Miracle-dz/UniFAR-FTUs huggingface dataset:
METTEOR: Robust Multi-Traffic Topology Engineering for Commercial Data Center Networks

Numerous optical circuit switched data center networks have been proposed over the past decade for higher capacity, though commercial adoption of these architectures have been minimal so far. One major challenge commonly facing these architectures is the difficulty of handling bursty traffic with optical circuit switches (OCS) with high switching latency. Prior works generally rely on fast-switching OCS prototypes to better react to traffic changes via frequent reconfigurations. This approach, unfortunately, adds further complexity to the control plane. We propose METTEOR, an easily deployable solution for optical circuit switched data centers, that is designed for the current capabilities of commercial OCSs. Using multiple predicted traffic matrices, METTEOR designs data center topologies that are less sensitive to traffic changes, thus eliminating the need of frequently reconfiguring OCSs upon traffic changes. Results based on extensive evaluations using production traces show that METTEOR increases the percentage of direct-hop traffic by about 80% over a fat tree at comparable cost, and by about 30% over a uniform mesh, at comparable maximum link utilizations. Compared to ideal solutions that reconfigure OCSs on every traffic matrix, METTEOR achieves close-to-optimal bandwidth utilization even with biweekly reconfiguration. This drastically lowers the controller and management complexity needed to perform METTEOR in commercial settings.
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders

A common strategy for the dimensionality reduction of nonlinear partial differential equations relies on the use of the proper orthogonal decomposition (POD) to identify a reduced subspace and the Galerkin projection for evolving dynamics in this reduced space. However, advection-dominated PDEs are represented poorly by this methodology since the process of truncation discards important interactions between higher-order modes during time evolution. In this study, we demonstrate that an encoding using convolutional autoencoders (CAEs) followed by a reduced-space time evolution by recurrent neural networks overcomes this limitation effectively. We demonstrate that a truncated system of only two latent-space dimensions can reproduce a sharp advecting shock profile for the viscous Burgers equation with very low viscosities, and a twelve-dimensional latent space can recreate the evolution of the inviscid shallow water equations. Additionally, the proposed framework is extended to a parametric reduced-order model by directly embedding parametric information into the latent space to detect trends in system evolution. Our results show that these advection-dominated systems are more amenable to low-dimensional encoding and time evolution by a CAE and recurrent neural network combination than the POD Galerkin technique.
Anisotropy-mediated reentrant localization

We consider a 2d dipolar system, d=2d=2, with the generalized dipole-dipole interaction \sim r^{-a}∼r−a, and the power aa controlled experimentally in trapped-ion or Rydberg-atom systems via their interaction with cavity modes. We focus on the dilute dipolar excitation case when the problem can be effectively considered as single-particle with the interaction providing long-range dipolar-like hopping. We show that the spatially homogeneous tilt \betaβ of the dipoles giving rise to the anisotropic dipole exchange leads to the non-trivial reentrant localization beyond the locator expansion, a<da<d, unlike the models with random dipole orientation. The Anderson transitions are found to occur at the finite values of the tilt parameter \beta = aβ=a, 0<a<d0<a<d, and \beta = a/(a-d/2)β=a/(a−d/2), d/2<a<dd/2<a<d, showing the robustness of the localization at small and large anisotropy values. Both exact analytical methods and extensive numerical calculations show power-law localized eigenstates in the bulk of the spectrum, obeying recently discovered duality a↔2d-aa↔2d−a of their spatial decay rate, on the localized side of the transition, a>a_{AT}a>aAT. This localization emerges due to the presence of the ergodic extended states at either spectral edge, which constitute a zero fraction of states in the thermodynamic limit, decaying though extremely slowly with the system size.
A hybrid model based on deep LSTM for predicting high-dimensional chaotic systems

We propose a hybrid method combining the deep long short-term memory (LSTM) model with the inexact empirical model of dynamical systems to predict high-dimensional chaotic systems. The deep hierarchy is encoded into the LSTM by superimposing multiple recurrent neural network layers and the hybrid model is trained with the Adam optimization algorithm. The statistical results of the Mackey-Glass system and the Kuramoto-Sivashinsky system are obtained under the criteria of root mean square error (RMSE) and anomaly correlation coefficient (ACC) using the singe-layer LSTM, the multi-layer LSTM, and the corresponding hybrid method, respectively. The numerical results show that the proposed method can effectively avoid the rapid divergence of the multi-layer LSTM model when reconstructing chaotic attractors, and demonstrate the feasibility of the combination of deep learning based on the gradient descent method and the empirical model.
Adaptive Variational Bayesian Inference for Sparse Deep Neural Network

