Miracle-dz/UniFAR-FTUs huggingface dataset:
Securitization of International Public Health: Implications for Global Health Governance and the Biological Weapons Prohibition Regime

This article analyzes the extent to which international public health has become securitized and what effects this has on global health governance and the biological weapons control regime. Attempts to securitize public health are traced in the two multilateral discursive spaces of greatest rele vance to biological weapons arms control and international public health; the community of state parties to the Biological Weapons Convention, and the World Health Organization. The conclusion is that with respect to pub lic health, the identified securitization moves have led to a strengthening of the state as actor in the provision of international public health. For bio logical weapons arms control, the impact of the identified securitization moves depends largely on the overall development of the biological weapons control regime. Keywords: securitization, international public health, health security, biological security, biological weapons. Public health and biological weapons arms control would appear to be two distinct policy arenas with little, if any, overlap in terms of actors involved, problems to address, and solutions to be proposed to better the human condition. Traditionally, security from biological weapons and security from disease were pursued by different actors on both the domes tic and the international level. For the former, biodefense and biological weapons (BW) arms control policies were formulated by the military and diplomatic communities, while responsibility for disease prevention and mit igation fell to the public health sectors of states, or to international organi zations such as the World Health Organization (WHO). This strict separation has become increasingly blurred. Starting in the mid-1990s, the possibility of terrorism with biological and chemical weapons has evolved into the number one security threat for military planners and decisionmakers in many countries, most notably the United States. This dramatic shift in threat perception, which was fueled first and foremost by the Aum Shinrikyo 1995 sarin gas attack in the Tokyo
Computational Challenges in Propositional Reasoning and Search

The past several years have seen much progress in the area of propositional reasoning and sat-issability testing. There is a growing consensus by researchers on the key technical challenges that need to be addressed in order to maintain this momentum. This paper outlines concrete technical challenges in the core areas of systematic search, stochastic search, problem encod-ings, and criteria for evaluating progress in this area.
A logic model of interest in information network

We analyze motivations for people to take some actions when they are reached by messages in information network. The concept of interest associate with people's motivations and actions in network is presented and defined. Then we propose a logic model in which interest can be formalized in a logic of time, actions, beliefs, and choices. The properties of interest are also studied.
Postural Coordination during Socio-motor Improvisation

Human interaction often relies on socio-motor improvisation. Creating unprepared movements during social interaction is not a random process but relies on rules of synchronization. These situations do not only involve people to be coordinated, but also require the adjustment of their posture in order to maintain balance and support movements. The present study investigated posture in such a context. More precisely, we first evaluated the impact of amplitude and complexity of arm movements on posture in solo situation. Then, we assessed the impact of interpersonal coordination on posture using the mirror game in which dyads performed improvised and synchronized movements (i.e., duo situation). Posture was measured through ankle-hip coordination in medio-lateral and antero-posterior directions (ML and AP respectively). Our results revealed the spontaneous emergence of in-phase pattern in ML direction and antiphase pattern in AP direction for solo and duo situations. These two patterns respectively refer to the simultaneous flexion/extension of the ankles and the hips in the same or opposite direction. It suggests different functional roles of postural coordination patterns in each direction, with in-phase supporting task performance in ML (dynamical stability) and antiphase supporting postural control in AP (mechanical stability). Although amplitude of movement did not influence posture, movement complexity disturbed postural stability in both directions. Conversely, interpersonal coordination promoted postural stability in ML but not in AP direction. These results are discussed in terms of the difference in coupling strength between ankle-hip coordination and interpersonal coordination.
EM-Psychiatry: An Ambient Intelligent System for Psychiatric Emergency

The proliferation of the market in patient care services is attracting attention in the healthcare industry; however, a remote mental healthcare system is still unattainable. In this paper, an ambient intelligent system of in-home psychiatric care service for emergency psychiatry (EM-psychiatry) is proposed for the remote monitoring of psychiatric emergency patients. The emergency psychiatric states of patients are modeled as the states of the maximum-entropy Markov model (MEMM), in which sensor observations, psychiatric screening scores, and patients’ histories are considered as the observations of MEMM. A modified Viterbi, a machine-learning algorithm, is used to generate the most probable psychiatric state sequence based on such observations; then, from the most likely psychiatric state sequence, the emergency psychiatric state is predicted through the proposed algorithm. The ambient EM-psychiatry model is implemented and the performance of the proposed prediction model is analyzed using the receiver operator characteristics curves, which demonstrates that the use of the EM-psychiatric screening questionnaire with biosensor observations enhances the prediction accuracy.
The Planar Coupled-Fed Multiband Antenna for Mobile Handset Application

