Miracle-dz/UniFAR-FTUs huggingface dataset:
M ay 2 00 6 Nodes of the Gap Function and Anomalies in Thermodynamic Properties of Superfluid 3

Departures of thermodynamic properties of three-dimensional superfluid 3He from the predictions of BCS theory are analyzed. Attention is focused on deviations of the ratios ∆(T = 0)/Tc and [Cs(Tc) − Cn(Tc)] /Cn(Tc) from their BCS values, where ∆(T = 0) is the pairing gap at zero temperature, Tc is the critical temperature, and Cs and Cn are the superfluid and normal specific heats. We attribute these deviations to the momentum dependence of the gap function ∆(p), which becomes well pronounced when this function has a pair of nodes lying on either side of the Fermi surface. We demonstrate that such a situation arises if the P -wave pairing interaction V(p1, p2), evaluated at the Fermi surface, has a sign opposite to that anticipated in B CS theory. Taking account of the momentum structure of the gap function, we derive a closed relation between the two ratios that contains no adjustable parameters and agrees with the experimental data. Some important features of the effective pairing interaction are inferred from the analysis.
Resistance to RevM10 inhibition reflects a conformational switch in the HIV-1 Rev response element

Nuclear export of certain HIV-1 mRNAs requires an interaction between the viral Rev protein and the Rev response element (RRE), a structured element located in the Env region of its RNA genome. This interaction is an attractive target for both drug design and gene therapy, exemplified by RevM10, a transdominant negative protein that, when introduced into host cells, disrupts viral mRNA export. However, two silent G->A mutations in the RRE (RRE61) confer RevM10 resistance, which prompted us to examine RRE structure using a novel chemical probing strategy. Variations in region III/IV/V of mutant RNAs suggest a stepwise rearrangement to RevM10 resistance. Mass spectrometry was used to directly assess Rev “loading” onto RRE and its variants, indicating that this is unaffected by RNA structural changes. Similarity in chemical footprints with mutant protein implicates additional host factors in RevM10 resistance.
Improving Female Breast Cancer Prognosis by Means of Fuzzy Rule Induction with Artificial Immune Systems

Breast cancer is the second most common cause of deaths from cancer among women in the United States. Even if significant steps have been made in the field of cancer treatment there's still room for investigation when it comes to the modeling of metastatic behavior of tumors. In particular over-treatment avoidance of patient is currently a challenging area of research due to the positive effects it can have patients' quality of life and clinical costs management. In this paper we propose a novel approach to gene signature finding aimed at improving predic- tion accuracy of the tumor recurrence. Our approach lays on a novel computational paradigm, namely Artificial Immune Sys- tems (AIS). Based on AIS, our algorithm, IFRAIS (Induction of Fuzzy Rules with Artificial Immune Systems) mines the high density array data in order to extract useful knowledge, in the form of "IF-THEN" rules, easily interpretable by physicians and able to improve prediction accuracy for tumor recurrence.
Automatic Configuration of Services for Security , Bandwidth , Throughput , and Availability

The process of efficiently deploying a complex system of services on a complex network of servers is tedious and error prone, with many properties to check and many possibilities to examine. Automated tools are needed to turn this into a humanly tractable problem. We present a precise model of a service-oriented computing system that allows many important configuration properties to be defined and optimized for, including throughput, network bandwidth, security and availability. We transform this model into a system of constraints that can then be solved using mathematical and constraint programming yielding an optimal system configuration that meets all the stated requirements. We have implemented this in OPL and have used it to generate optimal configurations for realistic systems with tens of services running on hundreds of servers communicating on multiple subnets.
Understanding User Instructions by Utilizing Open Knowledge for Service Robots

Understanding user instructions in natural language is an active research topic in AI and robotics. Typically, natural user instructions are high-level and can be reduced into low-level tasks expressed in common verbs (e.g., `take', `get', `put'). For robots understanding such instructions, one of the key challenges is to process high-level user instructions and achieve the specified tasks with robots' primitive actions. To address this, we propose novel algorithms by utilizing semantic roles of common verbs defined in semantic dictionaries and integrating multiple open knowledge to generate task plans. Specifically, we present a new method for matching and recovering semantics of user instructions and a novel task planner that exploits functional knowledge of robot's action model. To verify and evaluate our approach, we implemented a prototype system using knowledge from several open resources. Experiments on our system confirmed the correctness and efficiency of our algorithms. Notably, our system has been deployed in the KeJia robot, which participated the annual RoboCup@Home competitions in the past three years and achieved encouragingly high scores in the benchmark tests.
MAC Level Performance Comparison of Distributed and Adjacent OFDMA Subchannels

which are used in the IEEE 802.16 standard. The subchannels are either distributed or adjacent in the frequency channel. The analysis is focused on the tradeoff between accuracy in the SINR estimation due to time-correlated fading processes and the exploitation of diversity. The adjacent subchannel scheme provides a high degree of diversity in the subchannel quality compared to the distributed approach. The reason for that is the averaging effect of the latter scheme. At the same time that effect is also responsible for the more accurate SINR estimation in the distributed subchannel scheme. The performance comparison is conducted for the downlink in a multi user reference scenario with mobility.
Frequency Optimization of Conical Shells under Mass Equality Constraint

