Miracle-dz/UniFAR-FTUs huggingface dataset:
Induce Fetomaternal Tolerance TIM-3 Regulates Innate Immune Cells To

TIM-3 is constitutively expressed on subsets of macrophages and dendritic cells. Its expression on other cells of the innate immune system and its role in fetomaternal tolerance has not yet been explored. In this study, we investigate the role of TIM-3–expressing innate immune cells in the regulation of tolerance at the fetomaternal interface (FMI) using an allogeneic mouse model of pregnancy. Blockade of TIM-3 results in accumulation of inﬂammatory granulocytes and macrophages at the uteroplacental interface and upregulation of proinﬂammatory cytokines. Furthermore, TIM-3 blockade inhibits the phagocytic potential of uterine macrophages resulting in a build up of apoptotic bodies at the uteroplacental interface that elicits a local immune response. In response to inﬂammatory cytokines, Ly-6C hi G neg monocytic myeloid–derived suppressor cells expressing inducible NO synthase and arginase 1 are induced. However, these suppressive cells fail to downregulate the inﬂammatory cascade induced by inﬂammatory granulocytes (Ly-6C int G hi ) and apoptotic cells; the increased production of IFN- g and TNF- a by inﬂammatory granulocytes leads to abrogation of tolerance at the FMI and fetal rejection. These data highlight the interplay between cells of the innate immune system at the FMI and their inﬂuence on successful pregnancy in mice. The Journal of Immunology , 2013, 190: 000–000.
Oral and Maxillo-Facial Surgeons and Migraine

It all started with the pain following impacted wisdom tooth removal. This led me to develop techniques for the removal of impacted wisdom teeth through the smallest possible incisions, which resulted in minimal post-operative swelling[1]. Small incisions went against the current teaching at the time which was ‘Big Surgeon, Big Cut’. Despite the reduction in post-operative swelling, however, I was perplexed that patients still experienced the same amount of post-operative pain. What I found was that the post-operative pain was not so much related to the size of the incision nor to the amount of swelling – it was instead coming predominantly from the masseter muscles. I then discovered that the patients most prone to developing post-operative pain were those whose masseter muscles were tender to palpation prior to surgery. These patients concurrently had tenderness to percussion of the lower sevens. To reduce post-operative pain, I discovered that pre-operative occlusal adjustment of the tender sevens was all that was necessary.
Analyzing Tweets to Rank FIFA Players using Named Entity Recognition

Nowadays online media play significant role in sharing opinion on different occasions. People regularly share their experiences, feelings, and estimations. Opinions shared via online media can have impact on different issues. Twitter is a well known web-based media platform to express news simultaneously. Football is perhaps the most famous game all around the world. In this paper, an analysis hasbeen performed onFIFA top listed eight players of current time based on tweets from people. Sentiment analysis approach has been applied on tweets. A ranking system hasbeen designed by different computational and statistical meansto rank players.
TAR: Neural Logical Reasoning across TBox and ABox

Many ontologies, i.e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains. An ontology consists of an ABox, i.e., assertion axioms between two entities or between a concept and an entity, and a TBox, i.e., terminology axioms between two concepts. Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers. While previous NLR methods can give specific entity-level answers, i.e., ABox answers, they are not able to provide descriptive concept-level answers, i.e., TBox answers, where each concept is a description of a set of entities. In other words, previous NLR methods only reason over the ABox of an ontology while ignoring the TBox. In particular, providing TBox answers enables inferring the explanations of each query with descriptive concepts, which make answers comprehensible to users and are of great usefulness in the field of applied ontology. In this work, we formulate the problem of neural logical reasoning across TBox and ABox (TA-NLR), solving which needs to address challenges in incorporating, representing, and operating on concepts. We propose an original solution named TAR for TA-NLR. Firstly, we incorporate description logic based ontological axioms to provide the source of concepts. Then, we represent concepts and queries as fuzzy sets, i.e., sets whose elements have degrees of membership, to bridge concepts and queries with entities. Moreover, we design operators involving concepts on top of fuzzy set representation of concepts and queries for optimization and inference. Extensive experimental results on two real-world datasets demonstrate the effectiveness of TAR for TA-NLR.
On the robust isolated calmness of a class of nonsmooth optimizations on Riemannian manifolds and its applications

