Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Coevolutionary Features from Protein Sequences for Predicting Protein-Protein Interactions

Knowing the ways proteins interact with each other are crucial to our understanding of the functional mechanisms of proteins. It is for this reason that different approaches have been developed in attempts to predict protein-protein interactions (PPIs) computationally. Among them, the sequence-based approaches are preferred to the others as they do not require any information about protein properties to perform their tasks. Instead, most sequence-based approaches make use of feature extraction methods to extract features directly from protein sequences so that for each protein sequence, we can construct a feature vector. The feature vectors of every pair of proteins are then concatenated to form two classes of interacting and non-interacting proteins. The prediction of whether or not two proteins interact with each other is then formulated as a classification problem. How accurate PPI predictions can be made therefore depends on how good the features are that can be extracted from the protein sequences to allow interacting or non-interacting to be best distinguished. To do so, instead of extracting such features from individual protein sequences independently of the other protein in the same pair, we propose to jointly consider features from both sequences in a protein pair during the feature extraction process through using a novel coevolutionary feature extraction approach called CoFex. Coevolutionary features extracted by CoFex refer to the covariations found at coevolving positions. Based on the presence and absence of these coevolutionary features in the sequences of two proteins, feature vectors can be composed for pairs of proteins rather than individual proteins. The experiment results show that CoFex is a promising feature extraction approach and can improve the performance of PPI prediction.
Using DAIM as a reactive interpreter for openflow networks to enable autonomic functionality

OpenFlow is the first standardization of Software Defined Networks. OpenFlow approach, however, has number of limitations: it restricts its use within a single-domain, it is not scalable, and it does not adapt well to changes in local environments. We evaluate the number of approaches to solve these limitations, and propose DAIM model (Distributed Active information Model) which can be integrated into the OpenFlow structure at the level of the switches to provide a reactive interpreter that will manage the flow tables autonomically.
Fusion engines for multimodal input: a survey

Fusion engines are fundamental components of multimodal inter-active systems, to interpret input streams whose meaning can vary according to the context, task, user and time. Other surveys have considered multimodal interactive systems; we focus more closely on the design, specification, construction and evaluation of fusion engines. We first introduce some terminology and set out the major challenges that fusion engines propose to solve. A history of past work in the field of fusion engines is then presented using the BRETAM model. These approaches to fusion are then classified. The classification considers the types of application, the fusion principles and the temporal aspects. Finally, the challenges for future work in the field of fusion engines are set out. These include software frameworks, quantitative evaluation, machine learning and adaptation.
BEN: a novel domain in chromatin factors and DNA viral proteins

We report a previously uncharacterized alpha-helical module, the BEN domain, in diverse animal proteins such as BANP/SMAR1, NAC1 and the Drosophila mod(mdg4) isoform C, in the chordopoxvirus virosomal protein E5R and in several proteins of polydnaviruses. Contextual analysis suggests that the BEN domain mediates protein-DNA and protein-protein interactions during chromatin organization and transcription. The presence of BEN domains in a poxviral early virosomal protein and in polydnaviral proteins also suggests a possible role for them in organization of viral DNA during replication or transcription.
Experimental Study of Grinding Characteristics on SiCp/Al Composites

This paper deals with the grinding performances of SiCp/Al composites with higher volume fraction and larger SiC particle. The effects of the grinding parameters on the grinding force, removal mechanisms of SiC particles have been investigated. The grinding tests were carried out by using diamond wheel on surface grinding machine. The results indicate that the feed speed of worktable has more significant effect on the grinding forces than that of grinding depth. The scanning electron microscopic images of the machined surfaces indicate that the material removal of SiC particles was primarily due to the failure of the interface between the reinforcement and matrix, and resulting from microcracks along the interface and many fracture or crushed SiC particles on the ground surface.
WEB DOCUMENT SEGMENTATION USING FREQUENT TERM SETS FOR SUMMARIZATION

