Miracle-dz/UniFAR-FTUs huggingface dataset:
Validating an Efficient Method to Quantify Motion Sickness

Objective: Motion sickness (MS) can be a debilitating side effect associated with motion in real or virtual environments. We analyzed the effect of expectancy on MS and propose and validate a fast and simple MS measure. Background: Several questionnaires measure MS before or after stimulus presentation, but no satisfactory tool has been established to quickly capture MS data during exposure. To fill this gap, we introduce the Fast MS Scale (FMS), a verbal rating scale ranging from zero (no sickness at all) to 20 (frank sickness). Also, little is known about the role of expectancy effects in MS studies. We conducted an experiment that addressed this issue. Method: For this study, 126 volunteers participated in two experiments. During stimulus presentation, participants had to verbally rate the severity of MS every minute before filling in the Simulator Sickness Questionnaire (SSQ). To measure expectancy effects, participants were separated into three groups with either positive, negative, or neutral expectations. Results: We compared the verbal ratings with the SSQ scores. Pearson correlations were high for both the SSQ total score (r = .785) and the nausea subscore (r = .828). No expectancy effects were found. Conclusion: The FMS is a fast and valid method to obtain MS data. It offers the possibility to record MS during stimulus presentation and to capture its time course. We found expectancy not to play a crucial role in MS. However, the FMS has some limitations. Application: The FMS offers improved MS measurement. It is fast and efficient and can be performed online in environments such as virtual reality.
Prospects for Early Detection of Alzheimers Disease from Serial MR Images in Transgenic Mouse Models

The existing literature on the magnetic resonance imaging of murine models of Alzheimers disease is reviewed. Particular attention is paid to the possibilities for the early detection of the disease. To this effect, not only are relaxometric and volumetric approaches discussed, but also mathematical models for plaque distribution and aggregation. Image analysis plays a prominent role in this line of research, as stochastic image models and texture analysis have shown some success in the classification of subjects affected by Alzheimers disease. It is concluded that relaxometric approaches seem to be a promising candidate for the task at hand, especially when combined with sophisticated image analysis, and when data from more than one time-point is available. There have been few longitudinal studies of mice models so far, so this direction of research warrants future efforts.
Low-energy radix-2 serial and parallel FFT designs

The push for portable electronics for communication and biomedical applications has accelerated the growing momentum for high performance and low energy hardware implementations of the Fast Fourier Transform (FFT). This work presents several new hardware implementations of the radix-2 FFT algorithms that take advantage of intermittent data and parallelism to reduce the energy per FFT. In the modified serial design, by using a low-power control memory and a pipelined data look-ahead controller to optimize processing of sequences of data with zeros, up to 45% of energy savings are achieved as compared to the baseline design. Two fully parallel FFTs with different datapaths are also developed based on a FFT flow diagram with the same geometry in each stage. Energy savings of up to 90% (an order of magnitude) are achieved as compared to the baseline design. These results are demonstrated through post-layout and parasitic extraction Nanosim simulations with 90nm standard cell libraries. Thesis Supervisor: Anantha P. Chandrakasan Title: Joseph F. and Nancy P. Keithley Professor of Electrical Engineering
Berberine Restricts Coxsackievirus B Type 3 Replication via Inhibition of c-Jun N-Terminal Kinase (JNK) and p38 MAPK Activation In Vitro

Background At present, the treatment of coxsackievirus-induced myocarditis remains difficult. Berberine (BBR), an isoquinoline alkaloid isolated from traditional medicine herbs, exhibits significant anti-viral efficacy against various viruses. However, the underlying mechanism by which BBR controls CVB3 infection has not yet been reported. The purpose of this study was to investigate the anti-viral efficacy of BBR against CVB3 infection and its mechanism. Material/Methods In our experiments, the protein levels of VP1 and MAPKs signal pathway were measured by Western blot. The mRNA level of VP1 was measured by RT-PCR. The virus titers were determined by TCID50 assay. Results We found that BBR treatment significantly decreased CVB3 replication in HeLa cells. In addition, the BBR treatment reduced the phosphorylation levels of JNK and p38 MAPK upon CVB3 infection in both HeLa cells and primary rat myocardial cells. Conclusions Taken together, these results suggest that BBR inhibits CVB3 replication through the suppression of JNK and p38 MAPK activation, shedding new light on the investigation of therapeutic strategies against CVB3-induced viral myocarditis.
Radiofrequency thermal treatment with chemoradiotherapy for advanced rectal cancer

