Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Support Vector Machines With a Pairwise Decision Tree

To address the multiclass classification problem of hyperspectral data, a new method called pairwise decision tree of support vector machines (PDTSVM) is proposed. For an N -class problem, after training N(N - 1)/2 binary support vector machines (SVMs) for each pair of information class, PDTSVM only requires N - 1 binary SVMs for one classification. Based on the separability estimated by the geometric margin between two classes, binary SVMs are recursively selected by using a fast sequential forward selection. Each binary SVM is used to exclude the less-similar class. PDTSVM eliminates the wrong votes of the one-against-one method. It also has much fewer layers than other tree-based methods, which decreases accumulated errors. Tested with an 11-class problem, the results demonstrate the effectiveness of our method.
Modeling the Structure of Collective Intelligence by

The human problem solution process has attracted an increasing amount of interest among educators, managers, computer scientists and others. However, the discussion of the subject has suffered from the lack of stochastic tools to quantitatively capture both the subtler steps of the problem solution process and the diversity of human thinking. In order to stochastically model the human problem solution, this thesis presents an approach referred to as "influence modeling," that attempts to describe how an individual navigates from one random memory chunk to another related memory chunk, and how a group of people randomly remind one another of memory chunks that could be individually uncommon. As an application of influence modeling, this thesis shows how groups play "20-questions" games based on a semantic space, such as ConceptNet (a common-sense database). It also investigates how groups send signals about their behavior, which are collected by embedded devices, how group interaction processes could be automatically monitored with embedded devices, how group performance could be facilitated, and how to map group behavior and performance from the macroscopic level to the microscopic level in experiments in measuring collective intelligence. The influence modeling makes it possible to understand how a group could perform better than an individual. It also allows for the monitoring of the status of the problem solution, and makes it possible to direct group interaction in more fruitful ways. Thesis Advisor: Prof. Alex (Sandy) Pentland Toshiba Professor of Media Arts and Sciences
A Bayesian analysis of pharmacoeconomic data from a clinical trial on schizophrenia

Pharmacoeconomic studies are performed in a higher frequency to assess the economic interest of new drugs. However, a standard methodology does not still completely exist. We present here the principles and results of a cost‐effectiveness Bayesian analysis on data from 146 patients (interim analysis) collected during a clinical trial. This trial was originally planned to enrol 245 patients with predominantly negative schizophrenia symptoms and involved four treatment groups (a new treatment given at low dose and high dose, a comparator and a placebo). First, some prior distributions of the cost‐effectiveness ratio were numerically deduced from the effectiveness parameter clinical priors (based on investigators' opinions and questionnaires before going to blind breaking) and from cost function priors. The costs taken into account were hospitalizations, sick leave days, treatments, visits to the doctor, laboratory exams and suicide attempts. The effectiveness parameter was the change from baseline on SANS (scale for the assessment of negative symptoms). Posterior distributions were elaborated for the cost‐effectiveness ratio by combining the cost‐effectiveness ratio priors and likelihood together using the Bayes theorem. Results lead to a conclusion in favour of the new treatment given at high dose. Copyright © 2003 John Wiley & Sons, Ltd.
Chai: Collaborative heterogeneous applications for integrated-architectures

Heterogeneous system architectures are evolving towards tighter integration among devices, with emerging features such as shared virtual memory, memory coherence, and systemwide atomics. Languages, device architectures, system specifications, and applications are rapidly adapting to the challenges and opportunities of tightly integrated heterogeneous platforms. Programming languages such as OpenCL 2.0, CUDA 8.0, and C++ AMP allow programmers to exploit these architectures for productive collaboration between CPU and GPU threads. To evaluate these new architectures and programming languages, and to empower researchers to experiment with new ideas, a suite of benchmarks targeting these architectures with close CPU-GPU collaboration is needed. In this paper, we classify applications that target heterogeneous architectures into generic collaboration patterns including data partitioning, fine-grain task partitioning, and coarse-grain task partitioning. We present Chai, a new suite of 14 benchmarks that cover these patterns and exercise different features of heterogeneous architectures with varying intensity. Each benchmark in Chai has seven different implementations in different programming models such as OpenCL, C++ AMP, and CUDA, and with and without the use of the latest heterogeneous architecture features. We characterize the behavior of each benchmark with respect to varying input sizes and collaboration combinations, and evaluate the impact of using the emerging features of heterogeneous architectures on application performance.
Human Monoclonal Antibodies Broadly Neutralizing against Influenza B Virus