Query sensitive summarization aims at extracting th e query relevant contents from web documents. Web page segmentation focuses on reducing the run time overhead of the summarization systems by grouping the related contents of a web page into segments. A t query time, query relevant segments of the web pa ge are identified and important sentences from these s egments are extracted to compose the summary. DOM tree structures of the web documents are utilized t o perform the segmentation of the contents. Leaf no des of DOM tress are merged to form segments according to the statistical and linguistic similarity measur e. The proposed system has been evaluated by intrinsic approach making use of user satisfaction index. Th e performance of the system is compared with summarization without using preprocessed segments. Performance of this system is more promising than t he other measures like cosine similarity, jaccard measure which make use of sparse term-frequent vectors, since the most frequent term sets are consider ed to measure the relevance. Relevant segments alone n eed to be processed at run time for summarization which reduces the time complexity of the summarization process.
Tempered Adversarial Networks

Generative adversarial networks (GANs) have been shown to produce realistic samples from high-dimensional distributions, but training them is considered hard. A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset. We propose a simple modification that gives the generator control over the real samples which leads to a tempered learning process for both generator and discriminator. The real data distribution passes through a lens before being revealed to the discriminator, balancing the generator and discriminator by gradually revealing more detailed features necessary to produce high-quality results. The proposed module automatically adjusts the learning process to the current strength of the networks, yet is generic and easy to add to any GAN variant. In a number of experiments, we show that this can improve quality, stability and/or convergence speed across a range of different GAN architectures (DCGAN, LSGAN, WGAN-GP).
Optimization of plasmonic sensors in visible and infrared regime

In this invited paper, we glance at the history of plasmonic sensors and provide the optimization study of representative plasmonic sensors such as surface plasmon resonance (SPR) sensors, localized SPR sensors, and fiber grating SPR sensors. The key variables of specific plasmonic sensors are numerically examined and compared. Furthermore, we discuss the recent developments and prospect on various plasmonic sensors.
Rotation curve fitting and its fatal attraction to cores in realistically simulated galaxy observations

We study the role of systematic effects in observational studies of the cusp–core problem under the minimum disc approximation using a suite of high-resolution (25-pc softening length) hydrodynamical simulations of dwarf galaxies. We mimic realistic kinematic observations and fit the mock rotation curves with two analytic models commonly used to differentiate cores from cusps in the dark matter distribution. We find that the cored pseudo-isothermal sphere (ISO) model is strongly favoured by the reduced χ_ν^2 of the fits in spite of the fact that our simulations contain cuspy Navarro–Frenk–White profiles (NFW). We show that even idealized measurements of the gas circular motions can lead to the incorrect answer if velocity underestimates induced by pressure support, with a typical size of order ∼5 km s^(−1) in the central kiloparsec, are neglected. Increasing the spatial resolution of the mock observations leads to more misleading results because the inner region, where the effect of pressure support is most significant, is better sampled. Fits to observations with a spatial resolution of 100 pc (2 arcsec at 10 Mpc) favour the ISO model in 78–90 per cent of the cases, while at 800-pc resolution, 41–77 per cent of the galaxies indicate the fictitious presence of a dark matter core. The coefficients of our best-fitting models agree well with those reported in observational studies; therefore, we conclude that NFW haloes cannot be ruled out reliably from this type of analysis.
Suspected Allopurinol‐Induced Aseptic Meningitis

Drug‐induced aseptic meningitis is a syndrome with symptoms similar to those of infectious meningitis. A 60‐year‐old man with a history of recurrent renal stones was admitted to the hospital with fever, chills, and mental status changes after taking levofloxacin, allopurinol, and acetazolamide. No infectious source was identified. Once home, he resumed allopurinol, and within 2 hours, he experienced the same symptoms, requiring rehospitalization. He was diagnosed with suspected meningitis from an adverse drug reaction that we believe was due to allopurinol. It is important to remember, when all other causes are ruled out, that a patient's symptoms may be a drug‐induced adverse effect. Drug‐induced aseptic meningitis should be considered when patients with symptoms similar to those of infectious meningitis appear without infectious etiologies or cerebrospinal fluid pleocytosis, a suspected agent was recently started, and resolution of adverse effects occurs when the agent is withdrawn.
Myosin-driven peroxisome partitioning in S. cerevisiae

