Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolution of an Arsenal

Venom is a key innovation underlying the evolution of advanced snakes (Caenophidia). Despite this, very little is known about venom system structural diversification, toxin recruitment event timings, or toxin molecular evolution. A multidisciplinary approach was used to examine the diversification of the venom system and associated toxins across the full range of the ∼100 million-year-old advanced snake clade with a particular emphasis upon families that have not secondarily evolved a front-fanged venom system (∼80% of the 2500 species). Analysis of cDNA libraries revealed complex venom transcriptomes containing multiple toxin types including three finger toxins, cobra venom factor, cysteine-rich secretory protein, hyaluronidase, kallikrein, kunitz, lectin, matrix metalloprotease, phospholipase A2, snake venom metalloprotease/a disintegrin and metalloprotease, and waprin. High levels of sequence diversity were observed, including mutations in structural and functional residues, changes in cysteine spacing, and major deletions/truncations. Morphological analysis comprising gross dissection, histology, and magnetic resonance imaging also demonstrated extensive modification of the venom system architecture in non-front-fanged snakes in contrast to the conserved structure of the venom system within the independently evolved front-fanged elapid or viperid snakes. Further, a reduction in the size and complexity of the venom system was observed in species in which constriction has been secondarily evolved as the preferred method of prey capture or dietary preference has switched from live prey to eggs or to slugs/snails. Investigation of the timing of toxin recruitment events across the entire advanced snake radiation indicates that the evolution of advanced venom systems in three front-fanged lineages is associated with recruitment of new toxin types or explosive diversification of existing toxin types. These results support the role of venom as a key evolutionary innovation in the diversification of advanced snakes and identify a potential role for non-front-fanged venom toxins as a rich source for lead compounds for drug design and development.
Environment exploration for object-based visual saliency learning

Searching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism. We first define saliency based on a geometrical criterion and use this definition to segment salient elements given an attentive but costly and restrictive observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use an exploration strategy based on intrinsic motivation to drive our displacement in order to get relevant observations. Our approach has been tested on a robot in indoor environments as well as on publicly available RGB-D images sequences. We demonstrate that the approach outperforms several state-of-the-art methods in the case of indoor object detection and that the exploration strategy can drastically decrease the time required for learning saliency.
Issues and opportunities

As organizational computing moves from back roo m production processing to more highly visible areas such as computing for competitive advantage, expert systems , fourth generation technologies, local area networks, an d desktop publishing, one may assume that the vast majority of organizational effort should be spent on developing new applications to take advantage of these new technologies and development philosophies . As attractive as this migh t sound most organizations are faced with a much mor e glaring problem that is consuming the vast majority o f available resources . Resources that otherwise might b e dedicated to these new projects . The problem is how do w e manage and maintain a 30 year inventory of applicatio n software . The issue is not trivial since the software span s both hardware and software generations and is critical to the day-to-day operations of the business . A brief analysis of the problem indicates it will only get worse . For every new system placed in production we generate another piece of software to be added to the eclectic inventory . Admittedly maintenance can be classified as a technical problem and needs a technical solution . But the only hope of getting a handle on the issue and determinin g how to maintain the inventory, without sacrificing th e future of the organization, is to develop a manageria l solution. The solution needs to address such issues a s strategy, productivity, policy, procedures, and priorities . In an attempt to learn more about the practica l implications and organizational realities of managing th e maintenance function, 29 MIS directors and maintenanc e managers were assembled to discuss the issue. All of th e attendees represented member firms of the Informatio n System Research Center at the University of Houston . A round table discussion format was used to facilitat e interaction among the attendees . The objective of th e session was to establish the key elements of a mission statement for the information system (IS) maintenanc e function from hich organization specific action plans could be developed . The purpose of this article is to report on th e outcomes of the session and specifically to (1) describe th e key issues and questions raised by the group, (2) list a set of critical success factors for the maintenance functio n identified by the group, and (3) list a series of attributes that can be used to develop an organization's mission statemen t for the IS maintenance function .
Teaching Constructors : A Difficult Multiple Choice

We carried out research on teaching OOP to high-school students; the understanding of constructors turned out to be a major issue. This paper surveys different structures of declarations for constructors, their semantic context and their influence on programming and students’ comprehension. We found that the professional style of declaring a constructor to initialize attributes from parameters is to be preferred even though it seems difficult to learn. Other “simplerÔ styles caused serious misconceptions.
Spanning trees with O(1) average stretch factor

