Miracle-dz/UniFAR-FTUs huggingface dataset:
Preventive Effects of Bee Venom Derived Phospholipase A2 on Oxaliplatin-Induced Neuropathic Pain in Mice

Oxaliplatin, a chemotherapy drug used to treat colorectal cancer, induces specific sensory neurotoxicity signs that are aggravated by cold and mechanical stimuli. Here we examined the preventive effects of Bee Venom (BV) derived phospholipase A2 (bvPLA2) on oxaliplatin-induced neuropathic pain in mice and its immunological mechanism. The cold and mechanical allodynia signs were evaluated by acetone and von Frey hair test on the hind paw, respectively. The most significant allodynia signs were observed at three days after an injection of oxaliplatin (6 mg/kg, i.p.) and then decreased gradually to a normal level on days 7–9. The oxaliplatin injection also induced infiltration of macrophages and upregulated levels of the pro-inflammatory cytokine interleukin (IL)-1β in the lumbar dorsal root ganglia (DRG). Daily treatment with bvPLA2 (0.2 mg/kg, i.p.) for five consecutive days prior to the oxaliplatin injection markedly inhibited the development of cold and mechanical allodynia, and suppressed infiltration of macrophages and the increase of IL-1β level in the DRG. Such preventive effects of bvPLA2 were completely blocked by depleting regulatory T cells (Tregs) with CD25 antibody pre-treatments. These results suggest that bvPLA2 may prevent oxaliplatin-induced neuropathic pain by suppressing immune responses in the DRG by Tregs.
Automatic detection of exudates in retinal images using histogram analysis

Diabetic Retinopathy (DR) is the major cause of blindness caused by the damage to the blood vessels in the retina from diabetes. It cannot be prevented but early detection through fundus imaging by an ophthalmologist can prevent further vision loss. Presence of microaneurysms, hemorrhages, cotton-wool spots and exudates are the symptoms of mild DR. Of these, the detection of exudates is one of the important factors in the early diagnosis of DR. Exudates are fatty deposits on the retina which appear as yellowish regions in fundus image. Fundus images show considerable variation in brightness which makes automatic detection of exudates difficult. In this study, we are proposing a new method for preprocessing and false positive elimination towards the reliable detection of exudates. The brightness of the fundus image was changed by the nonlinear curve with brightness values of the hue saturation value (HSV) space. To emphasize brighter yellow regions (exudates), gamma correction was performed on each red and green components of the image. Subsequently, the histograms of each red and green component were extended. After that, the exudates candidates were detected using histogram analysis. Finally, false positives were removed by using multi-channel histogram analysis. To evaluate the new method for the detection of exudates, we examined 158 fundus images, including 84 abnormal images with exudates and 74 normal images. The sensitivity and specificity for the detection of abnormal and normal cases were 88.45% and 95.5% respectively.
Weight Matters: Health Educators' Knowledge of Obesity and Attitudes Toward People Who Are Obese

Background: Weight bias has been documented throughout society among clinicians as well as obese persons. Purpose: The purpose of this study was to assess Health Educators' professional philosophies, knowledge of obesity, and beliefs in a just world (meaning that people get what they deserve) in relation to their attitudes toward people who are obese. Methods: Members of the Health Education Directory, a national listserv, were solicited to participate as a convenience sample for this study. Participants completed a survey that consisted of (1) an item to assess Health Educators' professional philosophies; (2) items from the Anti-fat Attitudes Scale, the Global Belief in a Just World Scale, and the Obesity Knowledge Scale; and (3) demographic items (i.e., age, sex, educational attainment, and personal weight status). Results: Results revealed that participants embracing the behavior change philosophy scored higher on the Anti-fat Attitudes Scale than participants espousing other philosophies. Those who believed more strongly in a just world were more likely to hold negative attitudes toward obese persons. Discussion: When people feel stigmatized by Health Educators, prevention efforts are compromised. Translation to Health Education Practice: This research provides insights that will enable Health Educators to better serve their overweight and obese clients.
The New EasyLiving Project at Microsoft Research

EasyLiving is a new project in intelligent environments at Microsoft Research. We are working to make computing more accessible and more pervasive than today’s desktop computer. More specifically, our goal is to develop a prototype architecture and technologies for building intelligent environments that facilitate the unencumbered interaction of people with other people, with computers, and with devices. This paper describes our goals, design decisions, and applications of EasyLiving. 1. What Is Next in PCs?
Smart-Home Energy Management in the Context of Occupants' Activity

