Miracle-dz/UniFAR-FTUs huggingface dataset:
SDN-enabled OPS with QoS guarantee for reconfigurable virtual data center networks

Optical packet switching (OPS) can enhance the performance of data center networks (DCNs)by providing fast and large-capacity switching capability. Benefiting from the software-defined networking (SDN) control plane, which could update the look-up-table (LUT) of the OPS, virtual DCNs can be flexibly created and reconfigured. In this work, we have implemented and assessed an SDN-based control framework for an OPS node, where the OpenFlow protocol has been extended in support of the OPS switching paradigm. Application flows are switched by the OPS at submicrosecond hardware speed, decoupled from the slower (millisecond timescale) SDN control operation. By the DCN infrastructure provider, the virtual networks become directly programmable with the abstraction of the underlying OPS node. Experimental results validate the successful setup of virtual network slices for intra-data center interconnect and quality of service (QoS) guarantee for high-priority application flows. Data plane resources are efficiently shared by exploiting statistical multiplexing. In addition, the capability of exposing per-port OPS traffic statistics information to the SDN controller enables the implementation and experimental validation of load balancing algorithms to improve the QoS performance.
Unique decodability of bigram counts by finite automata

We revisit the problem of deciding whether a given string is uniquely decodable from its bigram counts by means of a finite automaton. An efficient algorithm for constructing a polynomial-size nondeterministic finite automaton that decides unique decodability is given. Conversely, we show that the minimum deterministic finite automaton for deciding unique decodability has at least exponentially many states in alphabet size.
The Behaviour of the FloquetMultipliersof Periodic SolutionsNear a Homoclinic

In this paper, we describe the behaviour of the moduli of the Floquet multipliers of periodic solutions near a homoclinic orbit. We discuss the relation between the Floquet multipliers and the eigenvalues of the equilibrium point at which the homoclinic orbit emanates. For two-dimensional problems, for which the equilibrium point has two real eigenvalues, we derive an asymptotic estimate for the nontrivial Floquet multipliers in terms of the period. For more complicated situations (higher dimensional problems, complex conjugate pairs of eigenvalues), we perform numerical experiments for some model equations, showing that eigenvalues with large negative real part cause small Floquet multipliers. Abstract In this paper, we describe the behaviour of the moduli of the Floquet multipliers of periodic solutions near a homoclinic orbit. We discuss the relation between the Floquet multipliers and the eigenvalues of the equilibrium point at which the homoclinic orbit emanates. For two-dimensional problems, for which the equilibrium point has two real eigenvalues, we derive an asymptotic estimate for the nontrivial Floquet multipliers in terms of the period. For more complicated situations (higher dimensional problems, complex conjugate pairs of eigenvalues), we perform numerical experiments for some model equations, showing that eigenvalues with large negative real part cause small Floquet multipliers.
Calibration and implementation of a novel omnidirectional vision system for robot perception

Motivated by the demand of detecting surroundings for a humanoid robot, we developed an omnidirectional vision system for robot perception (OVROP) with 5 Degrees of Freedom (DOFs). OVROP is mainly composed by three components: hardware, control architecture and image processing. Endowed with compatible hardware and software interfaces, OVROP can be applied to a variety of robots. With the utilization of black-box algorithm (BBA) and cylindrical coordinate transformation (CCT), OVROP allows undistorted omnidirectional perception of static surroundings. For the omnidirectional tracking of moving objects, subdivision method has been used and the experimental results prove its high efficiency. We have also confirmed that OVROP can perform 3D reconstruction of a desired target within 50ms. Thus OVROP is able to provide detailed information of surrounding environment with full range in real time for robot perception.
Classification of Reflexive Polyhedra in Three Dimensions

We present the last missing details of our algorithm for the classification of reflexive polyhedra in arbitrary dimensions. We also present the results of an application of this algorithm to the case of three dimensional reflexive polyhedra. We get 4319 such polyhedra that give rise to K3 surfaces embedded in toric varieties. 16 of these contain all others as subpolyhedra. The 4319 polyhedra form a single connected web if we define two polyhedra to be connected if one of them contains the other.
QR Decomposition in a Multicore Environment

In this study we examine performance benefits of implementing the QR decomposition in a way that takes advantage of multiple processes or threads. This is done by partitioning the matrix into blocks of a certain number of rows, which is called the blocksize. We examine this algorithm on “tall and skinny” matrices, which are matrices that have a very large number of rows, but comparatively fewer columns. These matrices are very important, as one of their most common uses is Linear Regression, which is a tool used in many different fields. We also compare this implementation to one which uses a MapReduce environment to compute the QR decomposition. We find that partitioning the matrix and using multiple processes to compute the QR decomposition in parallel provides for computing the decomposition much faster than computing the QR decomposition immediately on the original matrix.
A Framework for the Evaluation of Complete Weighted Network Topology in EEG Functional Connectivity

