Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Scalar Quantization for Distributed Extremization

In many resource allocation problems, a centralized controller needs to award some resource to a user selected from a collection of distributed users with the goal of maximizing the utility the user would receive from the resource. This can be modeled as the controller computing an extremization of the distributed users’ utilities. The overhead rate necessary to enable the controller to reproduce the users’ local state can be prohibitively high. Two approaches to reduce this overhead are lossy estimation and interactive communication. In the lossy estimator framework, rate savings are achieved by tolerating a bounded expected reduction in utility. In interactive communication, rate savings come at the expense of delay. In this paper, we consider the design of a simple achievable scheme based on successive refinements of scalar quantization at each user. The optimal quantization policy is computed via a dynamic program and we demonstrate that tolerating a small increase in delay can yield significant rate savings. We then consider two simpler quantization policies to investigate the scaling properties of the rate-delay trade-os. Using a combination of these simpler policies, the performance of the optimal policy can be closely approximated with lower computational costs.
Some links between min-cuts, optimal spanning forests and watersheds

Di erent optimal structures: minimum cuts, minimum spanning forests and shortest-path forests, have been used as the basis for powerful image segmentation procedures. The well-known notion of watershed also falls into this category. In this paper, we present some new results about the links which exist between these di erent approaches. Especially, we show that min-cuts coincide with watersheds for some particular weight functions.
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects

While classical experiments on spike-timing dependent plasticity analyzed synaptic changes as a function of the timing of pairs of pre- and postsynaptic spikes, more recent experiments also point to the effect of spike triplets. Here we develop a mathematical framework that allows us to characterize timing based learning rules. Moreover, we identify a candidate learning rule with five variables (and 5 free parameters) that captures a variety of experimental data, including the dependence of potentiation and depression upon pre- and postsynaptic firing frequencies. The relation to the Bienenstock-Cooper-Munro rule as well as to some timing-based rules is discussed.
A New Method for Static Video Summarization Using Local Descriptors and Video Temporal Segmentation

The continuous creation of digital video has caused an exponential growth of digital video content. To increase the usability of such large volume of videos, a lot of research has been made. Video summarization has been proposed to rapidly browse large video collections. To summarize any type of video, researchers have relied on visual features contained in frames. In order to extract these features, different techniques have used local or global descriptors. In this paper, we propose a method for static video summarization that can produce meaningful and informative video summaries. We perform an evaluation using over 100 videos in order to achieve a stronger position about the performance of local descriptors in semantic video summarization. Our experimental results show, with a confidence level of 99%, that our proposed method using local descriptors and temporal video segmentation produces better summaries than state of the art methods. We also demonstrate the importance of a more elaborate method for temporal video segmentation, improving the generation of summaries, achieving 10% improvement in accuracy. We also acknowledge a marginal importance of color information when using local descriptors to produce video summaries.
Authors Consent to Include This Contribution in the Open Access Online Repository of Ic0902: Yes Name of Author Giving Consent: Implementation of a Distributed Compressed Sensing Algorithm on Usrp2 Platforms

Contribution to Working Group/Special Interest Group WG1 : Definition of cognitive algorithms for adaptation and configuration of a single link according to the status of external environment A bstract-It has been recently demonstrated that the challenging implementation of the signal detectors can be facilitated by using the compressive sampling theory. In this paper, we consider a network of secondary nodes that cooperate to detect the primary signals by sampling the overall bandwidth periodically at a rate much smaller than the Nyquist rate. The delays inherent to the propagation channel are used to implement a periodic non-uniform sampling detector when the secondary nodes combine their observations. We demonstrate that the proposed detector can efficiently detect the primary user signal, and implement the algorithm on a USRP2 platforms network.
The Study of Password Authentication System Based on Elliptic Curve Cryptosystem

Aiming at the security problems of the password authentication systems in existence, this paper studies the elliptic curve cryptosystem basic principle, and then proposes a new password authentication scheme based on the elliptic curve cryptosystem. The particular implementation of this scheme is presented. And the security is analyzed finally. In this scheme, user's passwords are difficult to be explained in the process of storage and transmission. Authentication information keep dynamic, so replay attack can be prevented effectively. In addition, veritable users can detect the illegal uses who misappropriate the passwords, so information divulges or resources embezzlement can be refrained. This scheme is secure, effective ,easy to implement and hopeful for application.
Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures

Abstract Severe acute respiratory syndrome (SARS) has been the first severe contagious disease to emerge in the 21st century. The available epidemic curves for SARS show marked differences between the affected regions with respect to the total number of cases and epidemic duration, even for those regions in which outbreaks started almost simultaneously and similar control measures were implemented at the same time. The authors developed a likelihood-based estimation procedure that infers the temporal pattern of effective reproduction numbers from an observed epidemic curve. Precise estimates for the effective reproduction numbers were obtained by applying this estimation procedure to available data for SARS outbreaks that occurred in Hong Kong, Vietnam, Singapore, and Canada in 2003. The effective reproduction numbers revealed that epidemics in the various affected regions were characterized by markedly similar disease transmission potentials and similar levels of effectiveness of control measures. In controlling SARS outbreaks, timely alerts have been essential: Delaying the institution of control measures by 1 week would have nearly tripled the epidemic size and would have increased the expected epidemic duration by 4 weeks.
International Factor Price Differences: Leontief was Right!

The factor price equalization hypothesis is widely at odds with the large variation in factor prices across countries. Similarly, the Heckscher-Ohlin-Vanek (HOV) theorem constitutes an incomplete description of trade in factor services: its predictions are always rejected empirically. These two issues are examined using a modification of the HOV model that allows for factor-augmenting international productivity differences. The empirical results are stark: this simple modification of the HOV theorem explains much of the factor content of trade and the cross-country variation in factor prices.
Cleavage-stage embryo rotation tracking and automated micropipette control: Towards automated single cell manipulation

Micromanipulation of individual biological cells, such as embryos during preimplantation genetic diagnosis (PGD), is a delicate and time-consuming task. Two major procedures in PGD include the rotation of the embryo to gain a more favorable position for zona breaching, and the extraction of a blastomere after an opening has been made in the zona pellucida. Rotation tracking of cleavage-stage embryos has not been reported given their lack of distinctive features. In this manuscript, a geometric model for partially determining the three dimensional (3-D) angular position of 2-cell embryos using two dimensional (2-D) microscopic brightfield images was derived and verified using a computer generated model. This model was then applied using computer vision algorithms on a rotating cleavage-stage mouse embryo, demonstrating partial 3-D rotation tracking. Furthermore, embryo micromanipulation tasks are typically performed manually using micropipettes. Technological advances have made automation of these tasks possible. This manuscript also presents computer vision algorithms for the segmentation and calibration of micropipettes. The calibration procedure allowed automated position control of the micropipettes without the need for real-time vision feedback using micropipette recognition algorithms, and effective position control was verified using semi-automated blastomere extraction experiments. This manuscript presents preliminary work towards the automation of cell manipulation procedures.
University of Hyogo at NTCIR-11 TaskMine by Dependency Parsing

Our method for TaskMine consists of three steps. Firstly, we search for seed Web pages by using the query string with a word (cid:16)houhou(cid:17), which means (cid:16)method(cid:17). We collect more pages in consideration of the anchor texts in the seed pages. Then, we (cid:28)nd pairs of chunks satisfying prede(cid:28)ned patterns by dependency parsing on the sentences. We extract a target and a postpositional particle from a depending chunk, and extract an operation and a negation (if it exists) from a depended chunk. We regard a quadruplet of them as a subtask. Finally, we rank the extracted subtasks by their frequency based score.
Increased miR-132 level is associated with visual memory dysfunction in patients with depression

