Miracle-dz/UniFAR-FTUs huggingface dataset:
Further Research on Feature Selection and Classification Using Genetic Algorithms

This paper summarizes work on an approach that combines feature selection and data classiication using Genetic Algorithms. First, it describes our use of Genetic Algorithms combined with a K-nearest neighbor algorithm to optimize classiication by searching for an optimal feature weight-ing, essentially warping the feature space to coalesce individuals within groups and to separate groups from one another. This approach has proven especially useful with large data sets where standard feature selection techniques are computationally expensive. Second, it describes our implementation of the approach in a parallel processing environment, giving nearly linear speed-up in processing time. Third, it will summarize our present results in using the technique to discover the relative importance of features in large biological test sets. Finally, it will indicate areas for future research. 1 The Problem We live in the age of information where data is plentiful , to the extent that we are typically unable to process all of it usefully. Computer science has been challenged to discover approaches that can sort through the mountains of data available and discover the essential features needed to answer a speciic question. These approaches must be able to process large quantities of data, in reasonable time and in the presence of \noisy" data i.e., irrelevant or erroneous data. Consider a typical example in biology. Researchers in the Center for Microbial Ecology (CME) have selected soil samples from three environments found in agriculture. The environments were: near the roots of a crop (rhizosphere), away from the innuence of the crop roots (non-rhizosphere), and from a fallow eld (crop residue). The CME researchers wished to investigate whether samples from those three environments could be distinguished. In particular, they wanted to see if diversity decreased in the rhizosphere as a result of the symbiotic relationship between the roots and its near-neighbor microbes, and if so in what ways. Their rst experiments used the Biolog c test as the discriminator. Biolog consists of a plate of 96 wells, with a diierent substrate in each well. These sub-strates (various sugars, amino acids and other nutrients) are assimilated by some microbes and not by others. If the microbial sample processes the substrate in the well, that well changes color which can be recorded photometrically. Thus large numbers of samples can be processed and characterized based on the substrates they can assimilate. The CME researchers applied the Biolog test to 3 sets of 100 samples …
Functional network analysis of aging and Alzheimer's Disease: Results

This report, and the associated data files, present the results of a functional network analysis conducted upon a fMRI data set, with the aim of identifying differences, in various functional network properties, between a group of healthy young individuals, a group of healthy aged individuals, and a group of individuals diagnosed with probable to mild Alzheimer’s Disease. This report is intended as a complement to the PhD thesis Functional network analysis of aging and Alzheimer’s Disease, by Paul McCarthy, submitted for the degree of Doctor of Philosophy, at the University of Otago, Dunedin, New Zealand [12].
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach

Computer vision tasks are traditionally defined and evaluated using semantic categories. However, it is known to the field that semantic classes do not necessarily correspond to a unique visual class (e.g. inside and outside of a car). Furthermore, many of the feasible learning techniques at hand cannot model a visual class which appears consistent to the human eye. These problems have motivated the use of 1) Unsupervised or supervised clustering as a preprocessing step to identify the visual subclasses to be used in a mixture-of-experts learning regime. 2) Felzenszwalb et al. part model and other works model mixture assignment with latent variables which is optimized during learning 3) Highly non-linear classifiers which are inherently capable of modelling multi-modal input space but are inefficient at the test time. In this work, we promote an incremental view over the recognition of semantic classes with varied appearances. We propose an optimization technique which incrementally finds maximal visual subclasses in a regularized risk minimization framework. Our proposed approach unifies the clustering and classification steps in a single algorithm. The importance of this approach is its compliance with the classification via the fact that it does not need to know about the number of clusters, the representation and similarity measures used in pre-processing clustering methods a priori. Following this approach we show both qualitatively and quantitatively significant results. We show that the visual subclasses demonstrate a long tail distribution. Finally, we show that state of the art object detection methods (e.g. DPM) are unable to use the tails of this distribution comprising 50% of the training samples. In fact we show that DPM performance slightly increases on average by the removal of this half of the data.
Gauging the value of good data: Informational embodiment quantification