The planar multiband monopole antenna with a coupled shorted strip and a main antenna suited to smart phone applications is proposed in the paper. The antenna occupies 52.5×15 mm2 and printed on a single layer 0.8mm-thick FR4 substrate. By properly designing the dimensions of the antenna and tuning a coupling gap, it can be excited successfully multiple resonances covering our desired frequency bands. The measured results show that the antenna with enough impedance bandwidths ranged from 684-1142 MHz and 1618-2935 MHz, based on 6 dB return loss. In addition, the antenna has good radiation properties including nearly onmi-directional radiation patterns in the horizontal planes, efficiencies varied from 54 - 95% and gains from 1 - 4.2 dBi, respectively. Thus, it can fully covers ten-bands LTE-700/2300/2500, GSM-850/900, DCS-1800, PCS-1900, UMTS-2000, WiMAX-2300, WLAN-2400. It is suitable for smart handset applications.
CLaC-CORE: Exhaustive Feature Combination for Measuring Textual Similarity

CLaC-CORE, an exhaustive feature combination system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train a model for predicting similarity between sentences in a two phase method, which in turn uses all combinations of the features in the feature space and trains separate models based on each combination. Then it creates a meta-feature space and trains a final model based on that. This two step process improves the results achieved by singlelayer standard learning methodology over the same simple features. We analyze the correlation of feature combinations with the data sets over which they are effective.
Dietary protein and fat intake in relation to risk of colorectal adenoma in Korean

Abstract Consumption of red meat and alcohol are known risk factors for colorectal cancer, but associations for dietary fat remain unclear. We investigated the associations of dietary fat, protein, and energy intake with prevalence of colorectal adenoma. We performed a prospective cross-sectional study on asymptomatic persons who underwent a screening colonoscopy at a single center during a routine health check-up from May to December 2011. Dietary data were obtained via a validated Food Frequency Questionnaire (FFQ), assisted by a registered dietician. We also obtained information on alcohol consumption and smoking status, and measured metabolic syndrome markers including abdominal circumference, blood pressure, fasting glucose, serum triglyceride and high-density lipoprotein cholesterol. We calculated odds ratio (OR) and 95% confidence interval (CI) to evaluate the associations using the polytomous logistic regression models. As a secondary analysis, we also conducted a matched analysis, matched by age and sex (557 cases and 557 non-cases). The study sample included 557 cases (406 males and 151 females) with histopathologically confirmed colorectal adenoma, and 1157 controls (650 males and 507 females). The proportion of advanced adenoma was 28.1% of men and 18.5% of female, respectively. Although vegetable protein intake was inversely associated with the prevalence of colorectal adenoma, further adjustment for potential confounding factors attenuated the association, resulting in no significant associations. There were no significant associations between dietary fat intake and colorectal adenoma in energy-adjusted models. For vegetable protein in women, the OR for the comparison of those in the highest tertile with those in the lowest tertile was 0.47 (95% CI 0.25–0.91, P for trend = 0.07) after adjustment for total energy intake. However, after controlling for metabolic syndrome markers, body mass index, smoking status, alcohol consumption, and family history of colorectal adenoma, which were all significantly high in the colorectal adenoma patients group, the association became attenuated (OR 0.54, 95% CI 0.27–1.11, P for trend = 0.13). In conclusion, we did not observe the significant associations for intakes of total energy, total, animal and vegetable fats, and total, animal and vegetable proteins in relation to colorectal adenoma prevalence.
Math Anxiety in Children with and without Mathematical Difficulties

Mathematics anxiety is generally defined as feeling tense, fearful and apprehensive about mathematics (Richardson & Suinn, 1972). It is a multi-dimensional construct, characterized by different types of reactions: emotional (i.e., negative feelings); cognitive (e.g., intrusive concerns and thoughts); physiological (e.g., increased arousal, stress and agitation); and behavioural (e.g., avoiding contexts that require the use of mathematical skills, disengagement and off-task behaviours). From a different angle, math anxiety can generate reverse effect than positive factors, such as an interest toward mathematics and self-efficacy (Moore, Rudig & Ashcraft, 2014). Individuals with high levels of math anxiety tend to take fewer mathematics courses; gain lower grades in those they do attend; and avoid, where possible, additional maths classes (Ashcraft, 2002). In addition, highly math-anxious students are also more likely to avoid mathematically-oriented college majors and career paths that require quantitative skills (Ashcraft, Krause, & Hopko, 2007; Ashcraft & Moore, 2009). Math anxiety seems to have serious long-term consequences, adversely influencing an individual’s choice of career, type of occupation, and professional growth in adulthood (Ashcraft & Ridley, 2005; Beasley, Long & Natali, 2001; Hembree, 1990; Ho et al., 2000). Beyond consequences for an individual’s personal life, math anxiety also affects society. For example, in the USA math anxiety may contribute to the shortage of graduates, who want to work in the area of science, technology, engineering and mathematics-for the demands of a technology-dependent society -despite increased emphasis on improving mathematical education (Beilock & Maloney, 2015). Because of its consequences in limiting people’s mastery of mathematics, math anxiety has become a subject of increasing interest in educational, rather than only clinical settings. Many factors are involved in the links between math anxiety and mathematics. For example, these links depend on the nature of mathematics, such as increasing complexity of its contents during the school-years. In the following sections, we summarize previous studies of math anxiety, focusing on gender differences, distinction between mathematics difficulties related to math anxiety vs those related to specific mathematics impairments, and the role of genetic factors.
D PET : Beyond Conventional Dynamic PET Imaging