Energy consumption and occupants' comfort are key factors when evaluating smart-home environments. This paper focuses on occupants' comfort, which is affected by environmental factors (such as temperature, humidity, radiation of elements, and air movement), and occupant-related factors (such as occupants' level of activity, clothing insulation). To satisfy a thermal comfort objective, energy is needed for heating and cooling, which affects energy consumption. This paper presents a proof-of-concept analysis of smart-home control based on occupants' activity level in terms of human energy expenditure, and a trade-off analysis of the energy consumption versus thermal comfort when the activity level serves as an input into an intelligent home energy management system.
Learning by Collaborative and Individual-Based Recommendation Agents

Intelligent recommendation systems can be based on two basic principles: collaborative filters and individual-based models. In this work we examine the learning function that results from these two general types of learning smart agents. There has been significant work on the predictive properties of each type, but no work has examined the patterns in their learning from feedback over repeated trials. Using simulations, we create clusters of "consumers" with heterogeneous utility functions and errorful reservation utility thresholds. The consumers go shopping with one of the designated smart agents - purchasing products they like and rejecting ones they do not. Based on this feedback to their recommendations, agents learned about the consumers and potentially improve the quality of their recommendations. We characterize learning curves by modified exponential functions with an intercept for percent of recommendations accepted trial 0, an asymptotic rate of recommendation acceptance, and a rate at which learning moves from intercept to asymptote. We compare the learning of a baseline random recommendation agent, an individual-based logistic regression agent, and two types of collaborative filters that rely on k-means clustering and nearest neighbor algorithms popular in most commercial applications. Compared to collaborative filtering agents, individual models: 1) learn slower initially, but perform better in the long run when the environment is stable; 2) are less negatively affected by permanent changes in the individual's utility function; 3) are less adversely affected by error in the reservation threshold to which consumers compare a recommended product's utility. K-means filters reach a lower asymptote but approach it faster, reflecting a surprising stickiness of target classifications after feedback from recommendations made under initial (incorrect) hypotheses. Feedback was more diagnostic for individual models.
A Newton Basis Gmres Implementation

The GMRES method by Saad and Schultz is one of the most popular iterative methods for the solution of large sparse non-symmetric linear systems of equations. The implementation proposed by Saad and Schultz uses the Arnoldi process and the modified Gram-Schmidt (MGS) method to compute orthonormal bases of certain Krylov subspaces. The MGS method requires many vector-vector operations, which can be difficult to implement efficiently on vector and parallel computers due to the low granularity of these operations. We present a new implementation of the GMRES method in which, for each Krylov subspace used, we first determine a Newton basis, and then orthogonalize it by computing a QR factorization of the matrix whose columns are the vectors of the Newton basis. In this way we replace the vector-vector operations of the MGS method by the task of computing a QR factorization of a dense matrix. This makes the implementation more flexible, and provides a possibility to adapt the computations to the computer at hand in order to achieve better performance.
Exploring the Role of Problem-Based Learning in Developing Conflict Resolving and other Soft Skills - A Quasi-Experimental Study

Problem-based learning is widely regarded as a successful and innovative method of instruction for skills development in the recent decades. This empirical study therefore, intends to explore the perception of employees and assess the impact of three days PBL approach based training on the soft skills and traits of employees. A mixed method research approach was used for the study; the data for the study therefore, emerged from the semi-structured interviews, 53 items based survey, and observation of the training process. The employees belonging to the three development organizations from the area of Islamabad and Peshawar in Pakistan served as the sample of the study. Interviews and observation were transcribed and analyzed through thematic analysis while the data from the survey was processed in SPSS. The Findings from this study reveal the significant role of PBL in generating conflict resolving skills and other soft skills in employees. Keywords: Problem-based learning, Skill development, soft skills, traits, workplace, employees JEL Classifications: I2, P36
Internet Service and Maintenance for RP-Oriented Tele-Manufacturing

In this paper a methodology is put forward to create an Internet-based infrastructure to service and maintain RP-oriented tele- manufacturing. The WWW-based Java-enabled Internet computing model is used for implementing remote part submitting, queuing, monitoring, and managing. Control of different access competences allows manufacturing sites and queues to be maintained respec tively in any place. A Java-based software testbed has been developed to serve as a verification platform.
Learning belief networks from data: an information theory based approach

This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.  2002 Elsevier Science B.V. All rights reserved.
A Study of the 1942 Fly Population of New Haven *

Inasmuch as the virus of poliomyelitis has been found in flies,5' 6, 7, 10 the question of the importance of these insects as vectors in this disease has been reopened. For this reason a limited survey of the fly population within a given locality in the city of New Haven, Connecticut, was made during the summer of 1942, a nonepidemic year for poliomyelitis in this vicinity.t There are few exact data concerning the seasonal changes in the populations of flies in New England, although studies of the myiasis-producing Diptera have been reported recently from western states.2'
Observability analysis of 2D geometric features using the condition number for SLAM applications

