Miracle-dz/UniFAR-FTUs huggingface dataset:
Subspace Model Based Discriminative Instances Selection for Weakly Supervised Object Detection

Object detection from images is generally achieved through a supervised learning manner. However, in many real applications, to provide instance level label is still costly. Thus, weakly supervised approach is proposed and naturally cast as a Multiple Instance Learning (MIL) problem. Traditional MIL methods typically learn discriminative classifiers from positive and negative training bags. Alternatively, we propose to select more discriminative instances for learning classifiers to further improve detection accuracy. With the candidate set of positive instances, we can also train a Smoothing Latent Support Vector Machine (SLSVM) to finally detect objects from a bag of instances. We observed that object instances of a common category are visually similar and when characterized as high-dimensional feature representations, they approximately lie in a low-dimensional subspace. Therefore, we propose a formulation optimizes a labeling variable for each positive image and learns the subspace model by minimizing rank (via convex surrogate function) of the coefficient matrix associated with the subspace model. To improve discriminative power, we also promote incoherence between the subspace model and some "hard" negative instances by utilizing a ε-insensitive loss. For this non-convex problem, we resort to block coordinate descent and Alternating Direction Method of Multipliers(ADMM) to get local optimal solutions. The promising empirical studies on real data sets demonstrate that our proposed method is superior to the state-of-the-art weakly supervised object detection approaches.
Relativistic Flows after Shock Emergence

We investigate relativistic flows that occur after a shock wave generated inside a star reaches the surface. First, the effect of sphericity is included through successive approximations by adding correction terms to an already known self-similar ultrarelativistic solution for plane-parallel geometry. The inclusion of sphericity increases the early acceleration compared with the original plane-parallel flow. Second, we obtain semianalytic solutions for a mildly relativistic flow in which the rest-mass energy density is nonnegligible in the equation of state (EOS). To take this into account, we use enthalpy and pressure instead of density and pressure as thermodynamic variables. These solutions assume self-similar evolution except for the initial conditions. Third, we carry out numerical calculations with a special relativistic hydrocode based on Godunov's method to check the applicability of the sphericity corrections and semianalytic solutions. The EOS used in our calculations includes the rest-mass energy density. Comparisons with the numerical calculations support the validity of the sphericity corrections. The evolution of the pressure and Lorentz factor of each fluid element in the semianalytic solution for mildly relativistic flows matches the numerical results, at least for early phases. We also numerically investigate the final free-expansion phase. For spherically symmetric or plane-parallel ultrarelativistic flows, we could not observe this phase even when the self-similar variables grew to 106. However, flows in which the ratio of pressure to density at shock breakout is less than unity did reach free expansion. We derive the final energy distributions for these flows and compare with previous work.
Pokémon Go as a Positive Virtual Reality Game: Promoting Cognitive, Affective, and Empathic Benefits

Video game popularity and time playing in children, adolescents, and adults is steadily increasing due to heightened accessibility, advanced technological game design, and a rising sedentary lifestyle among Americans. The advent of exergames and virtual reality paradigms has led to a new wave of mobile video games that can be played anywhere, involve the combination of mobility and gaming, and may be used to improve cognition, affect, and perhaps empathy. The aim of the present study was to examine if the exergame Pokémon Go would improve visual and verbal working memory, attention, positive and negative affect, and empathy. Additionally, the current study is an extension of seminal research that discovered being in nature alone has positive effects on working memory and affect. Participants (N = 62) from a Florida University were assessed on the Alloway Working Memory Assessment (AWMA), the Positive and Negative Affect Schedule (PANAS), and the Interpersonal Reactivity Index (IRI) before and after playing Pokémon Go outdoors around the most natural parts of the campus (e.g., ponds, dense foliage). The participants then returned several days later, completed the assessments, and spent time outdoors not playing. The study was counterbalanced over the course of a year to control for seasonal differences. Main findings included increased verbal working memory scores and decreased negative affect after playing Pokémon Go with no changes in empathy. The results have important implications for those interested in using Pokémon Go to improve working memory and decrease stress and negative affect in adult populations.
BPMO Requirements Analysis and Design

This deliverable is concerned with Semantic Business Process Management (abbreviated as SBPM), especially its cornerstone Business Process Modeling Ontology (BPMO). Basically, the document focuses on the requirements analysis and design of BPMO, which is considered as the semantic description framework for business process in the SemBiz project. To determine the description requirements for business process management, we analyze business use cases from the industrial partners and abstract a generalized process scenario. For realizing the determined requirement list, we propose the comprehensive BPMO framework, and finally make it feasible with the extension of the semantic Web service description framework WSMO. Although the main framework for BPMO has been determined, some details may be modified in the following period, based on the BPMO real implementation with the use cases provided by the industrial partners.
An Event-Driven Sensor Architecture for Low Power Wearables

