Miracle-dz/UniFAR-FTUs huggingface dataset:
Degree-constrained multihop scatternet formation for Bluetooth networks

We describe BlueMesh, a new protocol for the establishment of scatternets, i.e., multihop ad hoc networks of Bluetooth devices. BlueMesh defines rules for device discovery, piconet formation and piconet interconnection in order to achieve the following desirable properties: a) BlueMesh generates connected scatternets without requiring the Bluetooth devices all to be in each other's transmission range; b) the BlueMesh scatternet topology is a mesh with multiple paths between any pair of nodes; c) BlueMesh piconets are made up of no more than 7 slaves. Simulation results in networks with 200 nodes show that BlueMesh is effective in quickly generating a connected scatternet in which each node, on average, does not assume more than 2.3 roles. Moreover, the length of routes between any two nodes in the network, is comparable to that of the shortest paths between the nodes.
A rapid iris location algorithm based on embedded

A fast iris location method supported embedded system is presented in this paper for eliminating the effect of noise such as the thick eyelashes, eyelid in the iris location method and improve the performance of the algorithm. The collected iris image is enhanced and denoised. Then we can achieve the location of iris inside edge according to projection method and the calculus algorithm based on round match detection. The location of outer edge includes three steps. The first step is to remove the eyelashes by dual-threshold method. Secondly the facula is removed through corrosion method. Finally the accurate location can be obtained based on the geometry of circular and least-squares method. The experiments results show that the proposed method can overcome all sorts of interference, obtain the accurate location fast, reduce the blindness of the traditional location algorithm and is a good support for embed system.
Data center traffic engineering using Markov approximation

This paper proposes a data center traffic engineering scheme with Markov approximation. Markov approximation is a distributed optimization framework. It optimizes networks by independent behaviors of users that construct a time-reversible continuous-time Markov chain based on minimum necessary information in the networks. The proposed scheme aims at minimizing the maximum link utilization in data center networks by means of Markov approximation. The proposed scheme provides two strategies with different search space. Through simulation experiments, we show that the proposed scheme efficiently reduces the maximum link utilization under not only static situations but also dynamic situations where traffic demands dynamically change in data center networks.
Reflections on Shannon Information: In search of a natural information-entropy for images

It is not obvious how to extend Shannon's original information entropy to higher dimensions, and many different approaches have been tried. We replace the English text symbol sequence originally used to illustrate the theory by a discrete, bandlimited signal. Using Shannon's later theory of sampling we derive a new and symmetric version of the second order entropy in 1D. The new theory then naturally extends to 2D and higher dimensions, where by naturally we mean simple, symmetric, isotropic and parsimonious. Simplicity arises from the direct application of Shannon's joint entropy equalities and inequalities to the gradient (del) vector field image embodying the second order relations of the scalar image. Parsimony is guaranteed by halving of the vector data rate using Papoulis' generalized sampling expansion. The new 2D entropy measure, which we dub delentropy, is underpinned by a computable probability density function we call deldensity. The deldensity captures the underlying spatial image structure and pixel co-occurrence. It achieves this because each scalar image pixel value is nonlocally related to the entire gradient vector field. Both deldensity and delentropy are highly tractable and yield many interesting connections and useful inequalities. The new measure explicitly defines a realizable encoding algorithm and a corresponding reconstruction. Initial tests show that delentropy compares favourably with the conventional intensity-based histogram entropy and the compressed data rates of lossless image encoders (GIF, PNG, WEBP, JP2K-LS and JPG-LS) for a selection of images. The symmetric approach may have applications to higher dimensions and problems concerning image complexity measures.
Distributed Large-Scale Learning