In this work, we focus on variational Bayesian inference on the sparse Deep Neural Network (DNN) modeled under a class of spike-and-slab priors. Given a pre-specified sparse DNN structure, the corresponding variational posterior contraction rate is characterized that reveals a trade-off between the variational error and the approximation error, which are both determined by the network structural complexity (i.e., depth, width and sparsity). However, the optimal network structure, which strikes the balance of the aforementioned trade-off and yields the best rate, is generally unknown in reality. Therefore, our work further develops an {\em adaptive} variational inference procedure that can automatically select a reasonably good (data-dependent) network structure that achieves the best contraction rate, without knowing the optimal network structure. In particular, when the true function is H{\"o}lder smooth, the adaptive variational inference is capable to attain (near-)optimal rate without the knowledge of smoothness level. The above rate still suffers from the curse of dimensionality, and thus motivates the teacher-student setup, i.e., the true function is a sparse DNN model, under which the rate only logarithmically depends on the input dimension.
YOLOff: You Only Learn Offsets for robust 6DoF object pose estimation

Estimating the 3D translation and orientation of an object is a challenging task that can be considered within augmented reality or robotic applications. In this paper, we propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image in cluttered scenes. We adopt an hybrid pipeline in two stages: data-driven and geometric respectively. The first data-driven step consists of a classification CNN to estimate the object 2D location in the image from local patches, followed by a regression CNN trained to predict the 3D location of a set of keypoints in the camera coordinate system. We robustly perform local voting to recover the location of each keypoint in the camera coordinate system. To extract the pose information, the geometric step consists in aligning the 3D points in the camera coordinate system with the corresponding 3D points in world coordinate system by minimizing a registration error, thus computing the pose. Our experiments on the standard dataset LineMod show that our approach more robust and accurate than state-of-the-art methods.
The effect of servant leadership on employees’ self-reported performance: Does public service motivation play a mediating explanatory role?

Article history: Received: October 9, 2019 Received in revised format: November 25 2019 Accepted: December 31, 2019 Available online: January 2, 2020 Previous studies have established that leadership has a decisive role in shaping organizational outcomes in general and performance in particular. However, few efforts have gone into explaining the effect demonstrated by servant leadership on how employees do perceive their performance and whether such a relationship is mediated by public service motivation. This paper addresses servant leadership, public service motivation and employee performance among staff in the context of Jordanian civil servants. Data was gathered from a random sample drawn among 3000 employees of the Jordanian customs department. Structure equation modelling techniques were used to statistically analyze the dataset. This draws scholars’ attention into how servant leadership plays a role affecting civil servants perceived selfreported performance while motivating employees to serve the cause of governmental institutions which in turn enhances an increase on the reported performance levels. This paper produces further evidence to support servant leadership and public service motivation adoption to affect performance in previously neglected area of Jordanian public service within the Jordanian customs department. © 2020 by the authors; licensee Growing Science, Canada
Reducing Amygdala Activity and Phobic Fear through Cognitive Top–Down Regulation

Abstract The amygdala is critically involved in emotional processing, including fear responses, and shows hyperactivity in anxiety disorders. Previous research in healthy participants has indicated that amygdala activity is down-regulated by cognitively demanding tasks that engage the PFC. It is unknown, however, if such an acute down-regulation of amygdala activity might correlate with reduced fear in anxious participants. In an fMRI study of 43 participants (11 men) with fear of snakes, we found reduced amygdala activity when visual stimuli were processed under high cognitive load, irrespective of whether the stimuli were of neutral or phobic content. Furthermore, dynamic causal modeling revealed that this general reduction in amygdala activity was partially mediated by a load-dependent increase in dorsolateral PFC activity. Importantly, high cognitive load also resulted in an acute decrease in perceived phobic fear while viewing the fearful stimuli. In conclusion, our data indicate that a cognitively demanding task results in a top–down regulation of amygdala activity and an acute reduction of fear in phobic participants. These findings may inspire the development of novel psychological intervention approaches aimed at reducing fear in anxiety disorders.
Addressing comprehensive primary healthcare in Gujarat through mHealth intervention: Early implementation experience with TeCHO+ programme

Background: The Health and Family Welfare Department of Gujarat implemented a mHealth Programme called TeCHO+ (Technology for Community Health Operations) in 2018. TeCHO+ is aimed at making progress across all dimensions of the comprehensive primary healthcare services. Objective: The objective of this study is to record the document rollout and early implementation experience of TeCHO+ programme in Gujarat. Methods: The present participatory process documentation exercise was undertaken in Gujarat. The various steps for process documentation were decided after participation in multiple task force meetings and state/district- and taluka-level action seminars and reviewing the TeCHO+ programme data and progress reports. The perceptions of the health officials and field staff were gathered using key informant interviews from five districts of Gujarat: Bharuch, Narmada, Gandhinagar, Mahisagar, and The Dangs. In addition, stakeholders involved with TeCHO+ Programme were interviewed to understand the process of rollout, implementation challenges, and success. Results: In the initial phase, the TeCHO+ application focussed only on updating the Family Health Survey to improve the quality of data captured in the system. FHWs log-in the mobile application daily to access their daily work plan for which SMS alerts were are also generated. Most FHWs were in their 50s and were first-time smartphone users. Although, they were enthusiastic and open to the new technology, initially they faced certain difficulties in operating the application. The programme had a strong troubleshooting mechanism in terms of the use of WhatsApp group, helpline numbers, and voice calls in addition to supportive supervision. Discussion: TeCHO+ showed the potential to enhance the quality of the collected data and also service coverage. However, poor technology literacy of old FHWs, the launch of multiple programme memes at the same time, and target-driven task assignments pose major challenges.
Prevalence and correlates of depression in a rural adult population in Northwest India