Background Impaired visual memory seems to be a core feature of depression, while increased microRNA-132 (miR-132) levels have been widely reported in depression patients. The authors aimed to explore the relationship between miR-132 changes and visual memory deficits in unmedicated patients with major depressive disorder (MDD). Patients and methods A total of 62 medication-free MDD patients and 73 matched healthy controls (HCs) were tested for miR-132 expression level in peripheral blood using quantitative real-time polymerase chain reaction. We used a computerized neurocognitive task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) – pattern recognition memory (PRM) task – as a measurement of visual memory. The relationship between visual memory, miR-132 expression level, and clinical symptoms was explored in patients with MDD. Results Upregulated miR-132 expression levels were seen in MDD patients but not in HCs. Two-sample t-tests showed that MDD patients had decreased visual memory, mainly memory delayed compared to that of HCs. Correlation analyses revealed that in MDD patients, increased miR-132 expression levels were significantly correlated with visual memory as measured by the CANTABPRM. Hamilton Rating Scale for Anxiety scores were negatively correlated with PRM – number correct (immediate) and PRM – percent correct (immediate). Limitations The main limitations were missing data and lack of follow-up studies. Conclusion Our study suggests that increased miR-132 expression levels were associated with visual memory deficits, which may underlie the pathophysiology of MDD. In individuals with depression, immediate visual memory defects were positively correlated with anxiety symptoms.
Integrating Multithreading into the Spineless Tagless G-machine

To reduce the adverse effects of long latency communication operations in distributed implementations of the Spineless Tagless G-machine (STGM), a variant of the original abstract machine that contains explicit support for multithreading is introduced. In particular, source-to-source transformations can be used on the level of the abstract machine code to foster the tolerance to long latency communication. The changes to the original STG-language include a separation of demand from case selection together with the introduction of a new construct that provides an abstract notion of thread boundaries and thread synchronization.
Sex Differences in Object Location Memory

The ability to remember where objects were is thought to require multiple separate processes. One has to encode the precise positions occupied, assign the various objects to the correct (relative) locations, and achieve an integration of both types of spatial information. This study examined whether sex differences exist for these selective components of object location memory. Twenty males and 20 females participated in the following task. On a PC screen, they were shown a square with 10 different objects for 30 s. Subsequently, the objects disappeared from the screen, reappeared in a row above the square, and subjects were asked to relocate them in three different conditions. In the object-to-position-assignment condition, the original positions were premarked in the square, so subjects needed only to assign the correct object to the correct position. In the positions-only condition, all objects assumed the same identity. Therefore, subjects had only to reproduce the precise positions. Finally, in the combined condition, subjects were required to replace all the different objects in the square without any of object positions premarked. The absolute displacements between an object's original and its relocated position reflect the integration mechanism. Females did as well as males in the object-to-position-assignment condition and on the absolute displacements in the combined condition, but they were less efficient than males in positional reconstruction per se. Thus, it seems that the male advantage in spatial memory is not a general effect but applies only to certain specific processing components. Moreover, the employment of a dual task during encoding, concurrent articulatory suppression, yielded no significant interactions with sex. This suggests that females' weaker positional encoding does not derive from the use of an inefficient verbal strategy.
Quantitative Safety Guarantees for Physical Human-Robot Interaction

If robots are to be introduced into the human world as assistants to aid a person in the completion of a manual task two key problems of today's robots must be solved. The human-robot interface must be intuitive to use and the safety of the user with respect to injuries inflicted by collisions with the robot must be guaranteed. In this paper we describe the formulation and implementation of a control strategy for robot manipulators which provides quantitative safety guarantees for the user of assistant-type robots. We propose a control scheme for robot manipulators that restricts the torque commands of a position control algorithm to values that comply to preset safety restrictions. These safety restrictions limit the potential impact force of the robot in the case of a collision with a person. Such accidental collisions may occur with any part of the robot and therefore the impact force not only of the robot's hand but of all surfaces is controlled by the scheme. The integration of a visual control interface and the safely con trolled robot allows the safe and intuitive interaction between a per son and the robot. As an example application, the system is pro grammed to retrieve eye-gaze-selected objects from a table and to hand them over to the user on demand.
PSO-AG: A Multi-Robot Path Planning and obstacle avoidance algorithm