Influenza virus has the ability to evade host immune surveillance through rapid viral genetic drift and reassortment; therefore, it remains a continuous public health threat. The development of vaccines producing broadly reactive antibodies, as well as therapeutic strategies using human neutralizing monoclonal antibodies (HuMAbs) with global reactivity, has been gathering great interest recently. Here, three hybridoma clones producing HuMAbs against influenza B virus, designated 5A7, 3A2 and 10C4, were prepared using peripheral lymphocytes from vaccinated volunteers, and were investigated for broad cross-reactive neutralizing activity. Of these HuMAbs, 3A2 and 10C4, which recognize the readily mutable 190-helix region near the receptor binding site in the hemagglutinin (HA) protein, react only with the Yamagata lineage of influenza B virus. By contrast, HuMAb 5A7 broadly neutralizes influenza B strains that were isolated from 1985 to 2006, belonging to both Yamagata and Victoria lineages. Epitope mapping revealed that 5A7 recognizes 316G, 318C and 321W near the C terminal of HA1, a highly conserved region in influenza B virus. Indeed, no mutations in the amino acid residues of the epitope region were induced, even after the virus was passaged ten times in the presence of HuMAb 5A7. Moreover, 5A7 showed significant therapeutic efficacy in mice, even when it was administered 72 hours post-infection. These results indicate that 5A7 is a promising candidate for developing therapeutics, and provide insight for the development of a universal vaccine against influenza B virus.
Infection with hepatitis C virus transmitted by accidental needlesticks.

Sir—Garcia et al. [1] describe a health care worker who developed a case of acute infection with hepatitis C virus (HCV) with a very rapid onset (within 9 days) following a needlestick injury. A similar case, with a similarly short incubation period of 2 weeks, was reported by Morand et al. [2] in 2001. In both cases, HCV RNA testing was performed after the onset of symptoms at week 2 and produced positive results. Gar-cia et al. [1] conclude that their case " reveals the importance of detecting HCV RNA soon after receipt of a needlestick to provide prompt treatment to the patient, regardless of whether it is necessary, to prevent chronic HCV infection " [pg. 1634]. Similarly, the Centers for Disease Control and Prevention guidelines [3] state that an HCV RNA test may be performed at 4–6 weeks after receipt of a needlestick injury if earlier diagnosis of HCV infection is desired. We do not completely agree with this approach or with its applicability in current practice. In our prospective surveillance study [4], out of a total of 3735 health care workers who sustained a percutaneous injury from an anti-HCV– positive source and who were followed up for у6 months after exposure (mean duration of follow-up, 9.2 months; range, 6– 15 months), we detected 17 workers with HCV seroconversions following a need-lestick with a hollow-bore, blood-filled needle (0.5% of subjects; 95% CI, 0.3– 0.7). In 11 cases, HCV infection was identified within 2 months after exposure because of symptoms suggesting acute hepatitis. If we had adopted the approach proposed by Garcia et al. [1], obtaining an early diagnosis for the 6 subjects with seroconversion who did not develop symptoms—if of any advantage in treating them—would have required у3724 HCV RNA tests, assuming that a single test was sufficient to detect (or rule out) infection. Because there is no data indicating that early treatment of acute HCV infection is more effective than early treatment of chronic HCV infection [5] and because, in many cases, infection may resolve without therapy [6], the advantage of an early diagnosis though HCV RNA testing is debatable. Recently, the European Group for the Standardization of the Management of Occupational Exposure to HIV/Blood-Borne Infections, which we coordinate, issued recommendations for the management of health care workers who are occupationally exposed to hepatitis B and C viruses [7], and this document does not recommend performing routine HCV …
CUET_Binary_Hackers@DravidianLangTech EACL2024: Fake News Detection in Malayalam Language Leveraging Fine-tuned MuRIL BERT

Due to technological advancements, various methods have emerged for disseminating news to the masses. The pervasive reach of news, however, has given rise to a significant concern: the proliferation of fake news. In response to this challenge, a shared task in Dravidian- LangTech EACL2024 was initiated to detect fake news and classify its types in the Malayalam language. The shared task consisted of two sub-tasks. Task 1 focused on a binary classification problem, determining whether a piece of news is fake or not. Whereas task 2 delved into a multi-class classification problem, categorizing news into five distinct levels. Our approach involved the exploration of various machine learning (RF, SVM, XGBoost, Ensemble), deep learning (BiLSTM, CNN), and transformer-based models (MuRIL, Indic- SBERT, m-BERT, XLM-R, Distil-BERT) by emphasizing parameter tuning to enhance overall model performance. As a result, we introduce a fine-tuned MuRIL model that leverages parameter tuning, achieving notable success with an F1-score of 0.86 in task 1 and 0.5191 in task 2. This successful implementation led to our system securing the 3rd position in task 1 and the 1st position in task 2. The source code will be found in the GitHub repository at this link: https://github.com/Salman1804102/ DravidianLangTech-EACL-2024-FakeNews.
Auto-context Convolutional Neural Network for Geometry-Independent Brain Extraction in Magnetic Resonance Imaging