In this paper we define a software architecture for low power wearable computers. The architecture is event driven, and is designed so that application programs have access to sensor data without the need for polling. The software architecture is designed to allow for an underlying hardware architecture that can achieve maximal power efficiency by switching off parts of the hardware that are known not to be required.
Low-dimensional Query Projection based on Divergence Minimization Feedback Model for Ad-hoc Retrieval

Low-dimensional word vectors have long been used in a wide range of applications in natural language processing. In this paper we shed light on estimating query vectors in ad-hoc retrieval where a limited information is available in the original query. Pseudo-relevance feedback (PRF) is a well-known technique for updating query language models and expanding the queries with a number of relevant terms. We formulate the query updating in low-dimensional spaces first with rotating the query vector and then with scaling. These consequential steps are embedded in a query-specific projection matrix capturing both angle and scaling. In this paper we propose a new but not the most effective technique necessarily for PRF in language modeling, based on the query projection algorithm. We learn an embedded coefficient matrix for each query, whose aim is to improve the vector representation of the query by transforming it to a more reliable space, and then update the query language model. The proposed embedded coefficient divergence minimization model (ECDMM) takes top-ranked documents retrieved by the query and obtains a couple of positive and negative sample sets; these samples are used for learning the coefficient matrix which will be used for projecting the query vector and updating the query language model using a softmax function. Experimental results on several TREC and CLEF data sets in several languages demonstrate effectiveness of ECDMM. The experimental results reveal that the new formulation for the query works as well as state-of-the-art PRF techniques and outperforms state-of-the-art PRF techniques in a TREC collection in terms of MAP,P@5, and P@10 significantly.
Leukemia Cancer Mortality Trend in Iran, From 1995 to 2004

Background Cancer is the third most common cause of death in Iran, the leukemia cancer is one of the most important causes of cancer mortality. Regarding cancer mortality, data would be important to monitor the program screening effects, earlier diagnosis, demographic data and other prognostic factors. The aim of this study was mortality rates evaluating, then leukemia cancer trends among the Iranian population within almost a period of a decade, i.e. from 1995 to 2004. Methods National death Statistic Reported by Ministry of Health and Medical Education (MOH&ME) from 1995 to 2004, stratified by age group, sex, and cause of death, have included in this study. Leukemia cancer has expressed as the annual mortality rates/100,000, in general, and/or per gender, and age group. Results The general mortality rate of leukemia cancer has slightly increased within the mentioned study period, from 0.44 to 2.54, then leukemia cancer mortality has often seen in men more than women. Conclusion The mortality rate of leukemia has significantly increased throughout Iran. Associated risk factors with leukemia have headmost identified for their prevention and control. So, future studies to reveal leukemia risk factors among the Iranian population would be crucial in order to control its burden.
Reduced Set KPCA for Improving the Training and Execution Speed of Kernel Machines

This paper presents a practical, and theoretically wellfounded, approach to improve the speed of kernel manifold learning algorithms relying on spectral decomposition. Utilizing recent insights in kernel smoothing and learning with integral operators, we propose Reduced Set KPCA (RSKPCA), which also suggests an easy-toimplement method to remove or replace samples with minimal effect on the empirical operator. A simple data point selection procedure is given to generate a substitute density for the data, with accuracy that is governed by a user-tunable parameter `. The effect of the approximation on the quality of the KPCA solution, in terms of spectral and operator errors, can be shown directly in terms of the density estimate error and as a function of the parameter `. We show in experiments that RSKPCA can improve both training and evaluation time of KPCA by up to an order of magnitude, and compares favorably to the widely-used Nystrom and density-weighted Nystrom methods.
Physical attractiveness mediates the relationship between resting heart rate variability and mate value

The present study had two main purposes: (a) to replicate the findings of Vacharkulsemsuk and colleagues (2016) and Joel and colleagues (2017) wherein body language and physical attractiveness each predict desirability, and (b) to determine if body language and physical attractiveness mediate the observed link between heart rate variability (HRV) and mate value as reported by Bailey (2017). A total of 42 university students attended a laboratory session where they rated soundless video monologues of 192 students describing their relationship experiences. Ratings of expansive body language and physical attractiveness were found to predict rater’s desire to go on a future date with ratees. Furthermore, physical attractiveness mediated the HRV – mate value link. These results suggest that physical attractiveness acts as a proxy to communicate information about the HRV of a potential date in the mating marketplace.
Classroom Simulation : Distributions of Ties Induced by Rounding Continuous Data

