Miracle-dz/UniFAR-FTUs huggingface dataset:
Zack Butler

Previous work on self-reconfiguring modular robots has concentrated primarily on designing hardware and developing reconfiguration algorithms tied to specific hardware systems. In this paper, we introduce a generic model for lattice-based self-reconfigurable robots and present several generic locomotion algorithms that use this model. The algorithms presented here are inspired by cellular automata, using geometric rules to control module actions. The actuation model used is a general one, assuming only that modules can generally move over the surface of a group of modules. These algorithms can then be instantiated onto a variety of particular systems. Correctness proofs of many of the rule sets are also given for the generic geometry; this analysis can carry over to the instantiated algorithms to provide different systems with correct locomotion algorithms. We also present techniques for automated analysis that can be used for algorithms that are too complex to be easily analyzed
Body image and its relationship with sexual function and marital adjustment in infertile women

Background: Body image is related to cognitive, emotional, and physical aspects of women's life. Therefore, it is expected to have an important role in women's sexual health and marital adjustment too. This issue seems to be salient in infertile women who suffer from psychological consequences of infertility. This study was conducted to investigate the relationship of body image with sexual function and marital adjustment in infertile women in 2011 in Mashhad, Iran. Materials and Methods: This correlational study was performed on 130 infertile women who referred to Montaserieh Infertility Research Centre in Mashhad, Iran. Subjects were selected using convenient sampling method. To collect data, valid and reliable questionnaires including demographic and infertility-related data tool, modified Younesi Body Image Questionnaire, Rosen Female Sexual Function Index (FSFI), and Spanier Dyadic Adjustment Scale (DAS) were used. Data analysis was performed by SPSS software using Student's t-test, correlation, analysis of variance (ANOVA), and Tukey post-hoc test. Results: The mean scores of body image, sexual function, and marital adjustment in women were 308.1 ± 45.8, 27.23 ± 3.80, and 113.8 ± 19.73, respectively. There was a direct correlation between overall body image and subscales of sexual function including sexual arousal (P = 0.003), sexual desire (P = 0.024), vaginal moisture (P = 0.001), orgasm (P < 0.001), sexual satisfaction (P < 0.001), and dyspareunia (P = 0.007). A direct correlation was also observed between overall body image and subscales of marital adjustment including agreement and consent (P < 0.001), satisfaction with life (P < 0.001), continuity of life (P = 0.007), and expressing emotions within the family environment (P < 0.001). Conclusions: Improved sexual function and marital adjustment in cases with higher body image provides evidence that one of the solutions to reduce sexual dysfunction and marital dispute in infertile women could be planning educational and counseling programs to improve women's body image.
Geometric multichannel common signal separation with application to music and effects extraction from film soundtracks

We address the task of separation of music and effects from dialogs in film or television soundtracks. This is of interest for film studios wanting to release films in new, previously unavailable languages when the original separated music and effects track is not available. For this purpose, we propose several methods for common signal extraction from a set of soundtracks in different languages, which are multichannel extensions of previous methods for center signal extraction from stereo signals. The proposed methods are simple, effective, and have an intuitive geometrical interpretation. Experiments show that the proposed methods improve the results provided by our previously proposed methods based on basic filtering techniques.
Red and Processed Meat and Colorectal Cancer Incidence: Meta-Analysis of Prospective Studies

Background The evidence that red and processed meat influences colorectal carcinogenesis was judged convincing in the 2007 World Cancer Research Fund/American Institute of Cancer Research report. Since then, ten prospective studies have published new results. Here we update the evidence from prospective studies and explore whether there is a non-linear association of red and processed meats with colorectal cancer risk. Methods and Findings Relevant prospective studies were identified in PubMed until March 2011. For each study, relative risks and 95% confidence intervals (CI) were extracted and pooled with a random-effects model, weighting for the inverse of the variance, in highest versus lowest intake comparison, and dose-response meta-analyses. Red and processed meats intake was associated with increased colorectal cancer risk. The summary relative risk (RR) of colorectal cancer for the highest versus the lowest intake was 1.22 (95% CI = 1.11−1.34) and the RR for every 100 g/day increase was 1.14 (95% CI = 1.04−1.24). Non-linear dose-response meta-analyses revealed that colorectal cancer risk increases approximately linearly with increasing intake of red and processed meats up to approximately 140 g/day, where the curve approaches its plateau. The associations were similar for colon and rectal cancer risk. When analyzed separately, colorectal cancer risk was related to intake of fresh red meat (RR for 100 g/day increase = 1.17, 95% CI = 1.05−1.31) and processed meat (RR for 50 g/day increase = 1.18, 95% CI = 1.10−1.28). Similar results were observed for colon cancer, but for rectal cancer, no significant associations were observed. Conclusions High intake of red and processed meat is associated with significant increased risk of colorectal, colon and rectal cancers. The overall evidence of prospective studies supports limiting red and processed meat consumption as one of the dietary recommendations for the prevention of colorectal cancer.
PETcofold: predicting conserved interactions and structures of two multiple alignments of RNA sequences