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and robustness of brain extraction, therefore, is crucial for the accuracy of the entire brain analysis process. With the aim of designing a learning-based, geometry-independent and registration-free brain extraction tool in this study, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2D patches of different window sizes. In this architecture three parallel 2D convolutional pathways for three different directions (axial, coronal, and sagittal) implicitly learn 3D image information without the need for computationally expensive 3D convolutions. Posterior probability maps generated by the network are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain, to extract it from non-brain tissue. The brain extraction results we have obtained from our algorithm are superior to the recently reported results in the literature on two publicly available benchmark datasets, namely LPBA40 and OASIS, in which we obtained Dice overlap coefficients of 97.42% and 95.40%, respectively. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily-oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) datasets. In this application our algorithm performed much better than the other methods (Dice coefficient: 95.98%), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Our CNN-based method can provide accurate, geometry-independent brain extraction in challenging applications.
Institutional Barriers to Research on Sensitive Topics: Case of Sex Communication Research Among University Students

The advance of science and human knowledge is impeded by misunderstandings of various statistics, insufficient reporting of findings, and the use of numerous standardized and non-standardized presentations of essentially identical information. Communication with journalists and the public is hindered by the failure to present statistics that are easy for non-scientists to interpret as well as by use of the word significant, which in scientific English does not carry the meaning of "important" or "large. " This article promotes a new standard method for reporting two-group and two-variable statistics that can enhance the presentation of relevant information, increase understanding of findings, and replace the current presentations of two-group ANOVA, t-tests, correlations, chi-squares, and z-tests of proportions. A brief call to highly restrict the publication of risk ratios, odds ratios, and relative increase in risk percentages is also made, since these statistics appear to provide no useful scientific information regarding the magnitude of findings.
Taxonomic Implications of Morphological and Genetic Differences in Northeastern Coyotes (Coywolves) ( Canis latrans × C. lycaon ), Western Coyotes ( C. latrans ), and Eastern Wolves ( C. lycaon or C. lupus lycaon )

The eastern Coyote or Coywolf ( Canis latrans × C. lycaon ) inhabiting northeastern North America resulted from hybridization between the expanding population of the western Coyote ( Canis latrans ) and the remnant population of Eastern Wolf ( C. lycaon ) and possibly domestic dogs ( C. lupus familiaris ) in the early 20th century. This study compares the body mass of eastern (i.e., northeastern) Coyotes, western Coyotes, and Eastern Wolves and synthesizes the recent literature to gain better insight into the taxonomic relations and differences of closely-related Canis species. Northeastern Coyotes (males = 16.5 kg; females = 14.7 kg) were statistically ( P < 0.0001) intermediate in mass between western Coyotes (males = 12.2 kg; females = 10.7 kg) and Eastern Wolves (males = 28.2 kg, females = 23.7 kg), consistent with their hybrid origin, but were numerically closer to western Coyotes. Large Cohen’s d (3.00–8.56), (0.915–0.929), and Cohen’s f (3.28–3.62) values indicated large effect sizes from the body mass comparisons. Eastern Wolves were 61–71% heavier than the same sex in the northeastern Coyotes, which in turn were ca. 35–37% heavier than the same sex in the western Coyotes. Alternatively, western Coyotes were 73–74% of the size of the same sex in the northeastern Coyotes, which in turn were 59–62% of the size of the same sex in the Eastern Wolves. I also attempted to relate mitochondrial DNA (mtDNA) haplotypes to body mass. Six of 17 (35.3%) adult female northeastern Coyotes captured in Massachusetts weighed ≥18 kg, heavier than any other described Coyote from outside northeastern North America. Mitochondrial DNA haplotypes associated with these heavy female northeastern canids were C9 = 4, C19 = 1, and C48 = 1. Body mass (kg) and mtDNA haplotype data of 53 northeastern Coyotes (males = 28, females = 25) showed no difference between haplotype and body mass for males ( P < 0.852) or females ( P < 0.128), suggesting that there is not a particular haplotype (e.g., C1) that is associated with the heavier animals. I propose that the most appropriate name for this hybrid animal is Coywolf ( Canis latrans × C. lycaon ), rather than a type of Coyote. Coywolves are distinct, being larger than any other population of Coyotes but smaller than Eastern Wolves. I propose that the 5 distinct types of Canis be recognized as: western Coyote, Coywolf (northeastern Coyote), Eastern Wolf (including Red Wolf C. rufus ), Gray × Eastern Wolf hybrids (‘Great Lakes’ Wolves; C. lupus × C. lycaon or C. lycaon × C. lupus ), and Gray Wolf ( C. lupus ). The implications for wolf recovery in the northeastern United States is discussed.
Nowcasting Events from the Social Web with Statistical Learning