While there is increasing recognition of how relations of embodiment can be exploited for achievement of cognitive goals, we still lack any general method for formalizing the benefits that are then obtained, or for quantifying them. The present article describes a method that can be used to calculate the informational benefit obtained when embodiment becomes a vehicle for generation of ‘‘good data,’’ that is, data exhibiting behaviorally salient correlations.
Optimal Cloud Resource Auto-Scaling for Web Applications

In the on-demand cloud environment, web application providers have the potential to scale virtual resources up or down to achieve cost-effective outcomes. True elasticity and cost-effectiveness in the pay-per-use cloud business model, however, have not yet been achieved. To address this challenge, we propose a novel cloud resource auto-scaling scheme at the virtual machine (VM) level for web application providers. The scheme automatically predicts the number of web requests and discovers an optimal cloud resource demand with cost-latency trade-off. Based on this demand, the scheme makes a resource scaling decision that is up or down or NOP (no operation) in each time-unit re-allocation. We have implemented the scheme on the Amazon cloud platform and evaluated it using three real-world web log datasets. Our experiment results demonstrate that the proposed scheme achieves resource auto-scaling with an optimal cost-latency trade-off, as well as low SLA violations.
Frequency Analysis of the Splice Site Regions in Different Organisms

Summary We have carried out a comparative analysis of the sub-sequences of size six| ten at the (donor| acceptor) splice site regions of five different organisms. The frequency analysis of the unique sub-sequences at the donor and acceptor regions suggests that the distribution of their occurrence is approximately exponential. We have observed that the number of unique sub-sequences (occurring with different frequencies) at the donor region are less than at the acceptor, suggesting that the sub-sequences at the acceptor region are more variable. The sub-sequences with high percentage of occurrence (uniqueness) are considered to be highly involved in splicing. Our analysis suggests that sub-sequences of length ~6-8 nucleotides (nt) at the splice sites – with six bases in intron (including the two central, conserved dinucleotides) and two bases in exon are optimal for the efficient assembly and binding of the spliceosomal complex during the process of splicing. The score pattern obtained by the alignment of the nucleotides at the donor region with the acceptor and vice-versa also suggests that a single sub-sequence at the donor region have different degree of similarity with sub-sequences at the acceptor thus determining that the donor sub-sequences are more crucial in pairing with the corresponding acceptor sub-sequences during the process of splicing.
View Invariant 3D Video Watermarking Using Depth Based Embedding

In this paper, a video watermarking scheme is proposed to embed the watermarking with the depth sequences of the respective video frames for multi-view video plus depth (MVD) based 3D video sequences. To make the scheme invariant to 3D-HEVC compression attack, motion compensated temporal filtering (MCTF) is done over the video sequences to find motion-coherent connected pixels. Scale- invariant feature transform (SIFT) is done on the temporally filtered frame to make the proposed method view invariant. Moreover, embedding zone for watermarking has been selected in such a way that no watermark can be inserted in the foreground object to avoid perceptible artefacts. Also, a novel watermark embedding policy is used to insert the watermark with the depth of the 3D video to resist the view synthesis as well as video compression attack and temporal scalable attack on the depth sequences. A comprehensive set of experiments is carried out to justify the robustness of the proposed scheme.
Synthesis, Characterization, Molecular Docking, Cytotoxic and Antioxidant Activities of Di(indolyl)thiazolylpyrazoles