Background: Graph theory provides an analytical framework for brain functional connectivity. The complete weighted networks (CWNs) derived from functional connectivity analyses are fundamentally different to the sparse binary networks studied in other research areas. Nonetheless, CWNs are typically analysed using sparse network methodology. New Methods: We offer an alternative framework for brain networks designed specifically for CWN analysis, generalising widely used binary network models to CWN form and analysing at different densities using straightforward metrics related to the main topological features of brain networks: integration, regularity and modularity. Importantly, we introduce two novel techniques which complement and enhance this framework. Firstly, a new metric to measure the complexity of a network. Secondly, a new CWN null model for EEG functional connectivity. Results: Our complexity metric is demonstrated to be a specific classifier for real world network behaviour. Our null model offers an unprecedented ease and rigour of comparability for functional brain networks exactly because of its CWN formulation. Demonstrating these techniques in the context of EEG networks obtained from both zero-lag and phase-lag connectivity measures, we reveal that EEG phase-lag networks attain the maximal values of complexity achieved by our CWN null model. Comparison to existing methods: This framework extends existing methodology and is not directly comparable to other methods. Particularly, our null model is the first CWN null model. Complexity shows important differences to the three pre-existing topological features. Conclusions: These developments offer solid and flexible foundations for research in functional connectivity, but also, more generally, the implications extend to all graph analysis domains.
The Use of Head-and-Torso Models for Improved Spatial Sound Synthesis

Author(s): Algazi, Ralph; Duda, Richard O; Thompson, Dennis M. | Abstract: This paper concerns the use of a simple head-and-torso model to correct deficiencies in the low-frequency behavior of experimentally measured head-related transfer functions (HRTFs). This so-called \snowman'' model consists of a spherical head located above a spherical torso. In addition to providing improved low- frequency response for music reproduction, the model provides the major low-frequency localization cues, including cues for low-elevation as well as high-elevation sources. The model HRTF and the measured HRTF can be easily combined by using the phase response of the model at all frequencies and by \cross-fading'' between the dB magnitude responses of the model and the measurements. For e+-cient implementation, the exact snowman HRTF is approximated by two time delays and two first-order IIR filters. Because the poles are independent of the location of the virtual source, this supports a simple real-time implementation that allows for arbitrarily rapid head and source motion.
PGQ: Combining policy gradient and Q-learning

Policy gradient is an efficient technique for improving a policy in a reinforcement learning setting. However, vanilla online variants are on-policy only and not able to take advantage of off-policy data. In this paper we describe a new technique that combines policy gradient with off-policy Q-learning, drawing experience from a replay buffer. This is motivated by making a connection between the fixed points of the regularized policy gradient algorithm and the Q-values. This connection allows us to estimate the Q-values from the action preferences of the policy, to which we apply Q-learning updates. We refer to the new technique as 'PGQL', for policy gradient and Q-learning. We also establish an equivalency between action-value fitting techniques and actor-critic algorithms, showing that regularized policy gradient techniques can be interpreted as advantage function learning algorithms. We conclude with some numerical examples that demonstrate improved data efficiency and stability of PGQL. In particular, we tested PGQL on the full suite of Atari games and achieved performance exceeding that of both asynchronous advantage actor-critic (A3C) and Q-learning.
Altered Ureteric Branching Morphogenesis and Nephron Endowment in Offspring of Diabetic and Insulin-Treated Pregnancy

There is strong evidence from human and animal models that exposure to maternal hyperglycemia during in utero development can detrimentally affect fetal kidney development. Notwithstanding this knowledge, the precise effects of diabetic pregnancy on the key processes of kidney development are unclear due to a paucity of studies and limitations in previously used methodologies. The purpose of the present study was to elucidate the effects of hyperglycemia on ureteric branching morphogenesis and nephrogenesis using unbiased techniques. Diabetes was induced in pregnant C57Bl/6J mice using multiple doses of streptozotocin (STZ) on embryonic days (E) 6.5-8.5. Branching morphogenesis was quantified ex vivo using Optical Projection Tomography, and nephrons were counted using unbiased stereology. Maternal hyperglycemia was recognised from E12.5. At E14.5, offspring of diabetic mice demonstrated fetal growth restriction and a marked deficit in ureteric tip number (control 283.7±23.3 vs. STZ 153.2±24.6, mean±SEM, p<0.01) and ureteric tree length (control 33.1±2.6 mm vs. STZ 17.6±2.7 mm, p = 0.001) vs. controls. At E18.5, fetal growth restriction was still present in offspring of STZ dams and a deficit in nephron endowment was observed (control 1246.2±64.9 vs. STZ 822.4±74.0, p<0.001). Kidney malformations in the form of duplex ureter and hydroureter were a common observation (26%) in embryos of diabetic pregnancy compared with controls (0%). Maternal insulin treatment from E13.5 normalised maternal glycaemia but did not normalise fetal weight nor prevent the nephron deficit. The detrimental effect of hyperglycemia on ureteric branching morphogenesis and, in turn, nephron endowment in the growth-restricted fetus highlights the importance of glycemic control in early gestation and during the initial stages of renal development.
Wireless Sensor Network Wavelet Signal Processing