Graphical and numerical illustrations of the effects of moderate to severe rounding of normal data on the values of test statistics, actual significance level, and power for one-sample t tests and paired t tests. Goodness-of-fit tests confirm that rounded normal data are not normal. Combinatorial computations, simulations, and graphs are made using R. The level of programming, exposition, and suggested extensions is suitable for upper division and MS level statistics students.
**title** Asp Conference Series, Vol. **volume***, **year of Publication** **names of Editors** Black Holes and Pulsar Binaries

Stellar mass black holes are formed in the field, and are observed in binaries. Population synthesis estimates for the formation of BH-PSR binaries suggest these may be observed, but with very large formal uncertainties in the formation rate. In globular clusters, exchanges and binary interactions present more channels for BH-PSR formation, while ejection through recoil can remove a large fraction of the initial black hole population. A BH-PSR system ought to be observed in the near future, most likely in a globular cluster. 1. The Field We have observed several stellar mass black hole binary candidates in the field (cf Bailyn et al 1998). Current best estimates are that the minimum ZAMS for black hole formation is of the order M 0 ∼ 25M ⊙ , and that the cutoff mass may have significant metallicity dependence, with higher cutoff and narrower range of masses for black hole formation at higher metallicities (Fryer & Kalogera 2001, Fryer et al 2002). Possibly no stars, or only a very small proportion of stars, form black holes at super-solar metallicities. It is also possible that Pop III stars formed intermediate mass (M BH > ∼ 100M ⊙) black holes directly in significant numbers 1999). We infer that the fraction of black hole progenitor systems in binaries is comparable to that of other field stars, and a significant fraction of black holes remain bound to their companion after formation. In the case of a neutron star companion, the natal kick on formation reduces the probability of a bound system, but in general, for plausible amplitude kicks, a finite fraction of systems remains bound. This is also the case if black holes receive natal kicks in some fraction of instances (Brandt, Podsiadlowski, & Sigurdsson 1995, Israelian et al 1999, Kalogera et al 2001). One approach to estimating the formation of BH-PSR binaries is through stellar population synthesis models, which can either be done ab initio, or calibrated to observations. We recently carried out such a synthesis (Sipior & Sigurdsson 2002), as have a number of other authors (cf Belczynski, K., Kalogera, V., & Bulik 2002, Grishchuk et al 2001), using a range of independent stellar evolution codes and model assumptions for the kinematics of the supernova events. In constructing such a model, assumptions need to be made about:
KL-Cuts: A new approach for logic synthesis targeting multiple output blocks

This paper introduces the concept of kl-feasible cuts, by controlling both the number k of inputs and the number l of outputs in a circuit cut. To provide scalability, the concept of factor cuts is extended to kl-cuts. Algorithms for computing this kind of cuts, including kl-cuts with unbounded k, are presented and results are shown. As a practical application, a covering algorithm using these cuts is presented.
Dietary carbohydrates and insulin action in humans

The metabolic syndrome represents a vicious cycle whereby insulin resistance leads to compensatory hyperinsulinaemia, which maintains normal plasma glucose but may exacerbate insulin resistance. Excess insulin secretion may eventually reduce β-cell function due to amyloid deposition, leading to raised blood glucose and further deterioration of β-cell function and insulin sensitivity via glucose toxicity. Reducing postprandial glucose and insulin responses may be a way to interrupt this process, but there is disagreement about the dietary approach to achieve this. Glucose and insulin responses are determined primarily by the amount of carbohydrate consumed and its rate of absorption. Slowly absorbed, low glycaemic-index (GI) foods are associated with increased HDL cholesterol and reduced risk of type 2 diabetes. There is some evidence that low-GI foods improve insulin sensitivity in humans, although studies using established techniques (glucose clamp or frequently sampled intravenous glucose tolerance test) have not been done. Low carbohydrate diets have been suggested to be beneficial in the treatment of the metabolic syndrome because of reduced postprandial insulin. However, they may increase fasting glucose and impair oral glucose tolerance — effects which define carbohydrate intolerance. The effects of low carbohydrate diets on insulin sensitivity depend on what is used to replace the dietary carbohydrate, and the nature of the subjects studied. Dietary carbohydrates may affect insulin action, at least in part, via alterations in plasma free fatty acids. In normal subjects a high-carbohydrate/low-GI breakfast meal reduced free fatty acids by reducing the undershoot of plasma glucose, whereas low-carbohydrate breakfasts increased postprandial free fatty acids. It is unknown if these effects occur in insulin-resistant or diabetic subjects. Thus further work needs to be done before a firm conclusion can be drawn as to the optimal amount and type of dietary carbohydrate for the treatment of the metabolic syndrome.
Lamellar Keratoplasty: A Literature Review