Some new di(indolyl)thiazolylpyrazoles were prepared from the synthetic intermediate E-1,3-di(1H-indol-3-yl) prop-2-en-1-one under ultrasonication and studied their cytotoxic and antioxidant activities. All the compounds were screened for in vitro cytotoxic activity on three cancer cell lines. The compound 7e exhibited appreciable anticancer activity on NCI-H1299, HCT-166 p53 and PC-3 cancer cell lines. The binding conformation of the target molecules was predicted by docking methodology to explain the biological activities. In fact, the docking studies indicated that could be used as possible leads for therapies against cancers. Amongst all the tested compounds dimethoxy substituted di(indolyl)thiazolylpyrazole (7i) displayed significant radical scavenging activity.
Staged model-driven generators: Shifting responsibility for code emission to embedded metaprograms

We focus on MDE tools generating source code, entire or partial, providing a basis for programmers to introduce custom system refinements and extensions. The latter may introduce two maintenance issues once code is freely edited: (i) if source tags are affected model reconstruction is broken; and (ii) code inserted without special tags is overwritten on regeneration. Additionally, little progress has been made in combining sources whose code originates from multiple generative tools. To address these issues we propose an alternative path. Instead of generating code MDE tools generate source fragments as abstract syntax trees (ASTs). Then, programmers deploy metaprogramming to manipulate, combine and insert code on-demand from ASTs with calls resembling macro invocations. The latter shifts responsibility for source code emission from MDE tools to embedded metaprograms and enables programmers control where the produced code is inserted and integrated. Moreover, it supports source regeneration and model reconstruction causing no maintenance issues since MDE tools produce non-editable ASTs. We validate our proposition with case studies involving a user-interface builder and a general purpose modeling tool.
Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition

We consider a duopoly pricing game with a unique Bertrand-Nash equilibrium. The high-price firm has a nonvanishing market share, however, and intuition suggests that observed prices may be positively related to this market share. This relationship is implied by a model in which players make noisy (logit) best responses to expected payoff differences. The resulting logit equilibrium model was used to design an experiment in which the high-price firm's market share varies. The model accurately predicts the final-period price averages. A naive learning model predicts the observed differences in the time paths of average prices. Copyright Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association
Oxidative Stress in Post-Acute Ischemic Stroke Patients: Relevance of Early Intensive Neurorehabilitation

Oxidative stress was investigated in 10 post-acute stroke patients (median age 76.40 ± 2.09 years) assigned to in-hospital neurorehabilitation within 7 days of onset of a first-ever ischemic stroke. Assessments were made before and after neurorehabilitation. Outcome measures were the National Institutes of Health Stroke Scale (NIHSS), the Barthel Index (BI), the Katz Index (KI), the modified Rankin Scale (mRS) and the modified PULSES profile. Differences between scores evaluated before and after neurorehabilitation were expressed as Δ scores. Oxidative stress was assessed by measuring plasma amount of total peroxides by-products, nitrite/nitrate metabolites (NOx), total plasma antioxidant capacity (TEAC), Cu/Zn Superoxide Dismutase (Cu/ZnSOD), and serum urate concentration. Ten healthy volunteers (median age 74.30 ± 3.03 years) were recruited as controls for comparison of oxidative markers. Neurorehabilitation was associated with an improvement (P<0.05) of all the outcome measures except the mRS score. ΔPULSES correlated negatively with ΔBI scores (r=-0.656; P=0.040). Higher plasma levels of total peroxidative by-products and NOx metabolites were found in stroke patients than in controls before neurorehabilitation (P<0.05). Their values decreased in stroke patients after neurorehabilitation and were higher than those measured in controls (P<0.05). TEAC, Cu/Zn SOD and serum urate levels were lower in stroke patients than in controls before neurorehabilitation (P<0.05). Their values increased after neurorehabilitation so that no differences were observed comparing these values with those found in controls. Changes observed before and after neurorehabilitation in plasma NOx amount (ΔNOx) correlated positively with ΔNIHSS scores (r=0.675; P=0.032) and negatively with ΔPULSES (r=- 0.845; P=0.002). Cu/ZnSOD changes found before and after neurorehabilitation (ΔCu/Zn SOD) and ΔKI correlated positively (r=0.713; P=0.021). Our results showed that intensive neurorehabilitation may modulate oxidative stress and suggest its effectiveness in redox-mediated plasticity processes involved in the recovery of stroke-induced neurological deficits.
An Exact Algorithm for F-Measure Maximization