In Saccharomyces cerevisiae, the class V myosin motor Myo2p propels the movement of most organelles. We recently identified Inp2p as the peroxisome-specific receptor for Myo2p. In this study, we delineate the region of Myo2p devoted to binding peroxisomes. Using mutants of Myo2p specifically impaired in peroxisome binding, we dissect cell cycle–dependent and peroxisome partitioning–dependent mechanisms of Inp2p regulation. We find that although total Inp2p levels oscillate with the cell cycle, Inp2p levels on individual peroxisomes are controlled by peroxisome inheritance, as Inp2p aberrantly accumulates and decorates all peroxisomes in mother cells when peroxisome partitioning is abolished. We also find that Inp2p is a phosphoprotein whose level of phosphorylation is coupled to the cell cycle irrespective of peroxisome positioning in the cell. Our findings demonstrate that both organelle positioning and cell cycle progression control the levels of organelle-specific receptors for molecular motors to ultimately achieve an equidistribution of compartments between mother and daughter cells.
Cigarette smoking leads to persistent and dose‐dependent alterations of brain activity and connectivity in anterior insula and anterior cingulate

Although many smokers try to quit smoking, only about 20–25 percent will achieve abstinence despite 6 months or more of gold‐standard treatment. This low success rate suggests long‐term changes in the brain related to smoking, which remain poorly understood. We compared ex‐smokers to both active smokers and non‐smokers using functional magnetic resonance imaging (fMRI) to explore persistent modifications in brain activity and network organization. This prospective and consecutive study includes 18 non‐smokers (29.5 ± 6.7 years of age, 11 women), 14 smokers (≥10 cigarettes a day >2 years of smoking, 29.3 ± 6.0 years of age, 10 women) and 14 ex‐smokers (>1 year of quitting 30.5 ± 5.7 years of age, 10 women). Participants underwent a block‐design fMRI study contrasting smoking cue with control (neutral cue) videos. Data analyses included task‐related general linear model, seed‐based functional connectivity, voxel‐based morphometry (VBM) of gray matter and tract‐based spatial statistics (TBSS) of white matter. Smoking cue videos versus control videos activated the right anterior insula in ex‐smokers compared with smokers, an effect correlating with cumulative nicotine intake (pack‐years). Moreover, ex‐smokers had a persistent decrease in functional connectivity between right anterior insula and anterior cingulate cortex (ACC) compared with control participants, but similar to active smokers. Potentially confounding alterations in gray or white matter were excluded in VBM and TBSS analyses. In summary, ex‐smokers with long‐term nicotine abstinence have persistent and dose‐dependent brain network changes notably in the right anterior insula and its connection to the ACC.
Abstraction, Imagery, and Control in Cognitive Architecture

