p24 surface binds to diverse ligands N-glycans may provide increased binding energy in p24-cargo interaction Wnt binding to TMED2 may quench signaling similar to a decoy-type mechanism
Fingerprinting relational databases: schemes and specialties

In this paper, we present a technique for fingerprinting relational data by extending Agrawal et al.'s watermarking scheme. The primary new capability provided by our scheme is that, under reasonable assumptions, it can embed and detect arbitrary bit-string marks in relations. This capability, which is not provided by prior techniques, permits our scheme to be used as a fingerprinting scheme. We then present quantitative models of the robustness properties of our scheme. These models demonstrate that fingerprints embedded by our scheme are detectable and robust against a wide variety of attacks including collusion attacks.
ECSE-529 A Image Processing and Communication Project Report An Analysis of Eigenfaces for Face Detection

ii ABSTRACT This report studies the 1991 Mathew Turk and Alexander Pentland paper: " Face Recognition using Eigenfaces " that appeared in the CVPR proceedings [1]. It has since become an extremely popular approach to face detection. Once implemented, the algorithm proves many of Turk and Pentland's claims of the Eigenface approach to detecting faces with a limited amount of training required. The system proves to be quick and robust while detecting faces of trained and untrained images. Nevertheless, this report delves further and investigates the extents to which Eigenfaces can be used for face detection. Moreover, it examines how different factors (such as intensity, rotation, the size of the training set) may influence the results and the approach's total accuracy. Further, a fundamental flaw in the Turk and Pentland's method of Eigenfaces is revealed.
Evidence for genetic monogamy and female‐biased dispersal in the biparental mouthbrooding cichlid Eretmodus cyanostictus from Lake Tanganyika

In this study, we investigate whether apparent social monogamy (where a species forms a pair bond but may participate in copulations outside the pair bond) corresponds with genetic monogamy (where individuals participate only in copulations within a pair bond) in a biparental mouthbrooding cichlid fish, Eretmodus cyanostictus, from Lake Tanganyika, Africa. Our findings suggest that E. cyanostictus is both socially and genetically monogamous and that monogamy may result from limited opportunities for polygyny, rather than from reproductive benefits of monogamy. Mating systems are believed to influence the relative rate of dispersal of the sexes, and our results suggest that E. cyanostictus displays female‐biased dispersal, providing some support for the ‘resource competition’ hypothesis driving sex‐biased dispersal.
Exact closed-form expressions for the sum capacity and individual users’ rates in broadcast ergodic rayleigh fading channels

We derive closed-form expressions for the sum capacity of broadcast ergodic Rayleigh fading channels considering two power allocation policies along time: constant transmit power and optimal power allocation. In addition, we derive closed- form expressions for the individual users' rates when the sum capacity is achieved. We use these expressions to obtain the sum capacity and individual rates in a variety of broadcast channels. Among other facts, the numerical results reveal that the resulting rates using both power allocation policies are quite similar, except for some specific cases that are discussed.
Incremental MapReduce Computations

Abstract Distributed processing of large data sets is an area that received much attention from researchers and practitioners over the last few years. In this context, several proposals exist that leverage the observation that data sets evolve over time, and as such there is often a substantial overlap between the input to consecutive runs of a data processing job. This allows the programmers of these systems to devise an e ﬃ cient logic to update the output upon an input change. However, most of these systems lack compatibility existing models and require the programmer to implement an application-speciﬁc dynamic algorithm, which increases algorithm and code complexity. In this chapter, we describe our previous work on building a platform called Incoop, which allows for running MapReduce computations incrementally and transparently. Incoop detects changes between two ﬁles that are used as inputs to consecutive MapReduce jobs, and e ﬃ ciently propagates those changes until the new output is produced. The design of Incoop is based on memoizing the results of previously run tasks, and reusing these results whenever possible. Doing this e ﬃ ciently introduces several technical challenges that are overcome with novel concepts, such as a large-scale storage system that e ﬃ ciently computes deltas between two inputs, a Contraction phase to break up the work of the Reduce phase, and an a ﬃ nity-based scheduling algorithm. This chapter presents the motivation and design of Incoop, as well as a complete evaluation using several application benchmarks. Our results show signiﬁcant performance improvements without changing a single line of application code.
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT

This paper aims at effective use of training data by extracting sentences from large generaldomain corpora to adapt statistical machine translation systems to domain-specific data. We regard this task as a problem of filtering training sentences with respect to the target domain 1 via different similarity metrics. Thus, we give new insights into when data selection model can best benefit the in-domain translation. Based on the investigation of the state-ofthe-art similarity metrics, we propose edit distance as a new data selection criterion for this topic. To evaluate this proposal, we compare it with other methods on a large dataset. Comparative experiments are conducted on Chinese-English travel dialog domain and the results indicate that the proposed approach achieves a significant improvement over the baseline system (+4.36 BLEU) as well as the best rival model (+1.23 BLEU) using a much smaller training subset. This study may have a significant impact on mining very large corpora in a computationally-limited environment.
Real-time 3D hand posture estimation based on 2D appearance retrieval using monocular camera

This paper proposes a system for estimating arbitrary 3D human hand postures in real-time. It can accept not only pre-determined hand signs but also arbitrary postures and it works in a monocular camera environment. The estimation is based on a 2D image retrieval. More than 16,000 possible hand appearances are first generated from a given 3D shape model by rotating model joints and stored in an appearance database. Every appearance is tagged with its own joint angles which are used when the appearance was generated. By retrieving the appearance in the database well-matching to the input image contour, the joint angles of the input shape can be rapidly obtained. The search area is reduced by using an adjacency map in the database. To prevent tracking failures, a fixed number of the well-matching appearances are saved at every frame. After the multiple neighborhoods of the saved appearances are merged, the unified neighborhood is searched for the estimate efficiently by beam search. The posture estimates result from experimental examples are shown.
GL-V9, a new synthetic flavonoid derivative, ameliorates DSS-induced colitis against oxidative stress by up-regulating Trx-1 expression via activation of AMPK/FOXO3a pathway

GL-V9, a new synthesized flavonoid derivative, has been reported to possess anti-cancer properties in our previous studies. Uncontrolled overproduction of reactive oxygen species (ROS) has been implicated in oxidative damage of inflammatory bowel disease (IBD). In this study, we aimed to investigate the protective effect of GL-V9 against dextran sulfate sodium (DSS)-induced colitis. GL-V9 attenuated DSS-induced body weight loss, colon length shortening and colonic pathological damage. GL-V9 also inhibited inflammatory cells infiltration and decreased myeloperoxidase (MPO) and inducible nitric oxide synthase (iNOS) activities. Moreover, GL-V9 inhibited ROS and malondialdehyde (MDA) generation, but enhanced superoxide dismutase (SOD), glutathione (GSH) and total antioxidant capacity. GL-V9 reduced pro-inflammatory cytokines production in serum and colon as well. Mechanically, GL-V9 could increase Trx-1 via activation of AMPK/FOXO3a to suppress DSS-induced colonic oxidative stress. Furthermore, GL-V9 decreased pro-inflammatory cytokines and ROS production and increased the antioxidant defenses in the mouse macrophage cells RAW264.7 by promoting Trx-1 expression. In conclusion, our study demonstrated that GL-V9 attenuated DSS-induced colitis against oxidative stress by up-regulating Trx-1 via activation of AMPK/FOXO3a pathway, suggesting that GL-V9 might be a potential effective drug for colitis.