Robot path planning is one of the most challenging tasks as it involves several parameters and different constraints. Robots moving in an environment face many challenges such as avoiding obstacles. Path planning aims at directing the robot to reach a target via a collision-free path. Online Path Planning allows robots to move in an environment they do not have prior knowledge about and ought to discover while moving. This paper introduces, PSO-AG, an online multi robot path planning algorithm that combines the benefits of particle swarm optimization and Agoraphilic algorithms. In PSO-AG, particle swarm optimization works as the moving path planner that decides the next point for the robots to reach the target, and Agoraphilic works as the moving controller that steers the robots towards the target while avoiding obstacles along the path. Simulation was used to evaluate the performance of PSO-AG in different scenarios; including different sizes of robots swarms and different levels of environment difficulty; ranging from obstacle-free to partially obstructed environment. Experiments showed promising results of PSO-AG's scalability and target reaching rate.
Demographic information prediction based on smartphone application usage

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).
Stacked Training for Overfitting Avoidance in Deep Networks

When training deep networks and other complex networks of predictors, the risk of overfitting is typically of large concern. We examine the use of stacking, a method for training multiple simultaneous predictors in order to simulate the overfitting in early layers of a network, and show how to utilize this approach for both forward training and backpropagation learning in deep networks. We then compare this approach to overfitting avoidance with the dropout method for a number of common tasks.
Four-chamber plane detection in cardiac ultrasound images based on improved imbalanced AdaBoost algorithm

An improved imbalanced AdaBoost algorithm called ImAdaBoost was proposed for four-chamber plane detection in cardiac ultrasound images. By assigning dynamic weight adjustment factor to each sample according the precision and true positive rate of weak learner, imbalanced classification problem was converted to cost-sensitive classification problem. Template matching method was used to detect the degree of probe rotation, planes with non-zero degree was discarded to reduce the imbalance. Using multi-instance learning method, the ultrasound image was considered as a bag which was composed of small regions, each region was described by SIFT features, the Bag of Words method was used to map instance features to bag features. ImAdaBoost was used to detect four-chamber planes in cardiac ultrasound images. The experimental results indicate that ImAdaBoost is effective on clinical cardiac ultrasound images. True positive rate is increased while false positive rate is controlled within an acceptable range.
What drives quality improvement in chronic kidney disease (CKD) in primary care: process evaluation of the Quality Improvement in Chronic Kidney Disease (QICKD) trial

Objectives This study is a process evaluation of the Quality Improvement in Chronic Kidney Disease (QICKD) study, comparing audit-based education (ABE) and sending clinical guidelines and prompts (G&P) with usual practice, in improving systolic blood pressure control in primary care. This evaluation aimed to explore how far clinical staff in participating practices were aware of the intervention, and why change in practice might have taken place. Setting 4 primary care practices in England: 2 received ABE, and 2 G&P. We purposively selected 1 northern/southern/city and rural practice from each study arm (from a larger pool of 132 practices as part of the QICKD trial). Participants The 4 study practices were purposively sampled, and focus groups conducted with staff from each. All staff members were invited to attend. Interventions Focus groups in each of 4 practices, at the mid-study point and at the end. 4 additional trial practices not originally selected for in-depth process evaluation took part in end of trial focus groups, to a total of 12 focus groups. These were recorded, transcribed and analysed using the framework approach. Results 5 themes emerged: (1) involvement in the study made participants more positive about the CKD register; (2) clinicians did not always explain to patients that they had CKD; (3) while practitioners improved their monitoring of CKD, many were sceptical that it improved care and were more motivated by pay-for-performance measures; (4) the impact of study interventions on practice was generally positive, particularly the interaction with specialists, included in ABE; (5) the study stimulated ideas for future clinical practice. Conclusions Improving quality in CKD is complex. Lack of awareness of clinical guidelines and scepticism about their validity are barriers to change. While pay-for-performance incentives are the main driver for change, quality improvement interventions can have a complementary influence.
An integrated approach for documenting, exploring, and building framework-based software

The use of well-designed class libraries and frameworks is a key element of object oriented software development. However, while frameworks may significantly reduce development time and cost, they are difficult to understand and therefore hard to learn to use. We present an environment that supports documenting, browsing and editing of selected aspects of frameworks and framework based applications on an abstraction level that is not available through static or dynamic program analysis. The main ideas of this environment are a specialized desktop metaphor for visualizing and browsing software architectures, the support of framework based development by means of architecture templates, and an open tool architecture that supports the integration of any additional tools for visualizing and editing application and framework parts.