This paper studies the robust isolated calmness property of the KKT solution mapping of a class of nonsmooth optimization problems on Riemannian manifold. The manifold version of the Robinson constraint qualification, the strict Robinson constraint qualification, and the second order conditions are defined and discussed. We show that the robust isolated calmness of the KKT solution mapping is equivalent to the M-SRCQ and M-SOSC conditions. Furthermore, under the above two conditions, we show that the Riemannian augmented Lagrangian method has a local linear convergence rate. Finally, we verify the proposed conditions and demonstrate the convergence rate on two minimization problems over the sphere and the manifold of fixed rank matrices.
Hypergraphs with edge-dependent vertex weights: p-Laplacians and spectral clustering

We study p-Laplacians and spectral clustering for a recently proposed hypergraph model that incorporates edge-dependent vertex weights (EDVW). These weights can reflect different importance of vertices within a hyperedge, thus conferring the hypergraph model higher expressivity and flexibility. By constructing submodular EDVW-based splitting functions, we convert hypergraphs with EDVW into submodular hypergraphs for which the spectral theory is better developed. In this way, existing concepts and theorems such as p-Laplacians and Cheeger inequalities proposed under the submodular hypergraph setting can be directly extended to hypergraphs with EDVW. For submodular hypergraphs with EDVW-based splitting functions, we propose an efficient algorithm to compute the eigenvector associated with the second smallest eigenvalue of the hypergraph 1-Laplacian. We then utilize this eigenvector to cluster the vertices, achieving higher clustering accuracy than traditional spectral clustering based on the 2-Laplacian. More broadly, the proposed algorithm works for all submodular hypergraphs that are graph reducible. Numerical experiments using real-world data demonstrate the effectiveness of combining spectral clustering based on the 1-Laplacian and EDVW.
Human-to-Robot Manipulability Domain Adaptation with Parallel Transport and Manifold-Aware ICP

Manipulability ellipsoids efficiently capture the human pose and reveal information about the task at hand. Their use in task-dependent robot teaching - particularly their transfer from a teacher to a learner - can advance emulation of human-like motion. Although in recent literature focus is shifted towards manipulability transfer between two robots, the adaptation to the capabilities of the other kinematic system is to date not addressed and research in transfer from human to robot is still in its infancy. This work presents a novel manipulability domain adaptation method for the transfer of manipulability information to the domain of another kinematic system. As manipulability matrices/ellipsoids are symmetric positive-definite (SPD) they can be viewed as points on the Riemannian manifold of SPD matrices. We are the first to address the problem of manipulability transfer from the perspective of point cloud registration. We propose a manifold-aware Iterative Closest Point algorithm (ICP) with parallel transport initialization. Furthermore, we introduce a correspondence matching heuristic for manipulability ellipsoids based on inherent geometric features. We confirm our method in simulation experiments with 2-DoF manipulators as well as 7-DoF models representing the human-arm kinematics.
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks

Spiking neural network is a kind of neuromorphic computing that is believed to improve the level of intelligence and provide advantages for quantum computing. In this work, we address this issue by designing an optical spiking neural network and find that it can be used to accelerate the speed of computation, especially on combinatorial optimization problems. Here the spiking neural network is constructed by the antisymmetrically coupled degenerate optical parametric oscillator pulses and dissipative pulses. A nonlinear transfer function is chosen to mitigate amplitude inhomogeneities and destabilize the resulting local minima according to the dynamical behavior of spiking neurons. It is numerically shown that the spiking neural network-coherent Ising machines have excellent performance on combinatorial optimization problems, which is expected to offer new applications for neural computing and optical computing.
A Geometric Framework for Odor Representation

We present a generalized theoretical framework for olfactory representation and plasticity, using the theory of smooth manifolds and sheaves to depict categorical odor learning via distributed neural computation. Beginning with the space of all possible inputs to the olfactory system, we develop a dynamic model for odor learning that culminates in a perceptual space in which categorical odor representations are hierarchically constructed through experience, exhibiting statistically appropriate consequential regions and clear relationships between the broader and narrower identities to which a given odor might be assigned. The model reflects both the sampling-based physical similarity relationships among odorants, as observed in physiological receptor response profiles, and the acquired, learning-dependent perceptual similarity relationships among odors that can be measured behaviorally, and defines the relationship between them. Individual training and experience generates correspondingly more sophisticated odor identification capabilities. Because these odor representations are constructed from experience and depend on local, distributed plasticity mechanisms, geometries that fix curvature are insufficient to describe the capabilities of the system. This generative framework also encompasses hypotheses explaining representational drift in postbulbar circuits and the context-dependent remapping of perceptual similarity relationships.
Social Interactions for Autonomous Driving: A Review and Perspective