Observability analysis is a very powerful tool for discriminating whether a robot can estimate its own state. However, this method cannot investigate how much of the system is observable. This is a major problem from a state estimation perspective because there is too much noise in real environments. Therefore, although the system (or a mobile robot) is observable, it cannot estimate its own state. To address this problem, we propose an observability analysis method that uses the condition number. Mathematically, the condition number of matrix represents a degree of robustness to noise. We utilize this property of the condition number to investigate the degree of observability. In other words, the condition number of the observability matrix demonstrates the feasibility of state estimation and the robustness of its feasibility for estimation.
Affine projections of polynomials

An m-variate polynomial f is said to be an affine projection of some n-variate polynomial g if there exists an n×m matrix A and an n-dimensional vector b such that f(x) = g(Ax + b). In other words, if f can be obtained by replacing each variable of g by an affine combination of the variables occurring in f , then it is said to be an affine projection of g. Given f and g can we determine whether f is an affine projection of g? Some well known problems (such as VP versus VNP and matrix multiplication for example) are instances of this problem. The intention of this paper is to understand the complexity of the corresponding computational problem: given polynomials f and g find A and b such that f = g(Ax + b), if such an (A,b) exists. We first show that this is an NP-hard problem. We then focus our attention on instances where g is a member of some fixed, well known family of polynomials so that the input consists only of the polynomial f(x) having m variables and degree d. We consider the situation where f(x) is given to us as a blackbox (i.e. for any point a ∈ F we can query the blackbox and obtain f(a) in one step) and devise randomized algorithms with running time poly(mnd) in the following special cases: (1) when f = Permn(Ax + b) and A satisfies rank(A) = n. Here Permn is the permanent polynomial. (2) when f = Detn(Ax + b) and A satisfies rank(A) = n. Here Detn is the determinant polynomial. (3) when f = Pown,d(Ax + b) and A is a random n×m matrix with d = n. Here Pown,d is the power-symmetric polynomial of degree d. (4) when f = SPSn,d(Ax + b) and A is a random (nd) × m matrix with n constant. Here SPSn,d is the sum-of-products polynomials of degree d with n terms. Acknowledgments. The author would like to thank : Ketan Mulmuley and Milind Sohoni for suggesting the use of the corresponding lie algebras for equivalence to the determinant, K V Subrahmanyam for his nice lectures on representation theory and explaining aspects of the GCT approach, Michael Forbes for pointing out the relationship between symmetric rank and tensor rank, Shubhangi Saraf and Srikanth Srinivasan for discussions pertaining to projections of the sum of products polynomial. The author would also like to thank the organizers of the Geometric Complexity Theory workshop for their kind invitation. ∗Microsoft Research India, neeraka@microsoft.com
A Full-Band Spherical Harmonics Expansion of the Valence Bands up to High Energies

The valence bands of silicon can be expanded exactly by spherical harmonics only up to the energy, where the bands reach the surface of the Brillouin zone (BZ). For higher energies an approximation is required. Therefore an anisotropic extension of the band structure is presented which is determined by matching the density of states (DOS) and moments of the inverse group velocity of the exact full-band (FB) structure. The Boltzmann equation (BE) based on the exact FB at low energies and the approximation at high energies is solved by the spherical harmonic expansion (SHE) method for bulk silicon including impact ionization. The results are compared to Monte Carlo (MC) data based on the exact FB structure
Use of Molecular Dynamics for the Refinement of an Electrostatic Model for the In Silico Design of a Polymer Antidote for the Anticoagulant Fondaparinux

Molecular dynamics (MD) simulations results are herein incorporated into an electrostatic model used to determine the structure of an effective polymer-based antidote to the anticoagulant fondaparinux. In silico data for the polymer or its cationic binding groups has not, up to now, been available, and experimental data on the structure of the polymer-fondaparinux complex is extremely limited. Consequently, the task of optimizing the polymer structure is a daunting challenge. MD simulations provided a means to gain microscopic information on the interactions of the binding groups and fondaparinux that would have otherwise been inaccessible. This was used to refine the electrostatic model and improve the quantitative model predictions of binding affinity. Once refined, the model provided guidelines to improve electrostatic forces between candidate polymers and fondaparinux in order to increase association rate constants.
Machine Learning for Mention Head Detection in Multilingual Coreference Resolution

This work introduces a machine learning approach to the identification of mention heads needed for multilingual coreference resolution (MCR). We evaluate the method and compare it to a heuristic baseline and a rule-based approach, which are widely used in coreference resolution systems. We use the CoNLL-2012 shared task data sets, which include data for Arabic, Chinese, and English. We show that for MCR, machine learning offers a competitive, flexible, and robust solution for mention head detection.
Improving Inter-Task Communication Performance on Multi-Core Packet Processing Platform

