Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender classification in uncontrolled settings using additive logistic models

Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively unconstrained. We localize faces using a standard sliding-window detector. We preprocess the facial region by convolving with Gabor filters at at four scales and four orientations. We sample these responses and concatenate them to form a feature vector. We develop a classifier based on an additive sum of non-linear functions of one-dimensional projections of the data. In particular we investigate arc tangent and weighted sums of Gaussians. We describe a training method based on increasing the binomial log likelihood. We demonstrate that our system on two databases and show that it performs well relative to the state of the art.
Decision support based on the risk assessment of information systems and Bayesian learning

Risk protection has long been one of the main tasks of companies in a wide scope of business. From extensive range of risks the cyber-risks highlight as one of the most important. Cyber- risks are generated from hackers, malicious software, disgruntled employees, competitors, and many other sources both internal and external. Internal and external attacks on corporate assets and rapidly growing technology forced corporate management to conduct more appropriate awareness of the information security risks to information assets. The information security risk assessment, when performed correctly, can give corporate managers the information they need in order to understand and control the risks to their assets. The risks are in much more detail analysed in economic sectors, but in recent years there is increasing of risk assessment practice in the world of information technology. The model presented in this paper integrates the management and analysis of information risks and decision- making theory and thus creates a framework for the integrated management information system based on the technological risk assessment and Bayesian learning. The paper shows simulation and two case study scenarios in which is presented a potentially wide range of usage.
A Study on the State of Practice in Security Situational Awareness

We present the results of an interview study on the state of practice for Situational Awareness (SA) in the cybersecurity industry. Representatives from four global companies providing cybersecurity monitoring and analysis services and products were interviewed to get a view into the current state of practice in SA. The interviews were performed as a form of thematic interview, resulting in the classification of the results in three main areas of SA, i.e., how security is modelled, what information is collected, and how the data is analyzed. We describe the topics covered by the interviews, the common issues and methods, their differences, and provide a summary view on the current state of security monitoring and analysis in the cybersecurity industry. We also describe potential future work in terms of identified challenges in the area. The results help understand various aspects of cybersecurity situational awareness, to identify gaps between research and practice, and to build holistic SA solutions.
Assessing the efficacy of a novel temperature and humidity control machine to minimize house dust mite allergen exposure and clinical symptoms in allergic rhinitis children sensitized to dust mites: a pilot study.

BACKGROUND House dust mite avoidance is advised in dust mite sensitized patients to decrease the risk to develop allergic symptoms. Maintaining a relative humidity (RH) of less than 50% in households is recommended to prevent dust mite proliferation. OBJECTIVE To investigate the efficacy of a novel temperature and humidity machine to control the level of dust mite allergens and total nasal symptom score (TNSS) in dust mite sensitized allergic rhinitis children. METHOD Children (8-15 years) with dust mite sensitized persistent allergic rhinitis (AR) were enrolled. The temperature and humidity control machine was installed in the bedroom where the enrolled children stayed for 6 months. TNSS was assessed before and every month after machine set up and the level of dust mite allergen (Der p 1 and Der f 1) from the mattress were measured before and every 2 months after machine set up using enzyme-linked immunosorbent assay (ELISA). RESULTS A total of 7 children were enrolled. Noticeable reduction of Der f 1 was observed as early as 2 months after installing the machine, but proper significant differences appeared 4 months after and remained low until the end of the experiment (p <0.05). Although no correlation was observed between TNSS and the level of dust mite allergens, there was a significant reduction in TNSS at 2 and 4 months (p <0.05) and 70% of the patients were able to stop using their intranasal corticosteroids by the end of the experiment. CONCLUSIONS The level of house dust mite in mattresses was significantly reduced after using the temperature and humidity control machine. This machine may be used as an effective tool to control clinical symptoms of dust mite sensitized AR children.
Design and Analysis of an Integrated Checkpointing Recovery Scheme for Distributed Applications