Motivation: Predicting RNA–RNA interactions is essential for determining the function of putative non-coding RNAs. Existing methods for the prediction of interactions are all based on single sequences. Since comparative methods have already been useful in RNA structure determination, we assume that conserved RNA–RNA interactions also imply conserved function. Of these, we further assume that a non-negligible amount of the existing RNA–RNA interactions have also acquired compensating base changes throughout evolution. We implement a method, PETcofold, that can take covariance information in intra-molecular and inter-molecular base pairs into account to predict interactions and secondary structures of two multiple alignments of RNA sequences. Results: PETcofold's ability to predict RNA–RNA interactions was evaluated on a carefully curated dataset of 32 bacterial small RNAs and their targets, which was manually extracted from the literature. For evaluation of both RNA–RNA interaction and structure prediction, we were able to extract only a few high-quality examples: one vertebrate small nucleolar RNA and four bacterial small RNAs. For these we show that the prediction can be improved by our comparative approach. Furthermore, PETcofold was evaluated on controlled data with phylogenetically simulated sequences enriched for covariance patterns at the interaction sites. We observed increased performance with increased amounts of covariance. Availability: The program PETcofold is available as source code and can be downloaded from http://rth.dk/resources/petcofold. Contact: gorodkin@rth.dk; backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.
Antibody response to central and peripheral nerve antigens in rat and guinea-pig

Experimental allergic encephalomyelitis is readily produced in a number of mammalian species by the injection of brain, or fractions derived therefrom, together with Freund's adjuvant. Whilst substitution of whole peripheral nerve leads to an allergic neuritis, there is considerable species difference in response to these antigens. Thus Waksman and Adams (1955; 1956) reported that guinea-pigs developed both central and peripheral lesions after injection with either brain or nerve. In half of those receiving brain, disease was limited to the central nervous system. One-third of guinea pigs challenged with peripheral nerve developed a purely peripheral lesion. On the other hand rabbits did not show central lesions after inoculation with peripheral nerve. The recent claims by Bornstein and his co-workers (Bornstein and Appel
Sentiment Classification and Feature based Summarization of Movie Reviews in Mobile Environment

framework is designed for sentiment classification and feature based summarization system in a mobile environment. Posting online reviews has become an increasingly popular way for people to share their opinions about specific product or service with other users. It has become a common practice for web technologies to provide the venues and facilities for people to publish their reviews. Sentiment classification and feature based summarization are essential steps for the classification and summarization of movie reviews. System proposed Random forest method for sentiment classification of movie reviews. Identification of movie features and opinion words are both important for feature based summarization. System identified movie features using a novel approach called Latent Semantic Analysis (LSA) and frequency based approach. Then system identified opinion words using part-of-speech (POS) tagging method. The result of LSA is extended to LSA based filtering mechanism to reduce the size of review summary. System design focused on the sentiment classification accuracy and system response time.
Hypersocial Museum: addressing the social interaction challenge with museum scenarios and attention-based approaches

This work intended to show that the use of museum scenarios and computational attention models are a good way to achieve social interaction studies. The use of attention-based algorithm allows the possibility to dynamically analyze visitors’ behavior in museums in long-term context where some typical behavior models are learnt. Moreover, by providing feedback to visitors, it is possible to interact with them and also to foster interaction between visitors: the system here becomes a sort of mediator in human to human interaction.
Examination and Linearization of Torque Control System for Direct Torque Controlled IPMSM

This paper examines the torque response based on direct torque control (DTC) for an interior permanent magnet synchronous motor (IPMSM). DTC with a PI controller for torque control is used in this study. The relationship between the gains of the PI controller and the torque response is derived based on the transfer function of the torque control loop. Also, this paper discusses the influence of the nonlinearity of the torque control loop on the torque response. In addition, this paper proposes a linearization method for the torque control loop based on gain scheduling, and improvement of torque response is achieved. The effectiveness of the proposed method is verified by both simulation and experimental results.
GM-transfer: Graph-based model for transfer learning

Traditional data mining and machine learning technologies may fail when the training data and the testing data are drawn from different feature spaces and different distributions. Transfer learning, which uses the data from source domain and target domain, can tackle this problem. In this paper, we propose an improved Graph-based Model for Transfer learning (GM-Transfer). We construct a tripartite graph to represent the transfer learning problem and model the relations between the source domain data and the target domain data more efficiently. By learning the informational graph, the knowledge from the source domain data can be transferred to help improve the learning efficiency on the target domain data. Experiments show the effectiveness of our algorithm.
Low power digital filtering using adaptive approximate processing