We previously reported that patients with a clinical complete response (CR) following radiofrequency thermal treatment exhibit significantly increased body temperature compared with other groups, whereas patients with a clinical partial response or stable disease depended on the absence or presence of output limiting symptoms. The aim of this study was to evaluate the correlation among treatment response, Hidaka radiofrequency (RF) output classification (HROC: termed by us) and changes in body temperature. From December 2011 to January 2014, 51 consecutive rectal cancer cases were included in this study. All patients underwent 5 RF thermal treatments with concurrent chemoradiation. Patients were classified into three groups based on HROC: with ≤9, 10–16, and ≥17 points, calculated as the sum total points of five treatments. Thirty-three patients received surgery 8 weeks after treatment, and among them, 32 resected specimens were evaluated for histological response. Eighteen patients did not undergo surgery, five because of progressive disease (PD) and 13 refused because of permanent colostomy. We demonstrated that good local control (ypCR + CR + CRPD) was observed in 32.7% of cases in this study. Pathological complete response (ypCR) was observed in 15.7% of the total 51 patients and in 24.2% of the 33 patients who underwent surgery. All ypCR cases had ≥10 points in the HROC, but there were no patients with ypCR among those with ≤9 points in the HROC. Standardization of RF thermal treatment was performed safely, and two types of patients were identified: those without or with increased temperatures, who consequently showed no or some benefit, respectively, for similar RF output thermal treatment. We propose that the HROC is beneficial for evaluating the efficacy of RF thermal treatment with chemoradiation for rectal cancer, and the thermoregulation control mechanism in individual patients may be pivotal in predicting the response to RF thermal treatment.
Uninterrupted Sedentary Behavior Downregulates BRCA1 Gene Expression

BRCA1 mutation carriers face a high lifetime risk of developing breast cancer. Physical activity induces broad transcriptional changes, and multiple studies have documented its beneficial effects across cancers. Because haploinsufficiency predisposes to breast cancer in these women, factors that increase BRCA1 levels may mitigate the effect of the mutation. Whether physical activity modulates BRCA1 expression and whether lifestyle factors could benefit women with a mutation remain unclear. The objective of this study was to systematically evaluate whether physical activity or sedentary behavior affects BRCA1 mRNA expression. Activity levels were assessed in 50 female participants (14 BRCA1 mutation carriers and 36 noncarriers) using the GT3X Actigraph accelerometer, and BRCA1 mRNA expression was quantified from peripheral blood lymphocytes using the Nanostring nCounter Analysis System. There was a significant negative correlation between the longest sedentary bout and BRCA1 mRNA expression (ρ = –0.32; P = 0.02). Women below the median for the longest sedentary bout had significantly higher BRCA1 mRNA levels compared with women above the median (161 vs. 132 counts; P = 0.04; one-sided Mann–Whitney U test). There was no significant relationship between mean metabolic equivalents of task rate or mean sedentary time and BRCA1 mRNA expression (Spearman correlation P ≥ 0.75; P ≥ 0.14; Mann–Whitney U test). These findings suggest that prolonged periods of sedentary behavior are associated with significantly lower BRCA1 mRNA expression. Whether this translates into a potentially more harmful effect in BRCA1 mutation carriers warrants further investigation. Cancer Prev Res; 9(1); 83–88. ©2015 AACR.
Confidence Estimation in Structured Prediction

Structured classification tasks such as sequence labeling and dependency parsing have seen much interest by the Natural Language Processing and the machine learning communities. Several online learning algorithms were adapted for structured tasks such as Perceptron, Passive- Aggressive and the recently introduced Confidence-Weighted learning . These online algorithms are easy to implement, fast to train and yield state-of-the-art performance. However, unlike probabilistic models like Hidden Markov Model and Conditional random fields, these methods generate models that output merely a prediction with no additional information regarding confidence in the correctness of the output. In this work we fill the gap proposing few alternatives to compute the confidence in the output of non-probabilistic algorithms.We show how to compute confidence estimates in the prediction such that the confidence reflects the probability that the word is labeled correctly. We then show how to use our methods to detect mislabeled words, trade recall for precision and active learning. We evaluate our methods on four noun-phrase chunking and named entity recognition sequence labeling tasks, and on dependency parsing for 14 languages.
Piecewise-Linear Expansions for Nonlinear Active Noise Control

