Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermediate-Mass Black Hole Induced Quenching of Mass Segregation in Star Clusters

In many theoretical scenarios it is expected that intermediate-mass black holes (IMBHs, with masses M ∼ 102–104 M☉) reside at the centers of some globular clusters. However, observational evidence for their existence is limited. Several previous numerical investigations have focused on the impact of an IMBH on the cluster dynamics or brightness profile. Here we instead present results from a large set of direct N-body simulations including single and binary stars. These show that there is a potentially more detectable IMBH signature, namely on the variation of the average stellar mass between the center and the half-light radius. We find that the existence of an IMBH quenches mass segregation and causes the average mass to exhibit only modest radial variation in collisionally relaxed star clusters. This differs from when there is no IMBH. To measure this observationally requires high-resolution imaging at the level of that already available from the Hubble Space Telescope (HST) for the cores of a large sample of Galactic globular clusters. With a modest additional investment of HST time to acquire fields around the half-light radius, it will be possible to identify the best candidate clusters to harbor an IMBH. This test can be applied only to globulars with a half-light relaxation time ≲1 Gyr, which is required to guarantee efficient energy equipartition due to two-body relaxation.
Cell density-driven detailed placement with displacement constraint

Modern placement process involves global placement, legalization, and detailed placement. Global placement produce a placement solution with minimized target objective, which is usually wire-length, routability, timing, etc. Legalization removes cell overlap and aligns the cells to the placement sites. Detailed placement further improves the solution by relocating cells. Since target objectives like wire-length and timing are optimized in global placement, legalization and detailed placement should not only minimize their own objectives but also preserve the global placement solution. In this paper, we propose a detailed placement algorithm for minimizing wire-length, while preserving the global placement solution by cell displacement constraint and target cell density objective. Our detailed placer involves two steps: Global Move that allocates each cell into a bin/region that minimizes wire-length, while not overflowing the target cell density. Local Move that finely adjust the cell locations in local regions to further minimize the wire-length objective. With large-scale benchmarks from ICCAD 2013 detailed placement contest, the results show that our detailed placer, RippleDP, can improve the global placement results by 13.38% - 16.41% on average under displacement constraint and target placement density objective.
An analysis of the single sensor bearings-only tracking problem

In this paper we examine the single sensor, bearings-only target tracking problem. This problem is known to be difficult due to the potential unobservability of elements of the targetpsilas state and the high degree of nonlinearity in the measurement process. We compare the performance of three different tracking algorithms. The algorithms are of varying degrees of computational complexity. The results of these comparisons can be used to gain insight into the degree of difficulty caused by each of these two issues. The understanding this provides can aid in the selection of an appropriate target tracking filter for a given application.
k-Nearest Neighbor Classification over Semantically Secure Encrypted Relational Data

Data Mining has wide applications in many areas such as banking, medicine, scientific research and among government agencies. Classification is one of the commonly used tasks in data mining applications. For the past decade, due to the rise of various privacy issues, many theoretical and practical solutions to the classification problem have been proposed under different security models. However, with the recent popularity of cloud computing, users now have the opportunity to outsource their data, in encrypted form, as well as the data mining tasks to the cloud. Since the data on the cloud is in encrypted form, existing privacy-preserving classification techniques are not applicable. In this paper, we focus on solving the classification problem over encrypted data. In particular, we propose a secure k-NN classifier over encrypted data in the cloud. The proposed protocol protects the confidentiality of data, privacy of user's input query, and hides the data access patterns. To the best of our knowledge, our work is the first to develop a secure k-NN classifier over encrypted data under the semi-honest model. Also, we empirically analyze the efficiency of our proposed protocol using a real-world dataset under different parameter settings.
Anxiety-Related Mechanisms of Respiratory Dysfunction in a Mouse Model of Rett Syndrome