: Techniques for reducing power consumption in digital circuits have become increasingly important because of the growing demand for portable multimedia devices. Digital ﬁlters, being ubiquitous in such devices, are a prime candidate for low-power design. We present a new algorithmic approach to low-power frequency-selective digital ﬁltering which is based on the concepts of adaptive approximate processing. This approach is formalized by introducing the class of approximate ﬁltering algorithms in which the order of a digital ﬁlter is dynamically varied to provide time-varying stopband attenuation in proportion to the time-varying signal-to-noise ratio (SNR) of the input signal, while maintaining a ﬁxed SNR at the ﬁlter output. Since power consumption in digital ﬁlter implementations is proportional to the order of the ﬁlter, dynamically varying the ﬁlter order is a strategy which may be used to conserve power. From this practical technique we abstract a theoretical problem which involves the determination of an optimal ﬁlter order based on observations of the input data and a set of concrete assumptions on the statistics of the input signal. Two solutions to this theoretical problem are presented, and the key results are used to interpret the solution to the practical low-power ﬁltering problem. We construct a framework to explore the statistical properties of approximate ﬁltering algorithms and show that under certain assumptions, the performance of approximate ﬁltering algorithms is asymptotically optimal. The theoretical and numerical simulation results demonstrate the efﬁcacy and versatility of asymptotically optimal low-power digital ﬁltering using adaptive approximate processing, and contribute to the evolution of low-power digital signal processing which continues to be an area of focused interest and innovation.
Automatic Results Identification in Software Engineering Papers. Is it Possible?

This paper provides an analysis of the main methods for sentence classification in scientific papers and evaluates the feasibility of this technique in unstructured papers in Software Engineering area, in order to automatically find the study results in this area. Tests conducted with the existing methods using unstructured Test Software papers showed results far below those reported by the authors with input sets composed by structured health papers. Therefore, there is a need for new approaches to identify results in unstructured papers.
The Service-Context Information Processing Mechanism Based on Information Transformation Theory in B3G/4G

The processing mechanism of service-context information is the foundation of personalized, intelligent services provision. This paper presented a Service Context Information Processing Mechanism Architecture (SCIPMA) based on "the laws of information transforms" in B3G/4G, and elaborated on its functional structure and technology realization. Based on the SCIPMA, we proposed the context-aware service provision framework in B3G/4G network. A new network entity-the context server that implemented the function of SCIPMA was inducted. Furthermore, an application example (the User-State Aware Communication System USACS) was developed and a series of experiments were conducted to verify the efficiency and applicability of SCIPMA. Finally, the results of experiments and evaluation of performance was demonstrated.
Visualizing the formation of the Kondo lattice and the hidden order in URu2Si2

Heavy electronic states originating from the f atomic orbitals underlie a rich variety of quantum phases of matter. We use atomic scale imaging and spectroscopy with the scanning tunneling microscope to examine the novel electronic states that emerge from the uranium f states in URu2Si2. We find that, as the temperature is lowered, partial screening of the f electrons’ spins gives rise to a spatially modulated Kondo–Fano resonance that is maximal between the surface U atoms. At T = 17.5 K, URu2Si2 is known to undergo a second-order phase transition from the Kondo lattice state into a phase with a hidden order parameter. From tunneling spectroscopy, we identify a spatially modulated, bias-asymmetric energy gap with a mean-field temperature dependence that develops in the hidden order state. Spectroscopic imaging further reveals a spatial correlation between the hidden order gap and the Kondo resonance, suggesting that the two phenomena involve the same electronic states.
Role of Tyrosine Phosphorylation of HS1 in B Cell Antigen Receptor-mediated Apoptosis

The 75-kD HS1 protein is highly tyrosine-phosphorylated during B cell antigen receptor (BCR)-mediated signaling. Owing to low expression of HS1, WEHI-231-derived M1 cells, unlike the parental cells, are insensitive to BCR-mediated apoptosis. Here, we show that BCR-associated tyrosine kinases Lyn and Syk synergistically phosphorylate HS1, and that Tyr378 and Tyr-397 of HS1 are the critical residues for its BCR-induced phosphorylation. In addition, unlike wild-type HS1, a mutant HS1 carrying the mutations Phe-378 and Phe-397 was unable to render M1 cells sensitive to apoptosis. Wild-type HS1, but not the mutant, localized to the nucleus under the synergy of Lyn and Syk. Thus, tyrosine phosphorylation of HS1 is required for BCR-induced apoptosis and nuclear translocation of HS1 may be a prerequisite for B cell apoptosis.
Energy-Efficient Subcarrier-and-Bit Allocation in Multi-User OFDMA Systems