Software data plane has been regarded as the main hardware platform for next generation network infrastructure due to its well programmability and flexibility. However, current software lacks support of high performance intertask communication mechanism, which has been one of main limitations on software packet processing. Thus, this paper puts forward a lockless communication mechanism, which is realized through lockless circular queues. And Through building independent communication channel between any pair of network function, all network functions connected with our mechanism could avoid most overhead caused by synchronization and mutual exclusion. And through packet batching, our solution could achieve high throughput. At last, we build a prototype to verify the execution efficiency of our system.
Mutation Analysis for Security

Security has become, nowadays, a major concern for the organizations as the majority of its applications are exposed to Internet, which increases the threats of security considerably. Thus, the solution is to improve tools and mechanisms to strengthen the protection of applications against attacks and ensure the dierent security objectives. Among solutions we will talking about, in this paper, there is Mutation Analysis which is a technique of test that evaluates the quality of software tests and their ability to detect errors, It also compares the criteria and test generation strategies. In this study we will use the Mutation Analysis as a mean to qualify the penetration tests, and then, apply this technique in the security mechanisms and exactly on the mechanisms of access control. At the end we will propose a method for the elimination of hidden mechanisms for access control that will allow the access control policy to evolve.
Cervicovaginal Lamina Propria Lymphocytes: Phenotypic Characterization and Their Importance in Cytotoxic T-Lymphocyte Responses to Simian Immunodeficiency Virus SIVmac251

ABSTRACT Most human immunodeficiency virus (HIV) type 1 infections occur by the mucosal route. Thus, it is important to assess the immune responses to HIV in the vaginal, cervical, and rectal compartments. Here we quantitated the virus-specific CD8+ T-cell response and characterized the phenotype of lymphocytes in the genital tracts of naive macaques, macaques acutely or chronically infected with simian immunodeficiency virus SIVmac251, and macaques chronically infected with chimeric simian/human immunodeficiency virus SHIVKU2. Vaginal biopsy samples or samples obtained at the time of euthanasia were used in this analysis. The percentage of Gag-specific, tetramer-positive T cells was as high as 13 to 14% of the CD3+ CD8+ T-cell population in the vaginal and cervical laminae propriae of both SIVmac251 and SHIVKU2 chronically infected macaques. In most cases, the frequency of this response in the cervicovaginal compartment far exceeded the frequency in the blood or the draining iliac lymph node. Vaginal laminae propriae of naive macaques contained 55 to 65% CD3+ CD8+ cells and 28 to 34% CD3+ CD4+ cells, while the majority of intraepithelial cells were CD8+ T cells (75 to 85%). For the same cells, the surface expression of CD62L was low whereas that of αEβ7 was high. No difference in the expression of CD45RA on CD8+ T cells was observed in the chronic stage of SIVmac251 infection. Although no decrease in the percentage of CD4+ cells in the genital tract was observed within the first 12 days of infection, by 6 weeks from SIVmac251 infection and thereafter the percentage of CD4+ T cells was decreased in the laminae propriae of the vagina and cervix. Expression of CD45RA did not differ in naive and acutely SIVmac251 infected macaques. Information on the quality and quantity of local immune responses may help in the design of vaccine strategies aimed at containing viral replication at the site of viral encounter.
A Practical Framework for Privacy-Preserving Data Analytics

The availability of an increasing amount of user generated data is transformative to our society. We enjoy the benefits of analyzing big data for public interest, such as disease outbreak detection and traffic control, as well as for commercial interests, such as smart grid and product recommendation. However, the large collection of user generated data contains unique patterns and can be used to re-identify individuals, which has been exemplified by the AOL search log release incident. In this paper, we propose a practical framework for data analytics, while providing differential privacy guarantees to individual data contributors. Our framework generates differentially private aggregates which can be used to perform data mining and recommendation tasks. To alleviate the high perturbation errors introduced by the differential privacy mechanism, we present two methods with different sampling techniques to draw a subset of individual data for analysis. Empirical studies with real-world data sets show that our solutions enable accurate data analytics on a small fraction of the input data, reducing user privacy risk and data storage requirement without compromising the analysis results.
The Effect of Information on Voter Turnout: Evidence from a Natural Experiment

Do better informed people vote more? Recent theories of voter turnout emphasize a positive effect of being informed on the propensity to vote, but the possibility of endogenous information acquisition makes estimation of causal effects difficult. I estimate the causal effects of being informed on voter turnout using unique data from a natural experiment Copenhagen referendum on decentralization. Four of fifteen districts carried out a pilot project, exogenously making pilot city district voters more informed about the effects of decentralization. Empirical estimates based on survey data confirm a sizeable and statistically significant causal effect of being informed on the propensity to vote.