Let G be a connected graph with n vertices in which each edge has a weight, and let T be a spanning tree of G. The stretch factor of two vertices x and y is the ratio of the distance between x and y in T and the shortest-path distance between x and y in G. In SODA 2007, Abraham, Bartal and Neiman showed that there exists a spanning tree T of G such that the average stretch factor (over all i n 2 ¢ vertex pairs) is bounded by a constant. We prove this result for the cases when (i) G is the complete graph on a set of points inR d and edge weights represent Euclidean distances and (ii) G is the complete graph on a set of points in a metric space and edge weights represent distances in this space.
Decentralized Transmission Policies for Energy Harvesting Devices

The problem of finding decentralized transmission policies in a wireless communication network with energy harvesting constraints is formulated and solved using the decentralized Markov decision process framework. The proposed policy defines the transmission strategies of all devices so as to correctly balance the collision probabilities with the energy constraints. After an initial coordination phase, in which the network parameters are initialized for all devices, every node proceeds in a fully decentralized fashion. We numerically show that, unlike in the case without energy constraints where a fully orthogonal scheme can be shown to be optimal, in the presence of energy harvesting this is no longer the best choice, and the optimal strategy lies between an orthogonal and a completely symmetric system.
Multimodal target prediction model

This paper presents a Neural Network based model that can be used to predict pointing target for both physical and situational impairment. The model takes different trajectory profiles like velocity, acceleration and bearing of movement as input parameters and based on that predicts next pointing target. We reported three user studies -- one involving users with physical and age-related impairment using a mouse and the other two involved able-bodied users using head and eye-gaze tracking based systems. We found that the model can accurately predict target in all cases. Finally we proposed an adaptation system using the target prediction model that can statistically significantly reduce pointing times.
Hct-A Is a New Actinoporin Family from the Heteractis Crispa Sea Anemone

Several new actinoporin isoforms with molecular weights of 18995.5 to 19398.7 Da exhibiting a high hemolytic activity were isolated from the tropical sea anemone Heteractis crispa using a combination of liquid chromatography techniques. The actinoporins were demonstrated to occur as mono-, di-, and trimers in aqueous solutions. The sequences of the genes encoding actinoporins were identified, and the amino acid sequences of the new polypeptides belonging to the Hct-A actinoporin family were obtained. The new acinoporins differ in their isoelectric points, the number and localization of charged amino acid residues at the functionally important N-terminal fragment of the molecule, as well as in the charge of a tetrapeptide (amino acid residues 74–77) involved in an electrostatic interaction with the cytoplasmic membrane. A recombinant actinoporin, rHct-A2, with a molecular weight of 19141 Da, pI of 9.64, and hemolytic activity of 4.0 × 104 HU/mg, was obtained. The conductivity of the ion channels formed by rHct-A2 in the BLM was demonstrated to be similar to that of the native actinoporin from H. crispa. The obtained data expand knowledge on the structural and functional relationships of actinoporins and contribute to our understanding of the functioning mechanism of these molecules, which is the basis for the development of compounds with a high biomedical potential. Currently, they are considered as models for obtaining antitumor, antibacterial, and cardiac-stimulating agents.
Improved K-Modes Clustering Method Based on Chi-square Statistics

This paper proposes an improved K-Modes clustering method based on Chi-square statistics, using Chi-square statistics to characterize the relationship between the attributes of data objects. On this basis, the new distance measure is proposed, The distance measure method not only take into account the value of an attribute of an object different from itself, but also take into account other attributes’ influence, which can meet the practical problems. The experimental results show that the proposed clustering method is effective, it can improve the accuracy of the clustering.
A Functionally Superior Second-Generation Vector Expressing an Aurora Kinase-A-Specific T-Cell Receptor for Anti-Leukaemia Adoptive Immunotherapy

Aurora Kinase A is a cancer-associated protein normally involved in the regulation of mitosis. Being over-expressed in a range of cancers, it is a suitable target for cell-based immunotherapy. Gene transfer of T-cell receptor sequences cognisant of HLA-A*0201-restricted Aurora Kinase A antigen has previously been shown to transfer specific immunoreactivity against the target peptide in a Human Lymphocyte Antigen-restricted manner. While T cell receptor gene-transfer has great potential in overcoming the difficulties of isolating and expanding tumour-reactive lymphocytes from a patient’s own cells, one hurdle is potential mispairing and competition between exogenous and endogenous T cell receptor chains. We have used a retroviral vector design bearing a short-interfering RNA that downregulates endogenous T cell receptor chains, without affecting expression of the transgenic T cell receptor sequences. The T cell receptor expression cassette also includes a 2A self-cleaving peptide, resulting in equimolar expression of the T cell receptor alpha and beta chains, further enhancing formation of the desired T cell receptor. Via a simple, modular cloning method, we have cloned the alpha and beta chains of the anti-Aurora Kinase A-reactive T cell receptor into this ‘siTCR’ vector. We then compared the activity of this vector against the original, ‘conventional’ vector across a panel of assays. T cell receptors expressed from the siTCR-vector retained the cytotoxic functionality of the original vector, with evidence of reduced off-target reactivity. The rate of expression of correctly-formed T cell receptors was superior using the siTCR design, and this was achieved at lower vector copy numbers. Maintaining T cell receptor efficacy with a reduced vector copy number reduces the risk of genotoxicity. The siTCR design also reduces the risk of mispairing and cross-reactivity, while increasing the functional titre. Such improvements in the safety of T cell receptor gene-transfer will be crucial for clinical applications of this technology.
How Face-to-Face Interviews and Cognitive Skill Affect Item Non-Response: A Randomized Experiment Assigning Mode of Interview