No human drives a car in a vacuum; she/he must negotiate with other road users to achieve their goals in social traffic scenes. A rational human driver can interact with other road users in a socially-compatible way through implicit communications to complete their driving tasks smoothly in interaction-intensive, safety-critical environments. This paper aims to review the existing approaches and theories to help understand and rethink the interactions among human drivers toward social autonomous driving. We take this survey to seek the answers to a series of fundamental questions: 1) What is social interaction in road traffic scenes? 2) How to measure and evaluate social interaction? 3) How to model and reveal the process of social interaction? 4) How do human drivers reach an implicit agreement and negotiate smoothly in social interaction? This paper reviews various approaches to modeling and learning the social interactions between human drivers, ranging from optimization theory and graphical models to social force theory and behavioral&cognitive science. We also highlight some new directions, critical challenges, and opening questions for future research.
Anomalous periodicity and parafermion hybridization in superconducting qubits

Topological quantum computation relies on a protected degenerate subspace enabling complicated operations in a noise-resilient way. To this end, hybrid platforms based on non-Abelian quasiparticles such as parafermions hold great promise. These are predicted to emerge at the interface between fractional quantum Hall states and superconductors and therefore naturally couple to superconducting qubits. Here, we study a parafermionic ﬂuxonium circuit and show that the presence of topological states yields a striking periodicity in the qubit spectrum. In addition, peculiar and marked signatures of diﬀerent parafermion coupling, associated with multiple tunneling of fractional quasiparticles, can be detected in the qubit microwave spectrum. Finite parafermion coupling can reduce the full degeneracy of the non-Abelian manifold, and we show that this conﬁguration can be used to assess the remaining degree of protection of the system.
Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.
Uncertainty-guided Source-free Domain Adaptation

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliable. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation. For this, we construct a probabilistic source model by incorporating priors on the network parameters inducing a distribution over the model predictions. Uncertainties are estimated by employing a Laplace approximation and incorporated to identify target data points that do not lie in the source manifold and to down-weight them when maximizing the mutual information on the target data. Unlike recent works, our probabilistic treatment is computationally lightweight, decouples source training and target adaptation, and requires no specialized source training or changes of the model architecture. We show the advantages of uncertainty-guided SFDA over traditional SFDA in the closed-set and open-set settings and provide empirical evidence that our approach is more robust to strong domain shifts even without tuning.
Optimal Gamma density to Obfuscate Quantitative data with Added Noise

Protecting the privacy of individuals in a data-set is no less important than making statistical inferences from it. In case the data in hand is quantitative, the usual way to protect it is to add a noise to the individual data values. But, what should be an ideal density used to generate the noise, so that we can get the maximum use of the data, without compromising privacy? In this paper, we deal with this problem and propose a method of selecting a density within the Gamma family that is optimal for this purpose.
Derivation of Hyperbolic Transfer Equations from BGK-Equation

Institute of Mathematics and Computer Science,University of Latvia, Rain¸a bulv. 29, R¯iga, LV-1459, LATVIA(Dated: 2nd February 2008)We use the integral form of the Boltzmann equation which allows us to take into account thememory eﬀects using the initial condition that selects the solutions going to the local equilibriumMaxwell distribution in the t → −∞ limit. Implementing the relaxation-time approximation forthe collision integral (BGK-equation) we present the derivation of the hyperbolic Navier-Stokes andthe hyperbolic heat conduction equations in the ﬁrst order approximation. It is shown that therelaxation time in the obtained hyperbolic equations is the Maxwellian relaxation time. As specialcase we obtain the telegraph equation for the heat propagation in static medium and estimate therelaxation time for the heat conduction in some materials.
Assessment of Worst GEO Plasma Environmental Models for Spacecraft Surface Charging by SPIS

Assessment of the worst plasma environment for spacecraft surface charging in the geosynchronous Earth orbits (GEO) is important for spacecraft designs and operations, because it could cause spacecraft anomalies due to surface charging with resultant discharging arcs. The differential charging potential of spacecraft surfaces is considered to generate the harmful discharging arcs. There was no common standard of the worst GEO plasma environment for analysis of differential surface charging to prevent and mitigate the anomalies. Therefore in order to determine a new International Organization for Standardization (ISO) document for the purpose, a round-robin simulation was performed using the NASCAP-2k and MUSCAT. We perform surface charging simulations by Spacecraft Plasma Interaction Software (SPIS) as same as the round-robin simulation and evaluate the worst GEO plasma environmental models showing the detailed results. This study will contribute to a revision of the ISO document and also apply spacecraft charging risk estimations as space weather forecast.
Framework for Opinion Mining from Web Blogs