Functional link artificial neural networks exploiting orthogonal functional expansions have been recently proposed as efficient structures for nonlinear active noise control. In this paper it will be shown that simple piecewise-linear expansions can effectively replace trigonometric and orthogonal polynomial expansions. The controller is adapted using an extension of the affine projection algorithm we recently proposed and studied with reference to its transient and steady-state behavior
Entropy of the Randall-Sundrum black brane in the brick-wall method

We calculate the entropy of the black brane of the Randall-Sundrum(RS) model by using the brick-wall method. The modes along the extra dimension are semi-classically quantized on the orbifold. The number of modes in the extra dimension is remarkably given as a simple form with the help of the RS mass relation, and then the entropy for the scalar modes in the fivedimensional spacetime is described by the two-dimensional area of the black brane. PACS : 04.60. -m, 04.62. +v, 04.70. Dy Typeset using REVTEX ∗electronic address:wtkim@ccs.sogang. ac.kr †electronic address:john5@string.sogang. ac.kr ‡electronic address:yjpark@ccs.sogang. ac.kr 1
K-Nearest Neighbors directed synthetic images injection

It is widely acknowledged that good performances of content-based image retrieval systems can be attained by adopting relevance feedback mechanisms. One of the main difficulties in exploiting relevance information is the availability of few relevant images, as users typically label a few dozen of images, the majority of them often being non-relevant to user's needs. In order to boost the learning capabilities of relevance feedback techniques, this paper proposes the creation of points in the feature space which can be considered as representation of relevant images. The new points are generated taking into account not only the available relevant points in the feature space, but also the relative positions of non-relevant ones. This approach has been tested on a relevance feedback technique, based on the Nearest-Neighbor classification paradigm. Reported experiments show the effectiveness of the proposed technique relatively to precision and recall.
Psychosocial comparison of carers and noncarers

Abstract Limited exploration of young carers’ (YCs) impacts includes noncaregivers, thus reducing our understanding of YCs’ unique characteristics. Secondary comparative analyses of 124 YCs (n = 72 females, Mage = 12) and 124 noncarers were conducted to examine family, individual, and social contexts. Statistical analyses revealed YCs were different from non-YCs, suggesting caregiving could be a contributing factor in altering temperament and family dynamics as well as lowering self-esteem and raising the incidence of depressive symptoms. A comprehensive YC profile resulted from the findings. This profile may help promote identification and recognition of YCs.
Analysis of Dysfluencies by Computational Intelligence

The work presents a contemporary perspective of the field of dysfluent speech processing. It deals with suitable approaches of computational intelligence for speech analysis. It presents computational intelligence classifiers, based on probabilistic decision theory and classifiers based on cost function optimization. This work deals with common model of automatic speech recognition and explains the historical and the current speech feature extraction methods. It introduces definitions and known symptoms of communication disorder, stuttering, then selects the symptoms that are the subject of our interest and describes the state-of-the-art of computer analysis in stuttered speech. In our experiments we deal with Artificial Neural Networks and with Support Vector Machines, to solve the task of recognizing simple dysfluencies. We were looking for inspiration in data mining and in bioinformatics, to handle the problem of detecting complex dysfluent events. The proposed new methodology for processing long dysfluent speech intervals was based on the speech signal transformed into symbolic sequences. We present new algorithms, able to automatically adapt the length of the analysis window and detect complex dysfluencies. Moreover we found that dysfluencies have recursive property which was used in developing our algorithms. We derived new features of dysfluencies from yields of our new algorithms. We borrow inspiration from the field of video sequence analysis, to apply and derive new functions designated to detect specific dysfluent events. The new methodology for symbolic analysis of dysfluent speech, the new features of complex dysfluencies and the new algorithms were related to established methodologies and statistically compared. ∗Recommended by thesis supervisor: Prof. Jǐŕı Posṕıchal. Defended at Faculty of Informatics and Information Technologies, Slovak University of Technology in Bratislava on June 25, 2014. c © Copyright 2014. All rights reserved. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from STU Press, Vazovova 5, 811 07 Bratislava, Slovakia. Pálfy, J. Analysis of Dysfluencies by Computational Intelligence. Information Sciences and Technologies Bulletin of the ACM Slovakia, Vol. 6, No. 2 (2014) 45-58
Towards a Distributional Semantic Web Stack

The capacity of distributional semantic models (DSMs) to discover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.
Impact of inductance and routing orientation on timing performances of coupled interconnect lines