The concept of lamellar keratoplasty (LK) is not new. However, newer forms of lamellar keratoplasty techniques have emerged in the last decade or so revolving around the concept of targeted replacement of diseased corneal layers. These include anterior lamellar keratoplasty (ALK) techniques that aim to selectively replace diseased corneal stroma and endothelial keratoplasty techniques aiming to replaced damaged endothelium in endothelial disorders. Recent improvements in surgical instruments and introduction of new techniques as well as inherent advantages such as preservation of globe integrity and decreased graft rejection have resulted in the reintroduction of LK as an acceptable alternative to conventional PK. In this review, indications, benefits, limitations, and outcomes of various anterior and posterior lamellar keratoplasty techniques are discussed.
Addressing Virtual Work Challenges: Learning From the Field

ABSTRACT Companies increasingly rely on virtual teams. Despite numerous studies examining the challenges of geographically dispersed work, the findings are often mixed. The purpose of this article is to identify themes of challenges associated with virtual collaborations based on academic literature and do a gap analysis with industry trends. First, we identify five overarching categories of virtual team challenges based on reviewing the latest trends in the academic literature: trust and relationships, communication and knowledge sharing, perceptions and decision making, leadership, and diversity. Second, we utilize these categories to qualitatively code and analyze the company data from the Fortune Best Places to Work surveys from 2014 to 2017, using the document analysis technique. Our contribution is to identify similarities and differences in scholarly and industry approaches to addressing virtual teamwork challenges and thus highlight opportunities for development and future research.
Modelling and recognising spatio-temporal hand gestures with an uncalibrated camera

Tracking highly articulated 3-D objects such as human hands with an uncalibrated camera is not an easy task. In addition to changes in position, such objects change their shape and appearance with time. We present a robust tracker for such cases which works well in spite of cases of other similar moving objects, and background clutter. Our hand gesture analysis system is based on such a tracker. The system can recognise gestures involving the same hand shapes following different trajectories, and vice versa.
Using a minimal action grammar for activity understanding in the real world

There is good reason to believe that humans use some kind of recursive grammatical structure when we recognize and perform complex manipulation activities. We have built a system to automatically build a tree structure from observations of an actor performing such activities. The activity trees that result form a framework for search and understanding, tying action to language. We explore and evaluate the system by performing experiments over a novel complex activity dataset taken using synchronized Kinect and SR4000 Time of Flight cameras. Processing of the combined 3D and 2D image data provides the necessary terminals and events to build the tree from the bottom-up. Experimental results highlight the contribution of the action grammar in: 1) providing a robust structure for complex activity recognition over real data and 2) disambiguating interleaved activities from within the same sequence.
PATRICIA SOTEROPOULOS , PETER reveals spreading inflammatory signals and neuron loss Gene expression profiling of acute spinal cord injury

You might find this additional info useful. 24 times a year (twice monthly) by the American Physiological Society, 9650 Rockville Pike, Bethesda MD systems with techniques linking genes and pathways to physiology, from prokaryotes to eukaryotes. It is published publishes results of a wide variety of studies from human and from informative model Physiological Genomics
A 6-bit arbitrary digital noise emulator in 65nm CMOS technology

An arbitrary noise generator (ANG) is based on time-series charging of divided parasitic capacitance (TSDPC) and emulates power supply noise generation in a CMOS digital circuit. A prototype ANG incorporates an array of 32 × 32 6-bit TSDPC cells along with a 128-word vector memory and occupies 2 × 2 mm2 in a 65 nm 1.2 V CMOS technology. Digital noise emulation of functional logic cores such as register arrays and processing elements is demonstrated with chip-level waveform monitoring at power supply, ground, as well as substrate nodes.
Similarity Measure in the Object Model

The present paper deals with the deenition of a similarity measure for object-based systems. This measure is based on the semantic relations between words which identify the concepts of the object model. Wordnet, a widely available computer thesaurus, has been used to \guess" semantic relations between terms. The similarity measure is applied to class libraries by combining it with other metrics based on the structure of the objects. The similarity measure and its derived metrics are used to improve the reusability process in object-based libraries.
VP-Rec: A Hybrid Image Recommender Using Visual Perception Network

A requirement for a great user experience is to meet the exact needs for the usage of a recommender system. Such systems need user's historical preferences to reasonably perform, which might not be the case for a cold-start user. This paper presents VP-Rec, a hybrid image recommender system that addresses the new user cold-start problem. VP-Rec combines user visual perception and pairwise preferences as source of information to perform recommendations. First, we infer pairwise preferences from users ratings. Next, we build visual perception networks linking users according to their visual attention similarities. From these two inferred structures, we build consensual prediction models, so that when a new user enters the system, we capture his visual attention and choose the best model that fits him. The system has been tested on two image datasets, getting important improvements in terms of ranking quality (nDCG) when applied to new user cold-start scenario against state-of-art recommender systems.