We present a general methodology for inferring the occurrence and magnitude of an event or phenomenon by exploring the rich amount of unstructured textual information on the social part of the Web. Having geo-tagged user posts on the microblogging service of Twitter as our input data, we investigate two case studies. The first consists of a benchmark problem, where actual levels of rainfall in a given location and time are inferred from the content of tweets. The second one is a real-life task, where we infer regional Influenza-like Illness rates in the effort of detecting timely an emerging epidemic disease. Our analysis builds on a statistical learning framework, which performs sparse learning via the bootstrapped version of LASSO to select a consistent subset of textual features from a large amount of candidates. In both case studies, selected features indicate close semantic correlation with the target topics and inference, conducted by regression, has a significant performance, especially given the short length --approximately one year-- of Twitter’s data time series.
A VQ-Based Demosaicing by Self-Similarity

In this paper, we propose a learning-based demosaicing and a restoration error detection. A Vector Quantization (VQ)-based method is utilized for learning. We take advantage of a self-similarity in an image for a codebook generation in VQ. The mosaic image is interpolated via a traditional method, and applied scaling, blurring, phase-shifting and resampling are used to create a training data for the codebook. The characteristics of the training data are similar to those of an ideal image. Using such training data and approximation of an ideal codevector by a locally linear embedding (LLE)-based method increases the probability of finding a suitable codevector from the codebook. Even if we cannot find a good codevector in an ill-conditioned case, the error detection finds poorly estimated pixel values and replaces them with better restoration results by another demosaicing method.
Synthesis-Guided Partial Hierarchy Collapsing

This paper presents a framework for analyzing distribution of sequentially equivalent nodes in a hierarchical design. This information can be used for selectively collapsing hierarchical modules into 'super-modules' resulting in improved optimization and better placement decisions. Our framework is capable of comparing any two modules in the design hierarchy in terms of logic sharing. Our current implementation is based on ABC and Verific, and we are reporting preliminary result of our experiments.
Imitating play from game trajectories: Temporal difference learning versus preference learning

This work compares the learning of linear evaluation functions using preference learning versus least squares temporal difference learning, LSTD(λ), from samples of game trajectories. The game trajectories are taken from human competitions held by the French Othello Federation1. The raw board positions are used to create a linear evaluation function to illustrate the key difference between the two learning approaches. The results show that the policies learned, using exactly the same game trajectories, can be quite different. For the simple set of features used, preference learning produces policies that better capture the behaviour of expert players, and also lead to higher levels of play when compared to LSTD(λ).
Design considerations for bulk micromachined 6H-SiC high-G piezoresistive accelerometers

We report the utilization of key design parameters to simulate, batch-fabricate and evaluate first-generation single crystal 6H-SiC piezoresistive accelerometers for extreme impact, high electromagnetic fields (EM) and high temperature applications. The results from finite element analysis (FEA) of the selected design models were compared to evaluated prototypes. While FEA results predicted safe operation above 100,000-g's, preliminary experimental tests were performed up to 40,000-g's. Sensitivities ranging between 50 and 343 nV/g were measured. Non-linear behavior was observed over the shock range relative to the commercial accelerometer used as a benchmark. These initial results offer promise for the use of 6H-SiC accelerometers for extreme impact sensing in strong EM fields and temperature up to 600/spl deg/C that are beyond the capability of silicon.
Hybrid Codebook Model for Foreground Object Segmentation and Shadow/Highlight Removal

Real-time foreground object extraction is an important subject for computer vision applications. Model-based background subtraction methods have been used to extract the foreground objects. Different from previous methods, this paper introduces a hybrid codebook-based background subtraction method by combining the mixture of Gaussian (MOG) with the codebook (CB) method. We propose an ellipsoid CB model for modeling the dynamic background with highlight and shadow, and develop a modified shadow/ highlight removal method to overcome the influence of illumination change. Our method can avoid extracting the false foreground pixels (e.g., dark background) or missing the real foreground pixels (e.g., bright foreground). Finally, we have done two experiments to compare the performance of our method with the others based on [18] and the change detection benchmark dataset provided in CVPR 2011, respectively.
On the use of a Cooperative Neighbor Position Verification scheme to secure warning message dissemination in VANETs

Efficient schemes for warning message dissemination in vehicular ad hoc networks (VANETs) use context information collected by vehicles about their neighbor nodes to guide the dissemination process. These schemes maximize their performance when all the vehicles advertise correct information about their positions, and hence position errors may drastically reduce the performance of the dissemination process. We present a proactive Cooperative Neighbor Position and Verification (CNPV) protocol that detects nodes advertising false locations so as to mitigate the impact of adversarial users. We combine our mechanism with two warning dissemination schemes for VANETs, and demonstrate how these algorithms can benefit from the use of our security scheme in the presence of malicious nodes trying to exploit the inherent vulnerabilities of each algorithm.