Rett syndrome (RTT) is a severe neurological disorder that is associated with mutations in the methyl-CpG binding protein 2 (MECP2) gene. RTT patients suffer from mental retardation and behavioral disorders, including heightened anxiety and state-dependent breathing irregularities, such as hyperventilation and apnea. Many symptoms are recapitulated by the Mecp2-null male mice (Mecp2−/y). To characterize developmental progression of the respiratory phenotype and explore underlying mechanisms, we examined Mecp2−/y and wild-type (WT) mice from presymptomatic periods to end-stage disease. We monitored breathing patterns of unrestrained mice during wake–sleep states and while altering stress levels using movement restraint or threatening odorant (trimethylthiazoline). Respiratory motor patterns generated by in situ working heart–brainstem preparations (WHBPs) were measured to assess function of brainstem respiratory networks isolated from suprapontine structures. Data revealed two general stages of respiratory dysfunction in Mecp2−/y mice. At the early stage, respiratory abnormalities were limited to wakefulness, correlated with markers of stress (increased fecal deposition and blood corticosterone levels), and alleviated by antalarmin (corticotropin releasing hormone receptor 1 antagonist). Furthermore, the respiratory rhythm generated by WHBPs was similar in WT and Mecp2−/y mice. During the later stage, respiratory abnormalities were evident during wakefulness and sleep. Also, WHBPs from Mecp2−/y showed central apneas. We conclude that, at early disease stages, stress-related modulation from suprapontine structures is a significant factor in the Mecp2−/y respiratory phenotype and that anxiolytics may be effective. At later stages, abnormalities of brainstem respiratory networks are a significant cause of irregular breathing patterns and central apneas.
Brachial artery Doppler flux parameters before and after hot flush in Mexican postmenopausal women: preliminary report

Objective To analyse brachial artery flux parameters in postmenopausal women before and after hot flush. Material and methods Two groups of postmenopausal women were studied: Group I, without vasomotor symptoms (n = 10) and Group II, with vasomotor symptoms (n = 10). In all them a brachial artery Doppler ultrasound was done, measuring before and after hyperaemic stimulus of the arterial diameter (AD), the pulsatility index (PI), and the resistive index (RI). In Group I, measurements were done at baseline and five minutes after. In Group II, measurements were at baseline, and one and five minutes after the hot-flush. Comparison between the groups was done with Mann-Whitney U test, and within the groups with Wilcoxon test. Results No differences were found among the groups in Doppler parameters. When comparing each group separately, in Group I, at baseline and at five minutes measurements, the AD was greater after the hyperaemic stimulus than before it. In group II at baseline, the PI was significantly greater after the hyperaemic stimulus than before to it. At the first and fifth minute, the AD was significantly greater after the hyperaemic stimulus than before to it. Conclusions No differences were found between those who did not have and those who had hot flushes.
Does Learner Control Affect Learning?

Many intelligent tutoring systems permit some degree of learner control. A natural question is whether the increased student engagement and motivation such control provides results in additional student learning. This paper uses a novel approach, learning decomposition, to investigate whether students do in fact learn more from a story they select to read than from a story the tutor selects for them. By analyzing 346 students reading approximately 6.9 million words, we have found that students learn approximately 25% more in stories they choose to read, even though from a purely pedagogical standpoint such stories may not be as appropriate as those chosen by the computer. Furthermore, we found that (for our instantiation of learner control) younger students may derive less benefit from learner control than older students, and girls derive less benefit than boys.
Verbal Learning & Memory Function among Children with ADHD and Emotional Disorders

Memory function and learning process are commonly observed impaired among children with Attention Deficit Hyperactive Disorder (ADHD). However, children with Emotional Disorders (ED) too report inability to attend, register and recall the stimuli that eventually result in poor academic performance. The present study aimed to study the nature of impairment in verbal learning and memory function among children with ADHD and ED. The study further aimed to explore the effect of severity of illnesses over the degree of impairment in verbal learning and memory function in both the groups. Twenty children meeting the ICD-10 criteria for ADHD and twenty children meeting the ICD-10 criteria under F-93 for Emotional Disorders (ED) were assessed on the measure of verbal learning & memory and compared with twenty age and education matched normal control group. Children with ADHD and ED performed poorly on the measure of verbal learning and memory in comparison of normal control, but the degree of impairment was observed relatively higher among ADHD children than children with ED. However, severity of illness was not observed a contributory factor for the impairment in verbal learning and memory function in both the groups. Impairment in verbal learning and memory function is not confined only to the children with ADHD but these are now recognizable among children with Emotional Disorders too. Both the groups (Children with ADHD and ED) reflected impaired memory and learning function but in terms of degree of impairment the children with ADHD were always found standing towards higher side than children with emotional disorder.
Person Re-Identification Based on Spatiogram Descriptor and Collaborative Representation

Feature and metric designing are two vital aspects in person re-identification. In this letter, we firstly propose a novel spatiogram based person descriptor. Such spatiograms of different image regions from several color channels are calculated and accumulated to create a histogram vector and two distinctive spatial statistical vectors. Secondly, through further investigating the multi-shot set-based metric based on the recent collaborative representation model, we propose an effective and efficient multi-shot metric, which fuses the residual and coding coefficients after collaboratively coding samples on all person classes. Finally, we evaluate the proposed descriptor and metric with other published methods on benchmark datasets. Our methods not only achieve state-of-the-art results but also are novel, straightforward and computationally efficient, which will facilitate the real-time surveillance applications such as pedestrian tracking.
The Impact of Process Scaling on Scratchpad Memory Energy Savings