This dissertation presents a theory describing the components of a cognitive architecture supporting intelligent behavior in spatial tasks. In this theory, an abstract symbolic representation serves as the basis for decisions. As a means to support abstract decision-making, imagery processes are also present. Here, a concrete (highly detailed) representation of the state of the problem is maintained in parallel with the abstract representation. Perceptual and action systems are decomposed into parts that operate between the environment and the concrete representation, and parts that operate between the concrete and abstract representations. Control processes can issue actions as a continuous function of information in the concrete representation, and actions can be simulated (imagined) in terms of it. The agent can then derive useful abstract information by applying perceptual processes to the resulting concrete state. This theory addresses two challenges in architecture design that arise due to the diversity and complexity of spatial tasks that an intelligent agent must address. The perceptual abstraction problem results from the difficulty of creating a single perception system able to induce appropriate abstract representations in each of the many tasks an agent might encounter, and the irreducibility problem arises because some tasks are resistant to being abstracted at all. Imagery works to mitigate the perceptual abstraction problem by allowing a given perception system to work in more tasks, as perception can be dynamically combined with imagery. Continuous control, and the simulation thereof via imagery, works to mitigate the irreducibility problem. The use of imagery to address these challenges differs from other approaches in AI, where imagery is considered as an alternative to abstract representation, rather than as a means to it. A detailed implementation of the theory is described, which is an extension of the Soar cognitive architecture. Agents instantiated in this architecture are demonstrated, including agents that use reinforcement learning and imagery to play arcade games, and an agent that performs sampling-based motion planning for a car-like vehicle. The performance of these agents is discussed in the context of the underlying architectural theory. Connections between this work and psychological theories of mental imagery are also discussed.
Biomedical Term Recognition with the Perceptron HMM Algorithm

We propose a novel approach to the identification of biomedical terms in research publications using the Perceptron HMM algorithm. Each important term is identified and classified into a biomedical concept class. Our proposed system achieves a 68.6% F-measure based on 2,000 training Medline abstracts and 404 unseen testing Medline abstracts. The system achieves performance that is close to the state-of-the-art using only a small feature set. The Perceptron HMM algorithm provides an easy way to incorporate many potentially interdependent features.
Insulin‐like growth factor 1 expression correlates with clinical outcome in K‐RAS wild type colorectal cancer patients treated with cetuximab and irinotecan

Seventy to 40% of K‐RAS wild type colorectal tumors does not seem to benefit from treatment with antiepidermal growth factor receptor (anti‐EGFR) monoclonal antibodies. Recent data suggested that in presence of IGF‐1 system, altered activation colorectal cancer cells may escape anti‐EGFR mediated cell death. The interaction between IGF‐1 expression and K‐RAS mutational analysis was tested to verify the ability of IGF‐1 to identify a subgroup of patients more likely to benefit from EGFR‐targeted antibodies treatment. IGF‐1 expression and K‐RAS mutational status was assessed in advanced colorectal cancer patients receiving irinotecan/cetuximab. One hundred twelve patients were analyzed. IGF‐1 was negative in 30 patients (27%) and overexpressed in the remaining 82 cases (73%). In IGF‐1 negative and IGF‐1 positive tumors, we observed progressive disease in 9 (30%) and 55 (67%) patients, respectively (p = 0.001). Median progression‐free survival was 7.5 mo in patients showing IGF‐1 negative tumors and 3 mo for IGF‐1 expressing tumors (p = 0.002). Among K‐RAS wild type patients, IGF‐1 negative and positive tumors showed a partial response to cetuximab‐irinotecan in 13 (65%) and 11 (22%) cases, respectively (p = 0.002). Median progression‐free survival in IGF‐1 negative tumors was 10 mo and 3.2 mo in IGF‐1 positive colorectal cancers (p = 0.02). IGF‐1 proved to be a possible predictive factor for resistance to anti‐EGFR monoclonal antibodies in K‐RAS wild type colorectal cancer. Combined IGF‐1 and K‐RAS analysis may represent an effective strategy for a better selection of responding colorectal cancer patients.
Modeling of Spontaneous Activity in Developing Spinal Cord Using Activity-Dependent Depression in an Excitatory Network

Spontaneous episodic activity is a general feature of developing neural networks. In the chick spinal cord, the activity comprises episodes of rhythmic discharge (duration 5–90 sec; cycle rate 0.1–2 Hz) that recur every 2–30 min. The activity does not depend on specialized connectivity or intrinsic bursting neurons and is generated by a network of functionally excitatory connections. Here, we develop an idealized, qualitative model of a homogeneous, excitatory recurrent network that could account for the multiple time-scale spontaneous activity in the embryonic chick spinal cord. We show that cycling can arise from the interplay between excitatory connectivity and fast synaptic depression. The slow episodic behavior is attributable to a slow activity-dependent network depression that is modeled either as a modulation of cellular excitability or as synaptic depression. Although the two descriptions share many features, the model with a slow synaptic depression accounts better for the experimental observations during blockade of excitatory synapses.
Joint Sparsity-Based Representation and Analysis of Unconstrained Activities