The F-measure, originally introduced in information retrieval, is nowadays routinely used as a performance metric for problems such as binary classification, multi-label classification, and structured output prediction. Optimizing this measure remains a statistically and computationally challenging problem, since no closed-form maximizer exists. Current algorithms are approximate and typically rely on additional assumptions regarding the statistical distribution of the binary response variables. In this paper, we present an algorithm which is not only computationally efficient but also exact, regardless of the underlying distribution. The algorithm requires only a quadratic number of parameters of the joint distribution (with respect to the number of binary responses). We illustrate its practical performance by means of experimental results for multi-label classification.
Assessing the Challenge of Fine-Grained Named Entity Recognition and Classification

Named Entity Recognition and Classification (NERC) is a well-studied NLP task typically focused on coarse-grained named entity (NE) classes. NERC for more fine-grained semantic NE classes has not been systematically studied. This paper quantifies the difficulty of fine-grained NERC (FG-NERC) when performed at large scale on the people domain. We apply unsupervised acquisition methods to construct a gold standard dataset for FG-NERC. This dataset is used to benchmark methods for classifying NEs at various levels of fine-grainedness using classical NERC techniques and global contextual information inspired from Word Sense Disambiguation approaches. Our results indicate high difficulty of the task and provide a 'strong' baseline for future research.
Working Memory and Anticipatory Set Modulate Midbrain and Putamen Activity

To behave adaptively, an organism must balance the accurate maintenance of information stored in working memory with the ability to update that information when the context changes. This trade-off between fidelity and flexibility may depend upon the anticipated likelihood that updating will be necessary. To address the neurobiological basis of anticipatory optimization, we acquired functional magnetic resonance imaging data, while healthy human subjects performed a modified delayed-response task. This task used cues that predicted memory updating, with high or low probability, followed by a contingent updating or maintenance event. This enabled us to compare behavior and neuronal activity during conditions in which updating was anticipated with high and low probability, and measure responses to expected and unexpected memory updating. Based on the known role of dopamine in cognitive flexibility and working memory updating, we hypothesized that differences in anticipatory set would be manifest in the dopaminergic midbrain and striatum. Consistent with our predictions, we identified sustained activation in the dopaminergic midbrain and the striatum, associated with anticipations of high versus low updating probability. We also found that this anticipatory factor affected neural responses to subsequent updating processes, which suppressed, rather than elevated, midbrain and striatal activity. Our study addresses for the first time an important and hitherto understudied aspect of working memory.
A Sensitive Period for the Development of the Central Auditory System in Children with Cochlear Implants: Implications for Age of Implantation

Objective The aim of the present experiment was to assess the consequences of cochlear implantation at different ages on the development of the human central auditory system. Design Our measure of the maturity of central auditory pathways was the latency of the P1 cortical auditory evoked potential. Because P1 latencies vary as a function of chronological age, they can be used to infer the maturational status of auditory pathways in congenitally deafened children who regain hearing after being fit with a cochlear implant. We examined the development of P1 response latencies in 104 congenitally deaf children who had been fit with cochlear implants at ages ranging from 1.3 yr to 17.5 yr and three congenitally deaf adults. The independent variable was the duration of deafness before cochlear implantation. The dependent variable was the latency of the P1 cortical auditory evoked potential. Results A comparison of P1 latencies in implanted children with those of age-matched normal-hearing peers revealed that implanted children with the longest period of auditory deprivation before implantation—7 or more yr—had abnormal cortical response latencies to speech. Implanted children with the shortest period of auditory deprivation—approximately 3.5 yr or less—evidenced age-appropriate latency responses within 6 mo after the onset of electrical stimulation. Conclusions Our data suggest that in the absence of normal stimulation there is a sensitive period of about 3.5 yr during which the human central auditory system remains maximally plastic. Plasticity remains in some, but not all children until approximately age 7. After age 7, plasticity is greatly reduced. These data may be relevant to the issue of when best to place a cochlear implant in a congenitally deaf child.
Reprogramming Metabolism with Metformin Improves Tumor Oxygenation and Radiotherapy Response