As a major structural dynamic criterion in designing thin shells, the fundamental frequency is maximized for a specified total structural mass. The general optimization problem of metallic conical shell structures with clamped / free ends has been treated formally among all other types of boundary conditions. Both stiffened and unstiffened constructions are examined with the effective design variables selected to be the shell thickness distribution and the number, locations and size of the attached ring stiffeners. Structural analysis is based upon Donnell-Mushtari shell theory and an analytical approach based on Rayleigh-Ritz method has been implemented for calculating the natural vibration characteristics of the different shell types. The final optimization problem is formulated as a nonlinear mathematical programming problem solved by invoking the Matlab optimization toolbox routines, which implement the interior penalty function technique and interact with eigenvalue calculation routines. The proposed mathematical model has succeeded in maximizing the fundamental frequency without the penalty of increasing the total structural mass. Results show that the approach used in this study is efficient and produces designs having improved dynamic performance as compared with a known baseline design.
Contralateral Breast Symmetrisation in Immediate Prosthetic Breast Reconstruction after Unilateral Nipple-Sparing Mastectomy: The Tailored Reduction/Augmentation Mammaplasty

Background In the literature on nipple-sparing mastectomy (NSM) with one-stage immediate implant reconstruction, contralateral symmetrisation has drawn little attention, with many surgeons still performing standard cosmetic mammaplasty procedures. However, standard implant-based mammaplasty usually does not result in proper symmetry with the mastectomy side, especially regarding breast projection, overall shape, and volume distribution. Methods We retrospectively reviewed 19 consecutive patients undergoing unilateral NSM with immediate prosthetic reconstruction and contralateral simultaneous symmetrisation by using the tailored reduction/augmentation mammaplasty technique between June 2012 and August 2013. Results The average follow-up time was 13 months (range, 10-24 months). No major complications, such as infection, haematoma, and nipple-areola complex necrosis, were experienced. Conclusions Our experience suggests that simultaneous contralateral symmetrisation with tailored reduction/augmentation mammaplasty after unilateral immediate implant reconstruction after NSM facilitates durable and pleasant symmetric outcomes.
A procedure for assessing GO annotation consistency

MOTIVATION The Gene Ontology (GO) is widely used to annotate molecular attributes of genes and gene products. Multiple groups undertaking functional annotations of genomes contribute their annotation sets to the GO database resource and these data are subsequently used in comparative functional analysis research. Although GO curators adhere to the same protocols and standards while assigning GO annotations, the specific procedure followed by each annotation group can vary. Since differences in application of annotation standards would dilute the effectiveness of comparative analysis, methods for assessing annotation consistency are essential. The development of methodologies that are broadly applicable for the assessment of GO annotation consistency is an important issue for the comparative genomics community. RESULTS We have developed a methodology for assessing the consistency of GO annotations provided by different annotation groups. The method is completely general and can be applied to compare any two sets of GO annotations. This is the first attempt to assess cross-species GO annotation consistency. Our method compares annotation sets utilizing the hierarchical structure of the GO to compare GO annotations between orthologous gene pairs. The method produces a report on the annotation consistency and inconsistency for each orthologous pair. We present results obtained by comparing GO annotations for mouse and human gene sets. AVAILABILITY The complete current MGI_GOA GO annotation consistency report is available online at http://www.spatial. maine.edu/~mdolan/
Chondrogenically Tuned Expansion Enhances the Cartilaginous Matrix-Forming Capabilities of Primary, Adult, Leporine Chondrocytes

When expanded through passage, chondrocytes lose their ability to produce high-quality cartilaginous matrix. This study attempts to improve the properties of constructs formed with expanded chondrocytes through alterations in the expansion protocol and the ratio of primary to expanded chondrocytes used to form cartilage constructs. A chondrogenically tuned expansion protocol provided similar monolayer growth rates as those obtained using serum-containing medium and enhanced cartilaginous properties of resultant constructs. Various ratios of primary to chondrogenically expanded chondrocytes were then self-assembled to form neocartilage. Biochemical analysis showed that constructs formed with only expanded cells had twice the GAG per wet weight and collagen II/collagen I ratio compared to constructs formed with primary chondrocytes. Biomechanically, compressive properties of constructs formed with only passaged cells matched the instantaneous modulus and exceeded the relaxation modulus of constructs formed with only primary cells. These counterintuitive results show that, by applying proper expansion and three-dimensional culture techniques, the cartilage-forming potential of adult chondrocytes expanded through passage can be enhanced over that of primary cells.
A continuous user authentication scheme for mobile devices