As the interconnect lines play an increasingly dominant role in determining circuit performance, the dynamic delay variation due to the switching activity of neighboring lines has to be accurately characterized. The goal of this work is to simulate the effect of inductance and routing orientation and then to investigate their effects on timing performances by considering three configurations of three parallel coupled interconnects. For a Deep-Sub-Micron process, we show that when analyzing VLSI circuits, if standard distributed RC models are used, and inductive effects and routing orientation are ignored, large errors can occur in the prediction and evaluation of the circuit behaviour. Both affect greatly circuit performances.
Endocannabinoid-Mediated Long-Term Depression in the Avian Midbrain Expressed Presynaptically and Postsynaptically

Here, we examined long-term synaptic plasticity in the avian auditory midbrain, a region involved in experience-dependent learning. We found that coactivation of N-methyl-d-aspartate receptors (NMDAR) and type 1 cannabinoid receptors (CB1R) induces long-term depression (LTD) at the synapse between the central shell and the external portion of the inferior colliculus of the chicken. Although endocannabinoids are commonly thought of as presynaptic modulators, recent reports have suggested that they can also modulate the postsynaptic site. In the avian midbrain, we found that LTD is mediated by both presynaptic and postsynaptic changes. The presynaptic mechanism consists of a decrease in neurotransmitter release, whereas a depression of NMDAR-mediated current takes place on the postsynaptic side. Both the presynaptic and the postsynaptic effects depend on CB1R activation. The reduction of postsynaptic NMDAR currents represents a novel role of endocannabinoids in synaptic modulation.
Catalan numbers and relations

We define the notion of a Catalan pair (which is a pair of binary relations (S,R) satisfying certain axioms) with the aim of giving a common language to most of the combinatorial interpretations of Catalan numbers. We show, in particular, that the second component R uniquely determines the pair, and we give a characterization of R in terms of forbidden configurations. We also propose some generalizations of Catalan pairs arising from some slight modifications of (some of the) axioms.
Thermal-Aware Scheduling in Green Data Centers

Data centers can go green by saving electricity in two major areas: computing and cooling. Servers in data centers require a constant supply of cold air from on-site cooling mechanisms for reliability. An increased computational load makes servers dissipate more power as heat and eventually amplifies the cooling load. In thermal-aware scheduling, computations are scheduled with the objective of reducing the data-center-wide thermal gradient, hotspots, and cooling magnitude. Complemented by heat modeling and thermal-aware monitoring and profiling, this scheduling is energy efficient and economical. A survey is presented henceforth of thermal-ware scheduling and associated techniques for green data centers.
Aligning parallel texts with InterText

InterText is a flexible manager and editor for alignment of parallel texts aimed both at individual and collaborative creation of parallel corpora of any size or translational memories. It is available in two versions: as a multi-user server application with a web-based interface and as a native desktop application for personal use. Both versions are able to cooperate with each other. InterText can process plain text or custom XML documents, deploy existing automatic aligners and provide a comfortable interface for manual post-alignment correction of both the alignment and the text contents and segmentation of the documents. One language version may be aligned with several other versions (using stand-off alignment) and the application ensures consistency between them. The server version supports different user levels and privileges and it can also track changes made to the texts for easier supervision. It also allows for batch import, alignment and export and can be connected to other tools and scripts for better integration in a more complex project workflow.
ARMor: Fully verified software fault isolation

We have designed and implemented ARMor, a system that uses software fault isolation (SFI) to sandbox application code running on small embedded processors. Sandboxing can be used to protect components such as the RTOS and critical control loops from other, less-trusted components. ARMor guarantees memory safety and control flow integrity; it works by rewriting a binary to put a check in front of every potentially dangerous operation. We formally and automatically verify that an ARMored application respects the SFI safety properties using the HOL theorem prover. Thus, ARMor provides strong isolation guarantees and has an exceptionally small trusted computing base—there is no trusted compiler, binary rewriter, verifier, or operating system.
Protein-protein interaction extraction from bio-literature with compact features and data sampling strategy

A large number of protein-protein interactions (PPIs) have buried in massive biomedical articles published over the years. This leads to the development of automatic PPI extraction methods. However, existing methods based on supervised machine learning still face some challenges: (1) the feature space exploited in these methods is very sparse; and (2) the data used for training are imbalanced with respect to categories to be classified. In this paper, we first construct rich and compact features to alleviate the issue of feature sparseness. With these features, our method outperforms baselines by up to an F-score of 9.58% on the original AIMed corpus. Furthermore, we propose a data sampling strategy based on under-sampling to address the class imbalance problem. In order to re-balance data distribution, samples of the majority class are removed according to the prediction results iteratively. By this means, our method achieves a further 2.49% improvement in F-score on the original AIMed corpus.