Application of information technologies (IT) and wireless networking will liberate people from such confinement and enable continuous real time monitoring of physiological data, which is vital for medical care. To achieve this goal, it is necessary research and development on wearable intelligent sensor devices, sensor miniaturization, signal processing, wireless transmission, and databases for these vital data. Our goal is to implement wavelet transformations in our measured system.
Dynamic communication of humanoid robot with multiple people based on interaction distance

Research on human-robot interaction is getting an increasing amount of attention. Since almost all the research has dealt with only communication between one robot and one person, there have been quite few discussions about communication between a robot and multiple people. This paper proposes a method which enables robots to communicate with multiple people using the 'selection priority of the interactive partner' based on the concept of 'Proxemics'. In this method, a robot changes active sensory-motor modalities based on the 'interaction distance' information. The proposed method is implemented into a humanoid robot SIG2 using subsumption architecture. SIG2 has various sensory-motor modalities to interact with humans. A demonstration of SIG2 showed that the proposed method works well during interaction with multiple people.
Integrated Visual Analysis for Heterogeneous Datasets in Cohort Studies

Current medical research is often hypothesis driven, focusing on a limited number of parameters showing, or expected to show, some relation with the disease. When a supporting scientific ground or proper hypothesis is lacking however, this approach is not always fruitful. Visual analytics has seen limited application in medical research. We propose that visual analytics can be used to study parameters across patients, especially in cases where no clear hypothesis is available from the start. This can help medical researchers to focus their efforts. We present a visual analysis framework which provides highly interactive visual analysis of cohort data, and is able to deal with irregular multi-timepoint, imaging and non-imaging data. The framework integrates the extraction of features into the process of visual analysis and makes use of a carefully designed data structure able to keep track of data dependencies and interrelationships in inhomogeneous cohort study data. We evaluated the framework on a cohort of patients suspected of having neuropsychiatric SLE, a heterogeneous rheumatic disease. Visual analysis revealed a number of observations corroborating earlier findings. We were also able to identify new trends in the data that could indicate directions for further research, and illustrated thereby the potential of visual analytics to operate as a hypothesis generating tool.
Rehospitalizations among patients in the Medicare fee-for-service program.

BACKGROUND Reducing rates of rehospitalization has attracted attention from policymakers as a way to improve quality of care and reduce costs. However, we have limited information on the frequency and patterns of rehospitalization in the United States to aid in planning the necessary changes. METHODS We analyzed Medicare claims data from 2003-2004 to describe the patterns of rehospitalization and the relation of rehospitalization to demographic characteristics of the patients and to characteristics of the hospitals. RESULTS Almost one fifth (19.6%) of the 11,855,702 Medicare beneficiaries who had been discharged from a hospital were rehospitalized within 30 days, and 34.0% were rehospitalized within 90 days; 67.1% [corrected] of patients who had been discharged with medical conditions and 51.5% of those who had been discharged after surgical procedures were rehospitalized or died within the first year after discharge. In the case of 50.2% [corrected] of the patients who were rehospitalized within 30 days after a medical discharge to the community, there was no bill for a visit to a physician's office between the time of discharge and rehospitalization. Among patients who were rehospitalized within 30 days after a surgical discharge, 70.5% were rehospitalized for a medical condition. We estimate that about 10% of rehospitalizations were likely to have been planned. The average stay of rehospitalized patients was 0.6 day longer than that of patients in the same diagnosis-related group whose most recent hospitalization had been at least 6 months previously. We estimate that the cost to Medicare of unplanned rehospitalizations in 2004 was $17.4 billion. CONCLUSIONS Rehospitalizations among Medicare beneficiaries are prevalent and costly.
Regorafenib (BAY 73-4506): Antitumor and antimetastatic activities in preclinical models of colorectal cancer