An integrated checkpointing and recovery scheme which exploits the low latency and high coverage characteristics of a concurrent error detection scheme is presented. Message dependency, which is the main source of multistep rollback in distributed systems, is minimized by using a new message validation technique derived from the notion of concurrent error detection. The concept of a new global state matrix is introduced to track error checking and message dependency in a distributed system and assist in the recovery. The analytical model, algorithms and data structures to support an easy implementation of the new scheme are presented. The completeness and correctness of the algorithms are proved. A number of scenarios and illustrations that give the details of the analytical model are presented. The benefits of the integrated checkpointing scheme are quantified by means of simulation using an object-oriented test framework.
Sparse Optimal Control of the Schlögl and FitzHugh–Nagumo Systems

Abstract. We investigate the problem of sparse optimal controls for the so-called Schlögl model and the FitzHugh–Nagumo system. In these reaction–diffusion equations, traveling wave fronts occur that can be controlled in different ways. The L1-norm of the distributed control is included in the objective functional so that optimal controls exhibit effects of sparsity. We prove the differentiability of the control-to-state mapping for both dynamical systems, show the well-posedness of the optimal control problems and derive first-order necessary optimality conditions. Based on them, the sparsity of optimal controls is shown. The theory is illustrated by various numerical examples, where wave fronts or spiral waves are controlled in a desired way.
Superovulation and embryo transfer in dairy cattle : effect of management factors with emphasis on sex-sorted semen

6 ORIGINAL ARTICLES . 8 ABBREVIATIONS . 9 INTRODUCTION . 10 REVIEW OF LITERATURE. 12 1. Superovulation and embryo recovery in cattle . 12 2. Major animal-related factors affecting the outcome of superovulation . 14 2.1. Ovarian follicular waves . 14 2.2. Count of antral follicles . 15 2.3. Age. 16 3. Environmental factors affecting the outcome of superovulation. 16 4. Management factors affecting the outcome of superovulation . 17 4.1. Superovulatory treatment . 18 4.1. 1. Gonadotropin preparations . 18 4.1. 2. Administration of gonadotropins . 19 4.2. Sex-sorted semen . 20 4.2.1. Technique of sperm sorting. 20 4.2.2. AI of single-ovulating females . 21 4.2.3. In vivo embryo production . 22 4.2.4. Calves produced with sex-sorted semen . 25 4.3. Nutrition . 25 4.3. 1. Energy balance . 26 4.3. 2. Dietary protein . 28 AIMS . 31 MATERIALS AND METHODS . 32 1. Donors, recipients and embryos . 32 2. Experimental design . 33 2.1. Nutritional protein (I) . 33 2.2. Superovulation using two commercial FSH-preparations (II) . 34 2.3. Effect of sex-sorted semen on embryo production (III, IV) . 35 2.4. Effect of sex-sorted semen on calf production (V) . 36 3. Statistical analyses . 36 RESULTS. 38
Big Data and cloud computing: innovation opportunities and challenges

ABSTRACT Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers – Big Data and cloud computing – and reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.
Examining Key Stakeholder and Community Residents’ Understanding of Environmental Influences to Inform Place‐Based Interventions to Reduce Obesity in Rural Communities, Kentucky 2015

PURPOSE Rural residents report high rates of obesity, physical inactivity, and poor eating habits. The objectives of this study were to (1) use the collective impact model to guide efforts to elicit community members' perceptions of county-specific factors influencing high obesity rates; (2) determine the association between utilization of food retail venues and concern about obesity and healthy eating; and (3) determine community members' utilization of physical activity infrastructure and concern about physical inactivity. METHODS The study was conducted in 6 rural counties in Kentucky with adult obesity prevalence rates >40%. Community stakeholders met to assess counties' needs and assets in implementing interventions to reduce obesity in their communities. A random-digit dial survey (n = 756) also was conducted to examine awareness and availability of community resources for healthy eating and physical activity. FINDINGS Stakeholders identified lack of access to fruits and vegetables and poor physical activity infrastructure as contributors to obesity. Reporting moderate and serious concern about obesity and healthy eating was associated with higher odds of shopping at a supercenter compared with those expressing little concern. Reported access to information about physical activity opportunities was associated with higher odds of reporting the availability of safe places for physical activity, sidewalks, and trails compared with those who reported that information was difficult to obtain. CONCLUSIONS This study elicits community-identified barriers to healthy behaviors and provides foundational data to inform future place-based obesity reduction interventions.
Pneumonia case-finding in the RESPIRE Guatemala indoor air pollution trial: standardizing methods for resource-poor settings.