An interesting problem in the fields of search engine and ad placement engines, is learning with Big data. Big data is usually too large to fit into the memory of a single machine, in which case mini-batches have to be distributed across several machines. Existing distributed learning methods come with expensive synchronization. This synchronization could be a bad idea in a heterogenous setting where the machines might be of different capacities in which case the slower machine slows down the faster ones and the cost of each iteration equals the computation cost plus the synchronization plus the communication cost. This paper introduces an asynchronous technique for large-scale learning in which the speed of the minimization at the slower machine is at the rate of the faster machine. We experimented this technique with two machines, for which one is fast and the other slower. Experimental result shows that this technique converges.
Robot Motion Planning with Dynamics as Hybrid Search

This paper presents a framework for motion planning with dynamics as hybrid search over the continuous space of feasible motions and the discrete space of a low-dimensional workspace decomposition. Each step of the hybrid search consists of expanding a frontier of regions in the discrete space using cost heuristics as guide followed by sampling-based motion planning to expand a tree of feasible motions in the continuous space to reach the frontier. The approach is geared towards robots with many degrees-of-freedom (DOFs), nonlinear dynamics, and nonholonomic constraints, which make it difficult to follow discrete-search paths to the goal, and hence require a tight coupling of motion planning and discrete search. Comparisons to related work show significant computational speedups.
Development of a general criticality criterion for the risk estimation of driving situations and its application to a maneuver-based lane change assistance system

The results of a fundamental study on developing a general criticality criterion for driving situations are presented. The meaning of criticality in driving situations is defined and the influencing parameters are analyzed. Furthermore, a maneuver-based lane change assistance system is presented and evaluated in subject tests. The outcomes of the general top-down approach are compared to an evaluation criterion which is the available reaction time for the maneuver-based lane change assistance system, developed in a bottom-up approach.
Alternative measures of word relatedness in distributional semantics

This paper presents an alternative method to measuring word-word semantic relatedness in distributional semantics framework. The main idea is to represent target words as rankings of all co-occurring words in a text corpus, ordered by their tf – idf weight and use a metric between rankings (such as Jaro distance or Rank distance) to compute semantic relatedness. This method has several advantages over the standard approach that uses cosine measure in a vector space, mainly in that it is computationally less expensive (i.e. does not require working in a high dimensional space, employing only rankings and a distance which is linear in the rank’s length) and presumably more robust. We tested this method on the standard WS353 Test, obtaining the co-occurrence frequency from the Wacky corpus. The results are comparable to the methods which use vector space models; and, most importantly, the method can be extended to the very challenging task of measuring phrase semantic relatedness.
Enabling Visual Exploration of Long - t erm Physiological Data

With the recent development of wearable and comfortable biosensors, larger datasets of physiological data are being collected in challenging real-life scenarios. In order to gain insight from these datasets, behavioral scientists need tools that enable agile and efficient data exploration. In this work, we designed and implemented two visualization tools for large-scale time-based datasets, and combined the lessons learned to create a more general-purpose tool.
Moving object detection based on T-test combined with kirsch operator

Moving object detection is a hot topic in computer vision. It is widely used in many areas, such as video monitoring, and intelligent traffic. This paper proposes a new method, which combines the T-test with the kirsch operator. First, the T-test method can detect the moving area between two frames of the video sequences while the kirsch operator can detect the contour of the objects. Second, the moving information fuses the contour information, so the experimental results can obtain both the moving information and the contour information. The morphologic operation is used to get the accurate information of the moving object. Finally, the moving objects are labeled in the video sequences. Experimental results prove that the proposed method is effective and robust.
Skeletal muscle fibre-type shifting and metabolic profile in patients with chronic obstructive pulmonary disease