Scratchpad memories have been shown to reduce power consumption, but the different characteristics of nanometer scale processes, such as increased leakage power, motivate an examination of how the benefits of these memories change with process scaling. Process and application characteristics affect the amount of energy saved by a scratchpad memory. Increases in leakage as a percentage of total power particularly impact applications that rarely access memory. This study examines how the benefits of scratchpad memories have changed in newer processes, based on the measured performance of the WIMS (Wireless Integrated MicroSystems) microcontroller implemented in 180- and 65-nm processes and upon simulations of this microcontroller implemented in a 32-nm process. The results demonstrate that scratchpad memories will continue to improve the power dissipation of many applications, given the leakage anticipated in the foreseeable future.
Glass transitions and shear thickening suspension rheology

We introduce a class of simple models for shear thickening and/or “jamming” in colloidal suspensions. These are based on the schematic mode coupling theory (MCT) of the glass transition, having a memory term that depends on a density variable, and on both the shear stress and the shear rate. (Tensorial aspects of the rheology, such as normal stresses, are ignored for simplicity.) We calculate steady-state flow curves and correlation functions. Depending on model parameters, we find a range of rheological behaviors, including “S-shaped” flow curves, indicating discontinuous shear thickening, and stress-induced transitions from a fluid to a nonergodic (jammed) state, showing zero flow rate in an interval of applied stress. The shear thickening and jamming scenarios that we explore appear broadly consistent with experiments on dense colloids close to the glass transition, despite the fact that we ignore hydrodynamic interactions. In particular, the jamming transition we propose is conceptually quite differen.
Web page cleaning for web mining through feature weighting

Unlike conventional data or text, Web pages typically contain a large amount of information that is not part of the main contents of the pages, e.g., banner ads, navigation bars, and copyright notices. Such irrelevant information (which we call Web page noise) in Web pages can seriously harm Web mining, e.g., clustering and classification. In this paper, we propose a novel feature weighting technique to deal with Web page noise to enhance Web mining. This method first builds a compressed structure tree to capture the common structure and comparable blocks in a set of Web pages. It then uses an information based measure to evaluate the importance of each node in the compressed structure tree. Based on the tree and its node importance values, our method assigns a weight to each word feature in its content block. The resulting weights are used in Web mining. We evaluated the proposed technique with two Web mining tasks, Web page clustering and Web page classification. Experimental results show that our weighting method is able to dramatically improve the mining results.
Turbo equalization over a frequency selective channel

This paper deals with a new receiver scheme that uses the same principle as turbo-codes and called, for this reason a turbo-equalizer. This receiver associates an equalizer and a soft ouput channel decoder through an iterative process. It is well suited for transmissions over channels having a delay spread of many symbol durations and using M-QAM modulation. In certain cases, the turboequalizer can totally overcome the channel frequency selectivity without increasing the level of the noise and thus, this receiver can achieve the same performance as a non selective Gaussian channel with coding. I INTRODUCTION Mobile radio communications and digital broadcasting are at the origin of much research about frequency selectivity channels affected by Doppler. Different solutions have been proposed to combat the channel frequency selectivity, in particular channel coding and equalization. In a classical approach the equalizer is fed by the demodulator outputs and does not use the redundancy introduced by the channel coding to perform its processing. It is possible to improve the receiver performance if the equalizer is fed both by the demodulator ouputs and the soft decisions provided by the channel decoder. With this approach it is necessary to take into account the latency introduced by the channel decoder that is carried out by using an iterative process. The equalizer at iteration p is fed by soft decisions provided by the decoder at iteration ( p − 1). This new receiver scheme will be called turboequalization for the rest of this paper. II PRESENTATION OF THE TRANSMISSION SCHEME Let us consider the transmission scheme depicted in figure 1.
An Efficient Clustering Algorithm for Small Text Documents

Clustering text documents into different category groups is an important problem. The size of desired clusters is an important requirement for a clustering solution. In this paper, we present an efficient clustering algorithm called RTC based on the spherical k-means algorithm for small text documents. In RTC, we present a new initial centers choice method based on the density and farthest distance strategies. Based on the first variations adjustment of Ping-Pong algorithm, we also present a new partition adjustment method, which is guided by the set of border objects of clusters. We test the algorithm performance based on the Chinese natural language platform. The experimental results show that RTC outperforms the spherical k-means and bisecting k-means in clustering accuracy and Ping-Pong both in clustering accuracy and clustering time. Especially, in the clustering time aspect, RTC sometimes is 5 times faster than Ping- Pong.
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning

We consider the problem of undiscounted reinforcement learning in continuous state space. Regret bounds in this setting usually hold under various assumptions on the structure of the reward and transition function. Under the assumption that the rewards and transition probabilities are Lipschitz, for 1-dimensional state space a regret bound of O(T3/4 after any T steps has been given by Ortner and Ryabko (2012). Here we improve upon this result by using non-parametric kernel density estimation for estimating the transition probability distributions, and obtain regret bounds that depend on the smoothness of the transition probability distributions. In particular, under the assumption that the transition probability functions are smoothly differentiable, the regret bound is shown to be O(T2/3 asymptotically for reinforcement learning in 1-dimensional state space. Finally, we also derive improved regret bounds for higher dimensional state space.
Interhemispheric Interactions of the Human Thalamic Reticular Nucleus

The thalamic reticular nucleus is an important structure governing the recurrent interactions between the thalamus and cortex that may provide a substrate for unified perception. Despite the importance of the TRN, its activity has been scarcely investigated in vivo in animal models, and never in humans. Here we anatomically identify the human TRN using multiple registered and averaged proton density-weighted structural MRI scans and drive its functional activity with a dual phase-encoded stimulus. We characterize the retinotopic and temporal response properties in the visual sector of the TRN and measured an inhibitory relationship with the contralateral LGN. These observations provide a basis for further gross characterizations of the role of the TRN in human behavior.
Command injection attacks, continuations, and the Lambek calculus

This paper shows connections between command injection attacks, continuations, and the Lambek calculus: certain command injections, such as the tautology attack on SQL, are shown to be a form of control effect that can be typed using the Lambek calculus, generalizing the double-negation typing of continuations. Lambek's syntactic calculus is a logic with two implicational connectives taking their arguments from the left and right, respectively. These connectives describe how strings interact with their left and right contexts when building up syntactic structures. The calculus is a form of propositional logic without structural rules, and so a forerunner of substructural logics like Linear Logic and Separation Logic.
Optimal Policy for Initial Selection Radio Access Technology in Next Generation Wireless Networks

Rodolfo W. L. Coutinho Pos-Graduation Program in Electrical Engineering Federal University of Para Belém, Paŕ a, Brazil Email: rwlc@ufpa.br Vitor L. Coelho Faculty of Computer Science Federal University of Par ́ Belém, Paŕ a, Brazil Email: vitorlimac2@hotmail.com Joao C. W. A. Costa Pos-Graduation Program in Electrical Engineering Federal University of Par ́ Belém, Paŕ a, Brazil Email: jweyl@ufpa.br Glaucio H. S. Carvalho Faculty of Computer Science Federal University of Par ́ Belém, Paŕ a, Brazil Email: ghsc@ufpa.br
Blending of learning tools for enhanced practical wireless communications education

This paper addresses the blending of methodologies, applications, and the use of the Internet for delivery of knowledge and instruction in an undergraduate wireless communications engineering course. This solution combines several different delivery methods, such as face-to-face, team collaboration, field measurements, combined with high-end professional simulation software. Two examples of handover in a dual-mode cellular network and radio frequency (RF) signal propagation modeling are considered. Demonstrations and verification of theoretical models are presented based-on blending of learning means. Lessons are learned to enhance hands-on experiments in traditional laboratories to strengthen indepth analysis and learning skills of students, in addition to promoting proper team collaboration on projects.
A New Algorithm for Labeling of Human Motion

In this paper, we present a novel approach for the labeling of human motion based on a probabilistic model of body features and Constraint-Based Genetic Algorithm (CBGA), which learns the set of conditional independence relations among the body features through a fitness function. The approach allows the user to add custom rules to produce valid candidate solutions to achieve more accurate results with constraint-based genetic operators. We also extend these results to learning the probabilistic structure of human body to improve the labeling results, the handling of missing body parts, and the integration of multi-frame information to improve the accuracy rates. Finally, we analyze the performance of our proposed approach and show that it outperforms most of the current state of the art methods on a set of motion captured walking, running and dancing sequences in terms of quality and robustness. node ordering as a candidate solution in the population, and for each ordering, the solution is passed to K2, a greedy search algorithm, to obtain a network. In this work, the ordering and the conditional independence relations are also learnt separately, and the computation cost is very huge in this GA- based method. To resolve the computational efficiency problem of GA, search space reduction using constraints and supervised learning have been developed. Garofalakis (5) proposed a constraint-based algorithm to specify the expected tree size and accuracy in the search process. In other words, constraints can be used as a trade-off mechanism between the model accuracy and computational efficiency in the tree- building or tree-pruning process(6). In this paper, we modify posterior probability to resolve the missing body parts using CBGA. We also integrate information from multiple frames to improve the accuracy and robustness of labeling.