Purpose: Tumor hypoxia is a negative prognostic factor in multiple cancers, due in part to its role in causing resistance to radiotherapy. Hypoxia arises in tumor regions distal to blood vessels as oxygen is consumed by more proximal tumor cells. Reducing the rate of oxygen consumption is therefore a potential strategy to reduce tumor hypoxia. We hypothesized that the anti-diabetic drug metformin, which reduces oxygen consumption through inhibition of mitochondrial complex I, would improve radiation response by increasing tumor oxygenation. Experimental Design: Tumor hypoxia was measured in xenografts before and after metformin treatment using 2-nitroimidazole hypoxia markers quantified by immunohistochemistry (IHC), flow cytometry, and positron emission tomography (PET) imaging. Radiation response was determined by tumor growth delay and clonogenic survival in xenografts with and without administration of metformin. The impact of metformin use on outcome was assessed in 504 patients with localized prostate cancer treated with curative-intent, image-guided radiotherapy (IGRT) from 1996 to 2012. Three-year biochemical relapse-free rates were assessed using the Kaplan–Meier method. Results: Metformin treatment significantly improved tumor oxygenation in two xenograft models as measured by IHC, flow cytometry, and PET imaging. Metformin also led to improved radiotherapy responses when mice were administered metformin immediately before irradiation. Clinically, metformin use was associated with an independent and significant decrease in early biochemical relapse rates (P = 0.0106). Conclusion: Our data demonstrate that metformin can improve tumor oxygenation and response to radiotherapy. Our study suggests that metformin may represent an effective and inexpensive means to improve radiotherapy outcome with an optimal therapeutic ratio. Clin Cancer Res; 19(24); 6741–50. ©2013 AACR.
Differential Blood and Mucosal Immune Responses against an HIV-1 Vaccine Administered via Inguinal or Deltoid Injection

Mucosal immunity is central to sexual transmission and overall pathogenesis of HIV-1 infection, but the ability of vaccines to induce immune responses in mucosal tissue compartments is poorly defined. Because macaque vaccine studies suggest that inguinal (versus limb) vaccination may better target sexually-exposed mucosa, we performed a randomized, double-blinded, placebo-controlled Phase I trial in HIV-1-uninfected volunteers, using the recombinant Canarypox (CP) vaccine vCP205 delivered by different routes. 12 persons received vaccine and 6 received placebo, divided evenly between deltoid-intramuscular (deltoid-IM) or inguinal-subcutaneous (inguinal-SC) injection routes. The most significant safety events were injection site reactions (Grade 3) in one inguinal vaccinee. CP-specific antibodies were detected in the blood of all 12 vaccinees by Day 24, while HIV-1-specific antibodies were observed in the blood and gut mucosa of 1/9 and 4/9 evaluated vaccinees respectively, with gut antibodies appearing earlier in inguinal vaccinees (24–180 versus 180–365 days). HIV-1-specific CD8+ T lymphocytes (CTLs) were observed in 7/12 vaccinees, and blood and gut targeting were distinct. Within blood, both deltoid and inguinal responders had detectable CTL responses by 17–24 days; inguinal responders had early responses (within 10 days) while deltoid responders had later responses (24–180 days) in gut mucosa. Our results demonstrate relative safety of inguinal vaccination and qualitative or quantitative compartmentalization of immune responses between blood and gut mucosa, and highlight the importance of not only evaluating early blood responses to HIV-1 vaccines but also mucosal responses over time. Trial Registration ClinicalTrials.gov NCT00076817