Technology and the decreased cost of survey research have made it possible for researchers to collect data using new and varied modes of interview. These data are often analyzed as if they were generated using similar processes, but the modes of interview may produce differences in response simply due to the presence or absence of an interviewer. In this paper, we explore the differences in item non-response that result from different modes of interview and find that mode makes a difference. The data are from an experiment in which we randomly assigned an adult population to an in-person or self-completed survey after subjects agreed to participate in a short poll. For nearly every topic and format of question, we find less item non-response in the self-complete mode. Furthermore, we find the difference across modes in non-response is exacerbated for respondents with low levels of cognitive abilities. Moving from high to low levels of cognitive ability, an otherwise average respondent can be up to six times more likely to say “don’t know” in a face-to-face interview than in a self-completed survey, depending on the type of question.
Mice Deficient in ILI ~ Manifest Impaired Contact Hypersensitivity to Trinitrochlorobenzene

Mice rendered deficient in IL-1[3 by gene targeting in embryonic stem cells develop and grow normally in a protected laboratory environment. Endotoxin-stimulated peritoneal macrophages from IL-l~-deficient mice showed normal synthesis and cellular release of IL-lct after treatment with 5 mM ATP demonstrating that IL-113 is not necessary for expression and release of the IL-lot isoform. Mice deficient in IL-I~ showed unaltered sensitivity to endotoxic shock, with or without pretreatment with D-galactosamine. In contrast, IL-lJ3-deficient mice showed defective contact hypersensitivity responses to topically applied trinitrochlorobenzene (TNCB). This defect could be overcome either by application of very high doses of sensitizing antigen, or by local intradermal injection of recombinant IL-1[3 immediately before antigen application. These data demonstrate an essential role for IL-1[3 in contact hypersensitivity and suggest that IL-1{3 acts early during the sensitization phase of the response. They suggest an important role for 1I. -1(3 in initiation of the host response at the epidermal barrier.
Speech Emotion Recognition Using Adaptive Ensemble of Class Specific Classifiers

Emotion recognition plays a significant role in Human Computer Interaction (HCI) field for effective communication. The aim of this study is to built a generic emotion recognition system to face the challenges of recognition in resolving confusion among acoustical characteristics of emotions, identifying dominating emotion from mixed emotions etc. When there is confusion among the perception of emotion by human, the understanding of it by machine is a real challenge. Due to these reasons, it is very hard to produce highly accurate emotion recognition system in real time. Researchers are working to improve the performance of emotion recognition task by designing different classifiers and also using different ensemble methodologies at data level, feature level or decision levels to recognize emotion. We have built a generic SVM based emotion recognition system, which models emotions using given features. Out of given acoustical features, for every emotional class, class specific best features are identified based on f1 measure. The responses of the systems, built based on these best features are combined using new smart additive ensemble techniques. Decision logic is employed to decode the responses into an emotional class, the class which produces maximum value among all emotional classes. A rejection framework is also designed to reject a noisy and weak input file. We have tested the framework with 12 acoustical features on Berlin emotional corpus EMO-DB. The accuracy obtained from our generic emotion recognition system is 74.70% which is better than classifiers reported in the literature.
MEASURING BRAIN CONNECTIVITY WITH FUNCTIONAL IMAGING AND TRANSCRANIAL MAGNETIC STIMULATION