While the notion of joint sparsity in understanding common and innovative components of a multi-receiver signal ensemble has been well studied, we investigate the utility of such joint sparse models in representing information contained in a single video signal. By decomposing the content of a video sequence into that observed by multiple spatially and/or temporally distributed receivers, we first recover a collection of common and innovative components pertaining to individual videos. We then present modeling strategies based on subspace-driven manifold metrics to characterize patterns among these components, across other videos in the system, to perform subsequent video analysis. We demonstrate the efficacy of our approach for activity classification and clustering by reporting competitive results on standard datasets such as, HMDB, UCF-50, Olympic Sports and KTH.
Multilingual Word Sense Induction to Improve Web Search Result Clustering

In [Marco2013] a novel approach to Web search result clustering based on Word Sense Induction, i.e. the automatic discovery of word senses from raw text was presented; key to the proposed approach is the idea of, first, automatically inducing senses for the target query and, second, clustering the search results based on their semantic similarity to the word senses induced. In [1] we proposed an innovative Word Sense Induction method based on multilingual data; key to our approach was the idea that a multilingual context representation, where the context of the words is expanded by considering its translations in different languages, may improve the WSI results; the experiments showed a clear performance gain. In this paper we give some preliminary ideas to exploit our multilingual Word Sense Induction method to Web search result clustering.
Cooperate or not: The secondary user's dilemma in hierarchical spectrum sharing networks

We consider a spectrum sharing network consisting of a primary and a cognitive secondary transmitter-receiver pair, where the secondary transmitter can cooperatively relay primary traffic. If the secondary user chooses not to cooperate, it can transmit only when the channel is sensed idle. Otherwise, it relays the primary packet and transmits its own packet in the same time slot while guaranteeing the performance of the primary transmission. Choosing cooperation, the secondary user can transmit a packet immediately even if the primary queue is not empty, but it has to bear the additional cost of relaying. We consider a cognitive system, where, to solve this dilemma, the secondary user decides dynamically on when to cooperate. We derive the bounds of the stable-throughput region of the system, and formulate the problem as a Markov decision process (MDP). We prove the existence of a stationary policy that is average cost optimal. Numerical results show that the optimal dynamic secondary access can trade off between the gain and the cost of cooperation, and the average cost can be decreased significantly.
Solving Bivariate Polynomial Systems on a GPU

We present a CUDA implementation of dense multivariate polynomial arithmetic based on Fast Fourier Transforms over finite fields. Our core routine computes on the device (GPU) the subresultant chain of two polynomials with respect to a given variable. This subresultant chain is encoded by values on a FFT grid and is manipulated from the host (CPU) in higher-level procedures. We have realized a bivariate polynomial system solver supported by our GPU code. Our experimental results (including detailed profiling information and benchmarks against a serial polynomial system solver implementing the same algorithm) demonstrate that our strategy is well suited for GPU implementation and provides large speedup factors with respect to pure CPU code.
Systematic generation of performance models of reconfigurable analog circuits

In this work, a systematic technique to generate performance models of reconfigurable analog circuits is presented. The performance models are obtained in the form of multi-mode Pareto-optimal fronts (mm-PoFs), a new type of Pareto-optimal front (PoF) that characterizes the set of different performances that reconfigurable circuits can attain. The technique is based on the use of an evolutionary algorithm (EA) that acts as an optimizer, and the simulator HSPICE to measure the circuit performances. The use of this technique will be illustrated for a wireless multistandard problem, where a reconfigurable op-amp will be considered.