: Last two decade content generations on the web are phenomenon due to Web 2.0. It gives the platform to exchange the ideas, views, thought, experience, opinion, share information, likes and dislikes to the millions of peoples using Blogs, reviews, and other social network sites. Since the characteristics of blog are different than the normal webpage therefore we require the different technique to apply on blog data to extract the latent knowledge discovery from the blogs. The scope of this Paper limits to the discussion of analysis and mining of opinion from blogs. In this Paper an attempt has been made to describe and discuss various analysis and mining techniques used for extracting opinion from blogs. We have purposed a framework for opinion mining from blogs. The Paper also discusses various issues and challenges in analyzing and mining the blogs as well as future scope of research.
Volume- 2, Issue- 6

In Ayurveda there are many classical herbs frequently used in the treatment of diabetes mellitus type-II. In recent years, some new herbs have showed potential for the management of DM-II. One of such herbs is Withania coagulans Dunal. (Doda Paneer) which has profound hypoglycemic activity. W. coagulans has been acclaimed Rishyagandha in Ayurveda and used for nervine tonic, debility, insomnia, aphrodisiac, and rejuvenator. Rishyagandha (W. coagulans) is described in Charaka Samhita in “Brihaniya Mahakashaya”and “Madhur Skandha Dravya”. Acharya Charaka has described two types of therapies for Prameha, Brihana for krish and dourbala pramehi and Samshodhana for sthula and balvan pramehi. So in krisha and dourbala patient, Rishyagandha improves the Volume2, Issue6 Review Artical
Ubiquitination in Plant Meiosis

Meiosis is a specialized cell division which is essential to sexual reproduction. The success of this highly ordered process involves the timely activation, interaction, movement, and removal of many proteins. Ubiquitination is an extraordinarily diverse post-translational modiﬁcation with a regulatory role in almost all cellular processes. During meiosis, ubiquitin localizes to chromatin and the expression of genes related to ubiquitination appears to be enhanced. This may be due to extensive protein turnover mediated by proteasomal degradation. However, degradation is not the only substrate fate conferred by ubiquitination which may also mediate, for example, the activation of key transcription factors. In plant meiosis, the speciﬁc roles of several components of the ubiquitination cascade—particularly SCF complex proteins, the APC/C, and HEI10— have been partially characterized indicating diverse roles in chromosome segregation, recombination, and synapsis. Nonetheless, these components remain comparatively poorly understood to their counterparts in other processes and in other eukaryotes. In this review, we present an overview of our understanding of the role of ubiquitination in plant meiosis, highlighting recent advances, remaining challenges, and high throughput methods which may be used to overcome them.
Dental Aesthetics and Attitudes among University Students in Saudi Arabia-A Cross-Sectional Study

Objective: The objectives of this research were to investigate the level of satisfaction, knowledge, attitudes, problems and needs related to dental aesthetic among students of King Saud bin Abdulaziz University for Health Sciences. Methods: We used cross-sectional study design. Self-administered questionnaire were distributed among males and females dental students of first year pre professionals. The effect of parents’ occupations, geographical regions, gender and type of schooling on the level of knowledge, attitudes, problems and needs related to dental aesthetics were also determined. Results: A total of 294 students participated in the study. We found 45% of the students heard about dental aesthetics from their families and friends. Out of the total 74% subjects with non-working mother had more negative attitudes toward dental aesthetics. The most common problem reported was having protruded teeth (69%). The most common treatment need of the subjects was teeth whitening (82%) and of total 72% subjects with non-working mother had more need for treatments. Conclusion: The majority of students showed a high level of knowledge regarding dental aesthetics. On the other hand, a low level of positive attitudes regarding dental aesthetics was observed. Awareness program may be launched for parents and young students for promoting positive attitudes towards aesthetics needs, and dental screenings may be planned so that different aesthetic requirements and needs of students may be assessed as early interventions result in better oral and dental aesthetics. produce the final aesthetic effect [5]. regionally to the aesthetic requirements of the individual. Study conducted of that of the tooth than that their This concluded that of of
OPTIMAL _-_ COVE

The existing q-Markov COVER realization theory does not take into account the problems of arithmetic errors due to both the quantization of states and coefficients of the reduced order model. All q-Markov COVERs allow some freedom in the choice of parameters. In this paper we exploit this freedom in the existing theory to optimize the models with respect to thes,_ finite wordlength effects.