OBJECTIVE Trials of environmental risk factors and acute lower respiratory infections (ALRI) face a double challenge: implementing sufficiently sensitive and specific outcome assessments, and blinding. We evaluate methods used in the first randomized exposure study of pollution indoors and respiratory effects (RESPIRE): a controlled trial testing the impact of reduced indoor air pollution on ALRI, conducted among children <or= 18 months in rural Guatemala. METHODS Case-finding used weekly home visits by fieldworkers trained in integrated management of childhood illness methods to detect ALRI signs such as fast breathing. Blindness was maintained by referring cases to study physicians working from community centres. Investigations included oxygen saturation (SaO2), respiratory syncytial virus (RSV) antigen test and chest X-ray (CXR). FINDINGS Fieldworkers referred > 90% of children meeting ALRI criteria, of whom about 70% attended a physician. Referrals for cough without respiratory signs and self-referrals contributed 19.0% and 17.9% of physician-diagnosed ALRI cases respectively. Intervention group attendance following ALRI referral was 7% higher than controls, a trend also seen in compliance with RSV tests and CXR. There was no evidence of bias by intervention status in fieldworker classification or physician diagnosis. Incidence of fieldworker ALRI (1.12 episodes/child/year) is consistent with high sensitivity and low specificity; incidence of physician-diagnosed ALRI (0.44 episodes/child/year) is consistent with comparable studies. CONCLUSION The combination of case-finding methods achieved good sensitivity and specificity, but intervention cases had greater likelihood of reaching the physician and being investigated. There was no evidence of bias in fieldworkers classifications despite lack of concealment at home visits. Pulse oximetry offers practical, objective severity assessment for field studies of ALRI.
Leukotriene Receptors: Crucial Components in Vascular Inflammation

The accumulation of immune cells during vascular inflammation leads to formation of leukotrienes (LTs). While macrophages represent a major source of LT biosynthesis in the proximity of the vascular wall, activated T lymphocytes may, in addition, play a key regulatory role on macrophage expression of LT-forming enzymes. Within the vascular wall, LTs activate cell surface receptors of the BLT and CysLT subtypes expressed on vascular smooth muscle and endothelial cells. The LT receptor expression on those cells is highly dependent on transcriptional regulation by pro- and anti-inflammatory mediators. LT receptor activation on vascular smooth muscle cells is associated with both directly and indirectly induced vasoconstriction, as well as intimal hyperplasia through stimulation of migration and proliferation. On the other hand, endothelial LT receptors induce vasorelaxation and leukocyte recruitment and adhesion. Results from in vitro and in vivo studies of LT receptor antagonists indicate potential beneficial effects in atherosclerosis and other inflammatory cardiovascular diseases.
Filtration of submicrometer particles by pelagic tunicates

Salps are common in oceanic waters and have higher per-individual filtration rates than any other zooplankton filter feeder. Although salps are centimeters in length, feeding via particle capture occurs on a fine, mucous mesh (fiber diameter d ∼0.1 μm) at low velocity (U = 1.6 ± 0.6 cm·s−1, mean ± SD) and is thus a low Reynolds-number (Re ∼10−3) process. In contrast to the current view that particle encounter is dictated by simple sieving of particles larger than the mesh spacing, a low-Re mathematical model of encounter rates by the salp feeding apparatus for realistic oceanic particle-size distributions shows that submicron particles, due to their higher abundances, are encountered at higher rates (particles per time) than larger particles. Data from feeding experiments with 0.5-, 1-, and 3-μm diameter polystyrene spheres corroborate these findings. Although particles larger than 1 μm (e.g., flagellates, small diatoms) represent a larger carbon pool, smaller particles in the 0.1- to 1-μm range (e.g., bacteria, Prochlorococcus) may be more quickly digestible because they present more surface area, and we find that particles smaller than the mesh size (1.4 μm) can fully satisfy salp energetic needs. Furthermore, by packaging submicrometer particles into rapidly sinking fecal pellets, pelagic tunicates can substantially change particle-size spectra and increase downward fluxes in the ocean.
Measuring regularity of network patterns by grid approximations using the LLL algorithm