Energy efficiency is becoming increasingly important for wireless communication systems in order to minimize carbon footprint of wireless networks and to increase the battery life of mobile terminals. The spectral-energy efficiency trade-off is of primary significance to determine how much energy per bit is required in a wireless communication system to achieve a specific spectral efficiency. In this paper, we study energy-efficient resource allocation scheme for Orthogonal Frequency- Division Multiple Access (OFDMA) systems with multiple users. The trade-off between spectral and energy efficiencies is analyzed under the constraint of maintaining the fairness among users. We first formulate the energy-efficient optimization problem as an integer fractional programming. We then apply an iterative fractional method to simplify the problem to integer linear programming (ILP) problem. Simulation results demonstrate that the impact of user's quality of service (QoS) is minor on the energy efficiency when a large spectral efficiency is required.
Bridging the Gap: The Use of Pathfinder Networks in Visual Navigation

Abstract This paper describes a generic approach to the design of a 3D virtual environment for visual navigation. The basic design principle is that users need to understand how an environment is organised and how they can utilise the knowledge to find their way through the environment. In this paper, we focus on the relationship between implicit semantic structures associated with a collection of domain-specific documents and users' cognitive needs in the context of visual navigation. We extend the notion of Pathfinder networks in order to represent implicit semantic structures in a 3D virtual environment. Users' cognitive needs in visual navigation are conceptualised based on the concept of cognitive maps.
Differential flatness-based observer design for a PEM fuel cell using adaptive-gain sliding mode differentiators

In this paper, we propose a differential flatness-based observer for a PEM fuel cell air-feed system. The proposed observer uses the measurements of supply manifold air pressure and compressor mass flow rate in order to estimate the oxygen partial pressure, nitrogen partial pressure and the compressor speed. An adaptive-gain Second Order Sliding Mode differentiator based on super-twisting algorithm is designed to estimate the time derivatives of the system outputs when the boundary of higher time derivative is unknown. Then, the states are estimated based on these values. The objective is to minimize the number of embedded sensors in order to get a precise and economic solution. The feasibility and effectiveness of the proposed approach is demonstrated through simulation results obtained from a nonlinear fuel cell system.
The SRI IDES statistical anomaly detector

SRI International's real-time intrusion-detection expert system (IDES) contains a statistical subsystem that observes behavior on a monitored computer system and adaptively learns what is normal for individual users and groups of users. The statistical subsystem also monitors observed behavior and identifies behavior as a potential intrusion (or misuse by authorized users) if it deviates significantly from expected behavior. The multivariate methods used to profile normal behavior and identify deviations from expected behavior are explained in detail. The statistical test for abnormality contains a number of parameters that must be initialized and the substantive issues relating to setting those parameter values are discussed. <<ETX>>
A Policy-Oriented Architecture for Opportunistic Communication on Multiple Wireless Networks

Today’s mobile devices are already equipped with multiple wireless interfaces that differ in data rates, power consumption, monetary cost, and coverage areas. Previous research has shown that intelligent policy-based switching between wireless interfaces can obtain better performance than using a single interface [29]. We build upon this pioneering work to add the notion of user-defined policies that operate at the session layer, unlike the networkand transport-layer policies of past work. Session-layer policy definitions allow us to include a delay component (such as a deadline for data transfer) at a timescale of minutes and hours. This helps us distinguish between online and offline applications, and allows us a high degree of flexibility in network selection. For example, we can defer data transfer on currently available networks if they are too costly, and wait until a low cost network (such as a free WiFi hotsopt) becomes available for opportunistic use in the future. However, actually using multiple interfaces, which requires sessions to be maintained across interface switches, disconnections, and device shutdowns, is complex. Consequently, to be useful in practice, we believe that application writers need to be shielded from these details, while still exercising fine-grained control over network usage policies. In this paper, we describe a system that allows applications running on a mobile device to seamlessly exploit multiple heterogeneous wireless networks based on userdefined session-layer policies. We have designed, implemented, and evaluated the performance of an Opportunistic Connection Management Protocol (OCMP) that allows such applications to opportunistically communicate on multiple network interfaces, switch across interfaces, remain disconnected or powered off for arbitrarily long periods, and interoperate with legacy applications and servers. The implementation is in J2ME so that it can be run on any Java-based mobile device. Extensive policy control for interface selection is also provided to applications, along with a simple API for application developers. We explain the design, architecture, and implementation of OCMP, and illustrate its benefits through field experiments. Our results are encouraging and suggest that policy support for multi-network opportunistic communication is both achievable in current systems, and of significant practical value.