The aim of this study was to examine the nature of fibre-type redistribution in relation to fibre metabolic profile in the vastus lateralis in chronic obstructive pulmonary disease (COPD) and COPD subtypes. Fifteen COPD patients (eight with emphysema stratified by high-resolution computed tomography) and 15 healthy control subjects were studied. A combination of myofibrillar adenosine triphosphatase staining and immunohistochemistry was used to identify pure, as well as hybrid fibre types. For oxidative capacity, fibres were stained for cytochrome c oxidase and succinate dehydrogenase activities, and glycogen phosphorylase for glycolytic capacity. The proportion of type‐I fibres in COPD patients was markedly lower (16% versus 42%), especially in emphysema, and the proportion of hybrid fibres was higher (29% versus 16%) compared to controls. The proportion of fibres staining positive for oxidative enzymes was lower in COPD patients, which correlated with the proportion of type‐I fibres. In COPD oxidative capacity was lower within IIA fibres. The authors conclude that fibre-type transitions are involved in the fibre-type redistribution in chronic obstructive pulmonary disease. Low oxidative capacity is closely related to the proportion of type‐I fibres, but an additional reduction of oxidative enzyme activity is present within IIA fibres. Fibre-type abnormalities may be aggravated in emphysema.
Face Matching for Post-Disaster Family Reunification

The National Library of Medicine (NLM) has developed People LocatorTM (PL), a Web-based system for family reunification in cases of a natural or man-made disaster. PL accepts photos and brief text meta-data (name, age, etc.) of missing or found persons. Searchers may query PL with text information, but text data is often incomplete or inconsistent. Adding an image-based search capability, i.e., matching faces in query photos to those already stored in the system, would significantly benefit the user experience. We report on our face matching R&D that aims to provide robust face localization and matching on digital photos of variable quality. In this article, we review relevant research and present our approach to robust near-duplicate image detection as well as face matching. We describe the integration of our face matching system with PL, report on its performance, and compare it to other publicly available face recognition systems. In contrast to these systems that have many good quality well-illuminated sample images for each person, our algorithms are hampered by the lack of training examples for individual faces, as those are unlikely in a disaster setting.
Proposal : Using Collages as an Interface to Large Photograph Collections

A collage is a composite of a several different media elements combined to form an artistic creation. The media elements used in a collage vary by artistic preference and the message the collage is meant to convey. A very popular media to use is images or photographs. For the purposes of this proposal, I will refer to a collage, and photo collage interchangeably as I am not considering collages designed with other media elements.
Relationship between Opium Abuse and Severity of Depression in Type 2 Diabetic Patients

Background Opium use in diabetic populations is associated with major depressive disorder (MDD). This study was designed to investigate the relationship between opium use and severity of depression in Iranian diabetic patients. Methods In this case-control study, 642 type 2 diabetic patients were recruited from those presenting at two outpatient clinics at the Akhavan Hospital in Kashan, Iran; of them, 600 diabetic patients were included in the study and divided into two groups: opium-abusers (150 patients) and non-opium-abusers (450 patients). Clinical and demographic information was obtained through a detailed questionnaire. Depression symptomalogy and severity were assessed with the Beck Depression Inventory (BDI), and a corresponding diagnosis was made based on the Diagnostic and Statistical Manual of Mental Disorders-IV, Text Revision, 2000 (DSM-IV TR) criteria. Results The mean depression score was higher in the opium abuse group than in the non-abuser group (29.27±1.44 vs. 18.29±1.31, P<0.001). In general, a significant association was found between opium abuse and depression among patients (odds ratio [OR], 4.54; 95% confidence interval [CI], 2.87 to 7.44; P=0.001). No significant relationship was found between dysthymia and opium abuse (OR, 0.68; 95% CI, 0.18 to 1.192; P=0.155), while MDD was significantly higher in the opium abuser group (OR, 7.32; 95% CI, 5.20 to 12.01; P<0.001). Conclusion Depression is more frequent in opium-dependent diabetic patients, and its severity is also greater. Given these findings, opium-dependent diabetic patients should be advised about the increased risks of depression and related comorbidities.
Content Popularity Evolution in Online Social Networks