Regorafenib, a novel multikinase inhibitor, has recently demonstrated overall survival benefits in metastatic colorectal cancer (CRC) patients. Our study aimed to gain further insight into the molecular mechanisms of regorafenib and to assess its potential in combination therapy. Regorafenib was tested alone and in combination with irinotecan in patient‐derived (PD) CRC models and a murine CRC liver metastasis model. Mechanism of action was investigated using in vitro functional assays, immunohistochemistry and correlation with CRC‐related oncogenes. Regorafenib demonstrated significant inhibition of growth‐factor‐mediated vascular endothelial growth factor receptor (VEGFR) 2 and VEGFR3 autophosphorylation, and intracellular VEGFR3 signaling in human umbilical vascular endothelial cells (HuVECs) and lymphatic endothelial cells (LECs), and also blocked migration of LECs. Furthermore, regorafenib inhibited proliferation in 19 of 25 human CRC cell lines and markedly slowed tumor growth in five of seven PD xenograft models. Combination of regorafenib with irinotecan significantly delayed tumor growth after extended treatment in four xenograft models. Reduced CD31 staining indicates that the antiangiogenic effects of regorafenib contribute to its antitumor activity. Finally, regorafenib significantly delayed disease progression in a murine CRC liver metastasis model by inhibiting the growth of established liver metastases and preventing the formation of new metastases in other organs. In addition, our results suggest that regorafenib displays antimetastatic activity, which may contribute to its efficacy in patients with metastatic CRC. Combination of regorafenib and irinotecan demonstrated an increased antitumor effect and could provide a future treatment option for CRC patients.
Structural abnormalities in the thalamus of migraineurs with aura: A multiparametric study at 3 T

Background and objectives: The thalamus exerts a pivotal role in pain processing and cortical excitability control, and migraine is characterized by repeated pain attacks and abnormal cortical habituation to excitatory stimuli. This work aimed at studying the microstructure of the thalamus in migraine patients using an innovative multiparametric approach at high‐field magnetic resonance imaging (MRI). Design: We examined 37 migraineurs (22 without aura, MWoA, and 15 with aura, MWA) as well as 20 healthy controls (HC) in a 3‐T MRI equipped with a 32‐channel coil. We acquired whole‐brain T1 relaxation maps and computed magnetization transfer ratio (MTR), generalized fractional anisotropy, and T2* maps to probe microstructural and connectivity integrity and to assess iron deposition. We also correlated the obtained parametric values with the average monthly frequency of migraine attacks and disease duration. Results: T1 relaxation time was significantly shorter in the thalamus of MWA patients compared with MWoA (P < 0.001) and HC (P ≤ 0.01); in addition, MTR was higher and T2* relaxation time was shorter in MWA than in MWoA patients (P < 0.05, respectively). These data reveal broad microstructural alterations in the thalamus of MWA patients compared with MWoA and HC, suggesting increased iron deposition and myelin content/cellularity. However, MWA and MWoA patients did not show any differences in the thalamic nucleus involved in pain processing in migraine. Conclusions: There are broad microstructural alterations in the thalamus of MWA patients that may underlie abnormal cortical excitability control leading to cortical spreading depression and visual aura. Hum Brain Mapp 35:1461–1468, 2014. © 2013 Wiley Periodicals, Inc.
The challenges of student affairs at Kenyan public universities

Kenya is increasingly turning to the promise of mass higher education to help solve a range of economic and social issues. These efforts have had profound effects on university students, faculty and professionals who provide the vital student support services necessary for academic success. This case study explores the challenges that face Kenyan student services professionals within the context of the country’s history and cultures. Kenya’s student service professionals face four major challenges: the increasing costs of attendance, the resulting impact on student behaviours and actions, lack of training and senior leadership, and regular campus closures.
Face recognition system using PCA-ANN technique with feature fusion method

Biometric technology plays a vital role for providing the security which is imperative part in secure system. Human face recognition is a potential method of biometric authentication. This paper presents a process of face recognition system using principle component analysis with Back-propagation neural network where features of face image has been combined by applying face detection and edge detection technique. In this system, the performance has been analyzed based on the proposed feature fusion technique. At first, the fussed feature has been extracted and the dimension of the feature vector has been reduced using Principal Component Analysis method. The reduced vector has been classified by Back-propagation neural network based classifier. In recognition stage, several steps are required. Finally, we analyzed the performance of the system for different size of the train database. The performance analysis shows that the efficiency has been enhanced when the feature extraction operation performed successfully. The performance of the system has been reached more than 92% for the adverse conditions.
Robot assisted gait training with active leg exoskeleton (ALEX)

Gait training of stroke survivors can help in retraining their muscles and improving their gait pattern. Robot assisted gait training (RAGT) was developed for stroke survivors using ALEX and force-field controller, which use assist-as-needed paradigm for rehabilitation. In this paradigm undesirable gait motion is resisted and assistance is provided towards the desirable motion. The force-field controller achieves this paradigm by applying forces at the foot of the subject. Two stroke survivors participated in a 15-day gait training study each with ALEX. The results show that by the end of the training the gait pattern of the patients was improved towards healthy subjects gait pattern. Improvement is seen as increase in the size of the patientspsila gait pattern, increase in knee and ankle joint excursions and increase in their walking speed on the treadmill.