In this paper, we review novel techniques in the emerging field of spatiotemporal 4D PET imaging. We will discuss existing limitations in conventional dynamic PET imaging which involves independent reconstruction of dynamic PET datasets. Various approaches that seek to attempt some or all of these limitations are reviewed in this work, including techniques that utilize iterative temporal smoothing, advanced temporal basis functions, principal components transformation of the dynamic data, wavelet-based techniques as well as direct kinetic parameter estimation methods. Extension of 4D PET to 5D PET in which the additional dimension of (respiratory or cardiac) gating is considered has also been discussed.
Direct Synthesis of Netlists Into Pre-Routed FPGAs

Direct Synthesis of Netlists Into Pre-Routed FPGAs Daniel Di Matteo Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2014 This thesis introduces a new approach to compilation for FPGAs, which we call direct synthesis. We take a technology-mapped circuit netlist and directly map it into a preplaced and routed FPGA overlay. Solving this problem may help to address the increasing portion of compile time that is attributed to placement and routing, and the tremendous amount of area and energy consumed by the highly flexible FPGA routing network. This thesis presents a direct synthesis algorithm and an algorithm for generating the pre-placed and routed FPGA overlays. Using the direct synthesis flow which we have designed, we can successfully map circuits less than 100 BLEs in size, after modest modifications to the architecture of the FPGA overlay circuit. While we show that direct synthesis problem is challenging, further architectural modifications are proposed which can allow the direct synthesis of larger circuits to succeed.
Efficient Storage of Big-Data for Real-Time GPS Applications

GPS applications need real-time responsiveness and are location-sensitive. GPS data is time-variant, dynamic and large. Current methods of centralized or distributed storage with static data impose constraints on addressing the real-time requirement of such applications. In this project we explore the need for real-timeliness of location based applications and evolve a methodology of storage mechanism for the GPS application's data. So far, the data is distributed based on zones and it also has limited redundancy leading to non-availability in case of failures. In our approach, data is partitioned into cells giving priority to Geo-spatial location. The geography of an area like a district, state, country or for that matter the whole world is divided into data cells. The size of the data cells is decided based on the previously observed location specific queries on the area. The cell size is so selected that a majority of the queries are addressed within the cell itself. This enables computation to happen closer to data location. As a result, data communication overheads are eliminated. We also build some data redundancy, which is used not only to enable failover mechanisms but also to target performance. This is done by nine-cell approach wherein each cell stores data of eight of its neighbours along with its own data. Cells that have an overload of queries, can easily pass-off some of their workload to their near neighbours and ensure timeliness in response. Further, effective load balancing of data ensures better utilization of resources. Experimental results show that our approach improves query response times, yields better throughput and reduces average query waiting time apart from enabling real-time updates on data.
Evidence of different metabolic phenotypes in humans

The study of metabolic responses to drugs, environmental changes, and diseases is a new promising area of metabonomic research. Metabolic fingerprints can be obtained by analytical techniques such as nuclear magnetic resonance (NMR). In principle, alterations of these fingerprints due to appearance/disappearance or concentration changes of metabolites can provide early evidences of, for example, onset of diseases. A major drawback in this approach is the strong day-to-day variability of the individual metabolic fingerprint, which should be rather called a metabolic “snapshot. ” We show here that a thorough statistical analysis performed on NMR spectra of human urine samples reveals an invariant part characteristic of each person, which can be extracted from the analysis of multiple samples of each single subject. This finding (i) provides evidence that individual metabolic phenotypes may exist and (ii) opens new perspectives to metabonomic studies, based on the possibility of eliminating the daily “noise” by multiple sample collection.
A Survey on Security Techniques in Group Communication for Wireless Sensor Networks