Understanding content popularity growth on Online Social Networks (OSNs) is of great importance to Internet service providers, content creators and online marketers. However, most previous studies of OSNs are based on static views of the system, thus neglecting the temporal evolution of the network, and a possible correlation with content popularity growth. Moreover, previous analyses also greatly neglect the impact of the referrers (i.e., incoming links from external sites) on content popularity. We here provide some initial results on the analysis of content popularity growth in YouTube videos. Our study is based on three video datasets, namely popular videos, randomly collected videos, and copyright protected videos, with distinct characteristics in terms of temporal popularity evolution. We also characterize the different referrers that most often lead users to YouTube videos. Our results shed some light into aspects that impact content popularity growth.
High-functioning depression among women in South Africa: An exploratory study

The study estimated the prevalence and trends of chronic depressive symptoms among women in South Africa from the National Income Dynamics Study (NIDS) data collection waves (2008, 2010, 2012, 2014/2015). NIDS utilised the Center for Epidemiological Studies Depression (CES-D) scale to assess depressive symptoms among adults. From the trend analysis, we observed a decrease in self-reported significant depressive symptoms over the eight-year period in black women (2.66%), followed by mixed-race (coloured) women (1.15%). Across race, significant depressive symptoms were associated with socioeconomic status variables such as income, education, and occupational status. Within race, prevalence rates were higher among individuals with low socioeconomic status (lower income, less educated, and less skilled occupations). Black women were at a higher risk of exhibiting high-functioning depression due to their overrepresentation among low socioeconomic status individuals. Symptom prevalence may be explained largely by psychosocial stressors in patriarchal legacy communities.
Simulation-based retrospective optimization of stochastic systems: a family of algorithms

We consider optimizing a stochastic system, given only a simulation model that is parameterized by continuous decision variables. The model is assumed to produce unbiased point estimates of the system performance measure(s), which must be expected values. The performance measures may appear in the objective function and/or in the constraints. We develop a family of retrospective-optimization (RO) algorithms based on a sequence of sample-path approximations to the original problem with increasing sample sizes. Each approximation problem is obtained by substituting point estimators for each performance measure and using common random numbers over all values of the decision variables. We assume that these approximation problems can be deterministically solved within a specified error in the decision variables, and that this error is decreasing to zero. The computational efficiency of RO arises from being able to solve the next approximation problem efficiently based on knowledge gained from the earlier, easier approximation problems.
Dynamic field theory of sequential action: A model and its implementation on an embodied agent

How sequences of actions are learned, remembered, and generated is a core problem of cognition. Despite considerable theoretical work on serial order, it typically remains unexamined how physical agents may direct sequential actions at the environment within which they are embedded. Situated physical agents face a key problem - the need to accommodate variable amounts of time it takes to terminate each individual action within the sequence. Here we examine how Dynamic Field Theory (DFT), a neuronally grounded dynamical systems approach to embodied cognition, may address sequence learning and sequence generation. To demonstrate that the proposed DFT solution works with real and potentially noisy sensory systems as well as with real physical action systems, we implement the approach on a simple autonomous robot. We demonstrate how the robot acquires sequences from experiencing the associated sensory information and how the robot generates sequences based on visual information from its environment using low-level visual features.
Haemodynamic adaptation two months after transjugular intrahepatic portosystemic shunt (TIPS) in cirrhotic patients.