Face and touch modalities have independently been shown to yield promising results for continuous user authentication. In this study, we present a novel framework that combines these modalities. We show a stacked classifier approach can be used to improve the continuous authentication on mobile devices and address some prevalent issues with the current state-of-the-art. We use a state-of-the-art public dataset containing face and touch-gesture modalities for 50 users. Features are extracted from each modality for each user. We train a set of classifiers for user modalities to provide probability scores on a sample. The scores capture the nuances of each sample and are concatenated into a vector. This vector is used in a meta-level classifier. The scores we obtain from the meta-level classifiers show our approach performs better than previous continuous authentication approaches. We achieve an equal error rate of 3.77% for a single sample. We also show the added robustness a multi-modal approach provides if one modality is compromised.
Empirical study in Nigeria on effective implementation of Web 2.0 technology tools in learning: Case study of three Nigerian higher institutions

This paper presents a follow-up research which explored existing research with potentials for new insight for improving the effectiveness and use of Web 2.0 technology tools in learning in higher education. The first research used quantitative methods to validate the model that it developed. The research presented in this paper follows up the work by adopting a conversational approach of data collection in focus groups that consisted of management staff and lecturers. Data collection was done using unstructured interviews with 36 academics in two Nigerian institutions in a workshop aimed at creating awareness on the usefulness of Web 2.0 to enhance collaborative, problem solving and critical thinking skills. The research gathered through participatory activities constructive views on acceptance and effective use of Web 2.0 technology tools for learning from academic and management point of view and also compared these views with the quantitative findings in the previous research. The findings were that the research constructs are significantly related to acceptance and effective use of these tools for learning and these constructs are perceived usefulness, ease of use, social factor, performance expectancy, facilitating conditions, prior knowledge and motivation.
Multimodal adaptation through simulation for digital TV interface

Recent research on interactive electronic systems like computer, digital TV, smartphones can improve the quality of life of many disabled and elderly people by helping them to engage more fully to the world. However, existing design practices often isolate elderly or disabled users by considering them as users with special needs and do not consider their problems during the design phase. Later they try to solve the problem by providing a few accessibility features, which surely reduces the scalability of the systems. In this paper, we present a system that can help in designing interfaces for people with a wide range of abilities by simulating their interaction patterns. We have described the detail of our simulator in the context of other similar work and also discussed applications of the simulator for developing accessible and adaptable digital TV interfaces.
Specific radar emitter recognition based on wavelet packet transform and probabilistic SVM

Radar emitter recognition plays an important role in military automated command and control system. It is a composite task that involves radar signal interception, modulation recognition, features extraction and classification. In this paper, a wavelet packet transform is used for feature extraction of unintentional modulation on pulse (UMOP). Then specific radar emitter recognition is achieved by a probabilistic SVM (PSVM). Experiments on simulation and actual radar data verify the correctness and validity of this method.
Violencia de pareja en universitarios españoles: Resultados preliminares de un estudio exploratorio

espanolLa presencia de comportamientos agresivos en parejas jovenes es un factor de riesgo para la posterior violencia en parejas adultas, por ello su deteccion es imprescindible para prevenir este fenomeno. El objetivo de esta investigacion es determinar la frecuencia de diferentes tipos de agresiones a traves del Conflict in Adolescent Dating Relationships Inventory (CADRI) en 174 estudiantes espanoles de la Universidad de Salamanca con edades entre 18 y 21 anos. Los analisis estadisticos determinaron una consistencia interna aceptable en la mayoria de las subescalas del CADRI y una alta correlacion entre las mismas. En cuanto a las agresiones, no se encontraron diferencias significativas entre sexos, a excepcion de las agresiones verbales-emocionales cometidas y agresiones fisicas sufridas, donde mujeres y hombres puntuaron mas alto respectivamente. Se discuten las implicaciones de los resultados obtenidos, se comparan con antecedentes empiricos disponibles y se senalan algunas lineas futuras de investigacion. EnglishThe presence of aggressive behaviors in young couples is a risk factor for future adult partner violence, hence their detection is essential to prevent this phenomenon. The objective of this research is to determine the frequency of different types of aggression through the Conflict in Adolescent Dating Relationships Inventory (CADRI) in 174 Spanish students at the University of Salamanca, aged between 18 and 21. Statistical analysis determined an acceptable internal consistency for most CADRI subscales and high correlations between them. With regard to aggressions, there were no significant differences by sex, with the exception of perpetrating verbal-emotional abuse and suffering physical agressions, where women and men scored higher respectively. The implications of the results are compared with available empirical data and identifies some future research lines.
Towards a unified architecture for mapping static environments