wireless network is a type of network where various physical devices (e.g. computer, laptops, PDAs etc) are interconnected with each other using network infrastructure. Owing to wireless medium of data communication, the security risk is potentially high for unauthorized access and intrusion of various malicious programs. The security protocols of wireless network are governed by family of IEEE 802.11 standards. Wireless Network is studied in research with respect to wireless LAN (Local Area Network), wireless mesh network, wireless sensor network, mobile adhoc network, etc. In recent times, wireless sensor network was on constant focus among the research community owing to its potential advantage of data collection in remote areas as well as security problems associated with it. Wireless Sensor Network (WSN) consists of various sensor motes that form a cluster and perform data aggregation. Usually, the aggregated data is forwarded from the sensor nodes to the base station, which then reaches to user for analysis. The security problems is a matter of concern even for wireless sensor network that aims for either compromising the routing protocols or invoke illegitimate access to resources by bypassing the security protocols. In a wireless sensor network, the commununication takes place by group based, where sensor nodes are deployed in groups and each group performs communication using keys. Therefore this paper reviews some of the potential key-management techniques in past for maintaining group based communication and extracts the research gap.
Array-Based Approaches for the Identification of Epigenetic Silenced Tumor Suppressor Genes

Carcinogenesis involves the inactivation or inhibition of genes that function as tumor suppressors. Deletions, mutations, or epigenetic silencing of tumor suppressor genes can lead to altered growth, differentiation, and apoptosis. DNA methylation and histone modifications are important epigenetic mechanisms of gene regulation and play essential roles both independently and cooperatively in tumor initiation and progression. Realization that many tumor suppressor genes are silenced by epigenetic mechanisms has stimulated discovery of novel tumor suppressor genes. One of the most useful of these approaches is an epigenetic reactivation screening strategy that combines treatment of cancer cells in vitro with DNA methyltransferase and/or histone deacetylase (HDAC) inhibitors, followed by global gene expression analysis using microarrays, to identify upregulated genes. This approach is most effective when complemented by microarray analyses to identify genes repressed in primary tumors. Recently, using cancer cell lines treated with a DNA methylation inhibitor and/or a HDAC inhibitor in conjunction with cDNA microarray analysis, candidate tumor suppressor genes, which are subject to epigenetic silencing, have been identified in endometrial, colorectal, esophageal, and pancreatic cancers. An increasing number of studies have utilized epigenetic reactivation screening to discover novel tumor suppressor genes in cancer. The results of some of the most recent studies are highlighted in this review.
Improving severity prediction on software bug reports using quality indicators

Recently, research has been conducted to explore the prediction schemes to identify the severity of bug reports. Several text mining approaches have been proposed to facilitate severity prediction. However, these studies mainly focus on the textual information of the bug reports. Other attributes of the bug reports have not been comprehensively discussed. In this paper, we investigate the influences of four quality indicators of bug reports in severity prediction. In an empirical study with the Eclipse dataset, the results show that considering these indicators can further improve the performance of a previous work employing only textual information.
Design pattern recovery in object-oriented software

An approach to recover object oriented design patterns from design and code is presented. The pattern recovery process is based on a multi-stage filtering strategy to avoid combinatorial explosion on large software systems. To maintain independence from the language and the case tools adopted in developing software, both design and code are mapped into an intermediate representation. The multi-stage searching strategy allows to safely determine pattern candidates. To assess the effectiveness of the pattern recovery process a portable environment written in Java has been developed. Based on this environment, experimental results on public domain and industrial software were obtained and are discussed in the paper. Evidence is shown that, by exploiting information about method calls as a further constraint beyond the structural ones, the number of false positives is reduced.
Randomized Iterative Hard Thresholding for Sparse Approximations

Summary form only given. Typical greedy algorithms for sparse reconstruction problems, such as orthogonal matching pursuit and iterative thresholding, seek strictly sparse solutions. Recent work in the literature suggests that given a priori knowledge of the distribution of the sparse signal coefficients, better results can be obtained by a weighted averaging of several sparse solutions. Such a combination of solutions, while not strictly sparse, approximates an MMSE estimator and can outperform strictly sparse solvers in terms of l-2 reconstruction error. We introduce a novel method for obtaining such an approximate MMSE estimator by replacing the deterministic thresholding operator of Iterative Hard Thresholding with a randomized version. We demonstrate the improvement in performance experimentally for both synthetic 1D signals and real images.
Energy-Momentum Conservation Laws in Hamiltonian Field Theory

In the Lagrangian field theory, one gets different identities for different stress energy-momentum tensors, e.g., canonical energy-momentum tensors. Moreover, these identities are not conservation laws of the above-mentioned energy-momentum tensors in general. In the framework of the multimomentum Hamiltonian formalism, we have the fundamental identity whose restriction to a constraint space can be treated the energy-momentum conservation law. In standard field models, this appears the metric energy-momentum conservation law.