Developments in functional imaging during the past two decades have allowed for significant advances in understanding how the brain functions at a systems, circuit, or organ level. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) now allow researchers to image brain activity (usually related to oxygen or glucose use) with crisp spatial and temporal resolution. For example, fMRI can spatially resolve structures as small as 1 to 2 mm and view brain activity in time blocks as brief as 2 to 3 seconds. Although this time resolution is crude relative to the speed of neuronal activity and information flow between brain regions (on the order of milliseconds), these tools are nevertheless able to demonstrate the activity of clusters of brain cells through a sustained time domain in association with a behavior or task. Unfortunately, these slow time frames cannot image the directional flow of information through the brain, although exciting research in this area is under way. Thus, functional imaging tools alone have been limited in their ability to demonstrate how brain regions work in a coordinated and connected fashion to modulate information and regulate and produce behavior. Therefore, a fundamental problem with conventional functional imaging to date has been the inability to probe and understand the causal relationship between regional brain ac-
Molecular Cloning and Expression Analysis of IgD in Nile Tilapia (Oreochromis niloticus) in Response to Streptococcus agalactiae Stimulus

IgD is considered to be a recently-evolved Ig and a puzzling molecule, being previously found in all vertebrate taxa, except for birds. Although IgD likely plays an important role in vertebrate immune responses, the function of IgD in Nile tilapia (Oreochromis niloticus) is virtually unknown. In the present study, a membrane form of IgD (mIgD) heavy chains were cloned from the GIFT strain of Nile tilapia (designated On-mIgD). The On-mIgD heavy chain’s cDNA is composed of 3347 bp with a 31 bp of 5′-UTR, 3015 bp open reading frame (ORF) and 301 bp 3′-UTR, encoding a polypeptide of 1004 amino acids (GenBank accession no: KF530821). Phylogenetic analysis revealed that On-mIgD heavy chains showed the highest similarity to Siniperca chuatsi. Quantitative real-time PCR (qRT-PCR) analysis showed that On-mIgD expression occurred predominately in head kidney, thymus, spleen, and kidney. After Streptococcus agalactiae infection, transcripts of On-mIgD increased and reached its peak at 48 h in the head kidney and thymus, and 72 h in the spleen, respectively. Taken together, these results collectively indicated that IgD could possibly have a key role to play in the immune response when bacterial infections in Nile tilapia.
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption Generation

We present our submission to the Microsoft Video to Language Challenge of generating short captions describing videos in the challenge dataset. Our model is based on the encoder--decoder pipeline, popular in image and video captioning systems. We propose to utilize two different kinds of video features, one to capture the video content in terms of objects and attributes, and the other to capture the motion and action information. Using these diverse features we train models specializing in two separate input sub-domains. We then train an evaluator model which is used to pick the best caption from the pool of candidates generated by these domain expert models. We argue that this approach is better suited for the current video captioning task, compared to using a single model, due to the diversity in the dataset. Efficacy of our method is proven by the fact that it was rated best in MSR Video to Language Challenge, as per human evaluation. Additionally, we were ranked second in the automatic evaluation metrics based table.
Enhancing Recommender Systems Using Social Indicators

Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. Recommender systems are increasingly driving user experiences on the Internet. In recent years, on-line social networks have quickly become the fastest growing part of the Web. The rapid growth in social networks presents a substantial opportunity for recommender systems to leverage social data to improve recommendation quality, both for recommendations intended for individuals and for groups of users who consume content together. This thesis shows that incorporating social indicators improves the predictive performance of group-based and individual-based recommender systems. We analyze the impact of social indicators through small-scale and large-scale studies, implement and evaluate new recommendation models that incorporate our insights, and demonstrate the feasibility of using these social indicators and other contextual data in a deployed mobile application that provides restaurant recommendations to small groups of users. Dedication This thesis is dedicated to all of my family and friends. In particular, I dedicate this thesis to Aurora, my wife, for her tireless love and encouragement over the years. Without her support this work would not have been possible. v Acknowledgements Much of the research in this thesis was conducted in collaboration with current and former faculty and students at the University of Colorado Boulder, including Nice at Microsoft R&D Israel for acquiring a license for the Nielsen dataset used in this work. vi Contents Chapter 1 Overview 1 1.
Distance-aware adaptive resources allocation for hybrid satellite/4G networks

The hybrid composition of satellite and terrestrial 4G LTE-A networks serves as a key enabler for a potential ubiquitous coverage and flexibility in bandwidth configuration. Thus, achieving a fruitful synergy in support of seamless services anywhere/anytime is an important contribution of that kind of architecture. However, many challenges and issues for supporting heterogeneous networks should be considered, especially for resource allocation. In this paper we propose adaptive resources allocation algorithm in order to compensate the propagation delay disparity in LEO satellites coverage. The proposed scheme is an adaptive distance-aware spectrum sharing system. In the MAC layer, it results in a variable access delay on the satellite air interface that aims to maintain the end to end delay under the QoS threshold. Analytical Markov model was proposed, for a burst oriented access scheme in the one hand while simulation tests were done for packet oriented access scheme on the other hand; both of them were done for telephony application in heterogeneous 3G/4G networks.