In a recent work, we have proposed a novel way to approximate point sets with grids using the LLL algorithm, which operates in polynomial time. Now, we show how this approach can be applied to pattern recognition purposes with interpreting the rate of approximation as a new feature for regularity measurement. Our practical problem is the characterization of pigment networks in skin lesions. For this task we also introduce a novel image processing method for the extraction of the pigment network. Then, we show how our grid approximation framework can be applied with specializing it for the recognition of hexagonal patterns. The classification performance of our approach for the pigment network characterization problem is measured on a database annotated by a clinical expert. Throughout the paper we address several practical issues that may help to apply our general framework to other practical tasks, as well.
Sampling Theorems for Signals From the Union of Finite-Dimensional Linear Subspaces

Compressed sensing is an emerging signal acquisition technique that enables signals to be sampled well below the Nyquist rate, given that the signal has a sparse representation in an orthonormal basis. In fact, sparsity in an orthonormal basis is only one possible signal model that allows for sampling strategies below the Nyquist rate. In this paper, we consider a more general signal model and assume signals that live on or close to the union of linear subspaces of low dimension. We present sampling theorems for this model that are in the same spirit as the Nyquist-Shannon sampling theorem in that they connect the number of required samples to certain model parameters. Contrary to the Nyquist-Shannon sampling theorem, which gives a necessary and sufficient condition for the number of required samples as well as a simple linear algorithm for signal reconstruction, the model studied here is more complex. We therefore concentrate on two aspects of the signal model, the existence of one to one maps to lower dimensional observation spaces and the smoothness of the inverse map. We show that almost all linear maps are one to one when the observation space is at least of the same dimension as the largest dimension of the convex hull of the union of any two subspaces in the model. However, we also show that in order for the inverse map to have certain smoothness properties such as a given finite Lipschitz constant, the required observation dimension necessarily depends logarithmically on the number of subspaces in the signal model. In other words, while unique linear sampling schemes require a small number of samples depending only on the dimension of the subspaces involved, in order to have stable sampling methods, the number of samples depends necessarily logarithmically on the number of subspaces in the model. These results are then applied to two examples, the standard compressed sensing signal model in which the signal has a sparse representation in an orthonormal basis and to a sparse signal model with additional tree structure.
Portfolio Allocation for Bayesian Optimization

Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.
A Reproducible Benchmark for P2P Retrieval

With the growing popularity of information retrieval (IR) in distributed systems and in particular P2P Web search, a huge number of protocols and prototypes have been introduced in the literature. However, nearly every paper considers a dierent benchmark for its experimental evaluation, rendering their mutual comparison and the quantification of performance improvements an impossible task. We present a standardized, general purpose benchmark for P2P IR systems that finally makes this possible. We start by presenting a detailed requirement analysis for such a standardized benchmark framework that allows for reproducible and comparable experimental setups without sacrificing flexibility to suit dierent system models. We further suggest Wikipedia as a publicly-available and all-purpose document corpus and finally introduce a simple but yet flexible clustering strategy that assigns the Wikipedia articles as documents to an arbitrary number of peers. After proposing a standardized, real-world query set as the benchmark workload, we review the metrics to evaluate the benchmark results and present an example benchmark run for our fullyimplemented P2P Web search prototype MINERVA.
Judging Juveniles: Prosecuting Adolescents in Adult and Juvenile Courts

Acknowledgments1 Introduction: Growing Up Quickly 2 Law and Context 3 The Process of Prosecuting Adolescents: How Formal? 4 Judging Adolescents: What Matters? 5 Punishment for Adolescents: What Do They Get, and Why? 6 Children in an Adult World 7 Putting the Genie Back in the Bottle: Lessons for Policy Appendix: Research Methods Notes Index About the Author
Exposure Therapy for Substance Abusers with PTSD

Epidemiological research indicates that there is substantial comorbidity between posttraumatic stress disorder (PTSD) and substance use disorder (SUD). Moreover, there is growing evidence that having a comorbid PTSD diagnosis is associated with greater substance use problem severity and poorer outcomes from SUD treatment. In an attempt to improve the treatment outcome for individuals with PTSD-SUD, recently developed treatments combine exposure therapy for PTSD with an empirically supported treatment for SUD. This article describes one of the treatments and discusses treatment modifications that have been incorporated when translating this research-based therapy to practice in an inner-city community mental health center.