BACKGROUND AND AIMS: In portal hypertensive patients, transjugular intrahepatic portosystemic shunt (TIPS) acutely increases cardiac output and exaggerates peripheral vasodilatation. It has been suggested that the worsened hyperdynamic state may progress to high output heart failure. The aim was to evaluate the acute and short-term haemodynamic adaptation to this procedure. METHODS: Systemic, splanchnic, and pulmonary haemodynamics were studied in 15 cirrhotic patients under stable haemodynamic conditions before placement of TIPS, then 15-30 minutes after and two months later. For inclusion in the final analysis, an uneventful post-TIPS at two months follow up and a stable portacaval gradient were required. The following variables were measured or calculated: portacaval gradient; cardiac index (thermodilution); systolic and diastolic mean arterial, atrial, pulmonary arterial, and wedged pulmonary capillary pressures; heart rate; and total peripheral and pulmonary vascular resistances. Blood flow in the shunt was measured using duplex Doppler ultrasound. RESULTS: The portacaval gradient decreased by 56% and remained stable thereafter. Shunt blood flow was unchanged when measured immediately after TIPS and two months later. Immediately after TIPS there was a pronounced increase in cardiac index (+32%; p < 0.05) in association with a decrease in peripheral and pulmonary vascular resistance (-21%; p < 0.05 and -14%; NS). Two months later, whereas the initial rise in cardiac index was attenuated, peripheral vascular resistances remained similar and pulmonary vascular resistances decreased further (-33%; p < 0.05) compared with immediate post-TIPS values. CONCLUSIONS: Hyperdynamic circulation worsened immediately after TIPS, with a progressive adaptation during follow up. The mechanisms of post-TIPS induced haemodynamic changes include an abrupt volume load resulting from splanchnic decompression and an increased delivery of gut derived vasodilators to the systemic circulation. The persistence of decreased peripheral and pulmonary vascular resistances despite the reduction in high cardiac output two months after TIPS suggests that vasodilatation is not solely a compensatory response to a TIPS induced increased preload. Vasodilatory substances shunted away from the liver probably play an important part in this phenomenon.
Programming techniques for real time software implementation of optimal edge detectors :-a comparison between state of the Art DSPs and RISC architectures -

This paper presents the real time implementations of the Canny-Deriche optimal edge detectors on RISC and DSP processors. For each type of architecture, the most leading optimization techniques are described. A comparison is then made between DSP and RISC processing speeds. INTRODUCTION Canny-Deriche operators have asserted themselves to the edge detection field which stands as a fundamental component of image processing and computer vision. The main drawback is the prohibitive computational power they require. It has led people to design dedicated hardware implementations (FPGA, ASIC) to achieve the real time execution of these detectors. On the other hand, the still increasing performance of DSP and RISC calls into question the need for a dedicated architecture. This paper shows that crafty software implementation of Canny-Deriche edge detectors may achieve real time execution on state of the art DSP and RISC processors. To achieve this goal, we introduce data parallelism techniques as well as flow optimization methods. 1 OPTIMAL EDGE DETECTORS In this section we introduce optimal edge detectors and the derived filters we will use in the subsequent sections. 1.1 Canny’s optimal filters Canny’s approach [1] consists in finding the optimal FIR filter which satisfies the three following constraints for a Heaviside input signal: good detection, good localization, low maximum multiplicity due to the noise. Deriche [3], using Canny’s approach, has looked for an IIR filter which satisfies the same constraints. He got the same differential equation, but while changing the conditions at the limits, he obtained, for the Canny’s performance index, an improvement of 25%. Deriche’s operators are used for two important methods of edge detection. The first is based on the gradient maxima, the second, on the laplacian's zero crossings. The state of the art methods combine both of them. In this paper we will focus on the gradient method. The implemented operators are those proposed by Deriche in [3]. 1.2 Deriche’s gradient The horizontal derivative is the result of a smoothing in the vertical direction followed by a derivation in the horizontal direction. Respectively, the vertical derivative is based on those transposed directions. The smoothing operator is the sum of a causal and an anti-causal filter: [ ] ) 2 ( ) 1 ( 2 ) 1 ( ) 1 ( ) ( ) ( 1 2 1 1 − − − + − − + = − − − n y e n y e n x e n x k n y α α α α [ ] ) 2 ( ) 1 ( 2 ) 2 ( ) 1 ( ) 1 ( ) 1 ( ) ( 2 2 2 2 + − + + + − − + − = − − − − n y e n y e n x e n x e k n y α α α α α α ) ( ) ( ) ( 2 1 n y n y n y + = , α α α