Sensor fusion systems are becoming more and more important in automotive applications, especially for future driver assistance systems and autonomous driving. While it was sufficient to use single sensors in early driving assistance systems, future systems will rely on several sensors with different measurement principles. The probably conflicting measurements need to be fused to retrieve a complete picture of the current surroundings. However, an increased number of sensors leads to difficulties while incorporating their measurements, since every sensor provides its data via special interfaces. This inconvenience of current sensor fusion architectures has been solved for moving objects. A fusion architecture based on a generic interface - the object list - has been developed over the last years. It allows an easy use of additional sensors as long as they provide their measurements as object lists. Similar solutions for static environments are rare, but the recent proposal of the Fences-approach [1] is promising. This work shows, how sensors and occupancy grids are represented in the Fences-architecture. Steps and algorithms are presented to transform sensor reading into Fences as well as extracting Fences from common grid-based strategies for mapping static environments. The algorithms are applied to the conversion of ultrasonic sensor readings and LiDAR measurements to Fences. Furthermore, the construction of Fences from Bayesian occupancy grids is presented.
UTU: Disease Mention Recognition and Normalization with CRFs and Vector Space Representations

In this paper we present our system participating in the SemEval-2014 Task 7 in both subtasks A and B, aiming at recognizing and normalizing disease and symptom mentions from electronic medical records respectively. In subtask A, we used an existing NER system, NERsuite, with our own feature set tailored for this task. For subtask B, we combined word vector representations and supervised machine learning to map the recognized mentions to the corresponding UMLS concepts. Our system was placed 2nd and 5th out of 21 participants on subtasks A and B respectively showing competitive performance.
Measuring shape rectangularities

This paper introduces a family of rectangularity measures. Several rectangularity measures already exist in literature, and all of them evaluate how much the shape considered differs from a perfect rectangle. But all existing measures assume that all rectangles have the same shape, and a consequence is that these measures do not distinguish among the rectangles whose edge-ration differs. In this paper we consider that differently elongated rectangles have different shape and propose a family of new rectangularity measures which distinguish among such rectangles. New rectangularity measures are invariant with respect to translation, rotation and scaling transformations. In addition, we introduce a tuning parameter which enables an additional flexibility of the new measures with respect to performing applications.
Gait event-based human-robot synchrony for gait rehabilitation using adaptive oscillator

In the field of gait training with rehabilitation robots, reference robot trajectories that are synchronous to human gait is inevitable. In this paper, a novel adaptive oscillator-based synchronization algorithm using gait events during overground walking is proposed. The gait events are detected with hidden Markov model (HMM), the feature vector of which is measured with wearable inertia measurement unit (IMU) sensors. An adaptive oscillator, which was proposed to explain the mechanism of the synchronous flashing of fireflies, is implemented to estimate the phase of the human gait using the gait events (pulse) information. The reference trajectory is generated with a look-up table according to the estimated phase. The proposed algorithm has been implemented in a portable knee-ankle-robot and tested on a healthy subject. Experimental results demonstrate that the proposed algorithm is effective, flexible and efficient to achieve human-robot synchrony.
Feature Fusion Methods for Robust Speech Emotion Recognition Based on Deep Belief Networks

The speech emotion recognition accuracy of prosody feature and voice quality feature declines with the decrease of SNR (Signal to Noise Ratio) of speech signals. In this paper, we propose novel sub-band spectral centroid weighted wavelet packet cepstral coefficients (W-WPCC) for robust speech emotion recognition. The W-WPCC feature is computed by combining the sub-band energies with sub-band spectral centroids via a weighting scheme to generate noise-robust acoustic features. And Deep Belief Networks (DBNs) are artificial neural networks having more than one hidden layer, which are first pre-trained layer by layer and then fine-tuned using back propagation algorithm. The well-trained deep neural networks are capable of modeling complex and non-linear features of input training data and can better predict the probability distribution over classification labels. We extracted prosody feature, voice quality features and wavelet packet cepstral coefficients (WPCC) from the speech signals to combine with W-WPCC and fused them by Deep Belief Networks (DBNs). Experimental results on Berlin emotional speech database show that the proposed fused feature with W-WPCC is more suitable in speech emotion recognition under noisy conditions than other acoustics features and proposed DBNs feature learning structure combined with W-WPCC improve emotion recognition performance over the conventional emotion recognition method.