Background: Mental disorders are showing a discerning upward trend globally and it is projected that by 2030, depression would be the leading cause of disease burden globally. Depression has not only confined the developed world but is increasingly seen as a major public health problem in the developing world too. Methods: A stratified two-stage design was used to conduct a house-to-house survey in selected villages among consenting eligible members of the family. A three-part questionnaire, with the first part eliciting sociodemographic information was used. Patient Health Questionnaire (PHQ-9), the second part of the questionnaire was used to screen those positive for depression and Beck's Depression Inventory (BDI-II). The third part was used for rating the depression. Data thus collected was analyzed and Chi-square test was used as test of significance. Results: The prevalence of depression was found to be 8.94% (73/816). Beck's Depression Inventory (BDI- II) for rating the depression revealed that mild depression was present in nearly half of the cases (47.95%), while 39.7% and 9.5% of the cases had moderate and severe depression, respectively. Among the various variables, literacy level, marital status, alcohol intake, chronic illness, and socioeconomic status were significantly associated with the depression. Conclusions: Prevalence of depression to the tune of 8.94% in rural adults seems to be a cause of concern for the health planners. Prevention and treatment of depression must be taken on priority and as a challenge by the health professionals.
Augmentation ileocystoplasty: Operative steps

Even in the present times, there remains a role for augmentation cystoplasty, although its use has become less common due to other less invasive procedures such as intravesical injection of botulinum toxin A and neuromodulation. Ileocystoplasty is the most commonly performed bladder augmentation procedure because of the reconstructive urologist's familiarity with ileum and the ileum's ability to easily reach to the pelvis.
Allergic bronchopulmonary aspergillosis: Indian scenario

Aspergillus is ubiquitous, occurring in mycelial form and grows at 15-530C and humid conditions. Pulmonary aspergillosis is a clinical spectrum of lung disease caused by the fungus Aspergillus. ABPA is the commonest disease among allergic bronchopulmonary mycoses. The exact prevalence of ABPA is not known but contemporary estimates suggested that ABPA complicates 1 to 11% of all chronic cases of bronchial asthma. The basic underlying immuno-pathophysiologic process in ABPA is a hypersensitivity reaction to fungus in the bronchial tree. Patients are usually atopic with previous history of bronchial asthma. The onset is insidious with constitutional symptoms like anorexia, fatigue, weight loss, headache, generalized aches and pains, and low-grade fever. It is characterized by repeated episodes of exacerbation with periods of remission, if untreated may progress to fibrotic lung disease. Patients with chronic fibrotic disease may present with cyanosis, corpulmonale and respiratory failure. Radiologically fleeting shadows are characteristic of ABPA. Bronchiectasis, centrilobular nodules and mucoid impaction are main features of ABPA seen in CT scan thorax. Oral corticosteroid remains the cornerstone for the treatment of ABPA. Optimization of baseline asthma therapy is essential. Early diagnosis and proper treatment may alter the prognosis of disease and further prevent end stage lung fibrosis.
An Autopsy of Palliative Health Hazards Associated with the Contemporary Traditional Male Circumcision Rite: The Case of 2016 Alice Study

Aim: This article examined the health hazards associated with the contemporary traditional circumcision rite in Alice, Eastern Cape, South Africa. Methods: The study from which this article was extracted was explorative, descriptive, and employed a qualitative method. The study took use of one-on-one interviews, with the use of interview guide as an instrument to ease focus group discussions and interviews. Data were then analyzed thematically. Results: The findings brought the following factors; hospitalization of initiates; contraction of diseases; amputation of manhood organs; and maltreatment leading to health hazards. Conclusions: This article then recommended that, government, cultural custodians, and parents are requested to work hand in hand to curb all these health hazards associated with the contemporary rite.
Propensity Score Matching with Retrospective and Observational Data in Palliative Care

Indian Journal of Palliative Care ¦ Volume 26 ¦ Issue 1 ¦ January-March 2020 149 Sir, Randomized controlled trial (RCT) is considered gold standard in terms of clinical research as it provides best level of evidence which is required to establish a link between treatment and outcomes. [1] RCT eliminates bias and confounding factors, enrolls patients into the desired group using a predecided inclusion and exclusion criteria, and ensures that the collected data are accurate and statistical analysis is reliable. However, executing an RCT in a setting of palliative care is difficult as it is costly to conduct, is unethical on several occasions, and is time consuming. A possible solution to this is planning a retrospective study. The biggest hurdle in such a situation is the presence of bias. [2] Another issue is that the level of evidence generated by retrospective data will not be good enough, and thus the recommendation made by the results will be difficult to accept and subsequently implement.
Prevalence of low physical activity, its predictors and knowledge regarding being overweight/obesity: A community-based study from urban South India

Introduction: Physical inactivity is the fourth leading risk factor for mortality and morbidity as per the World Health Organisation (WHO). The current study was conducted in the city of Erode, Tamil Nadu, South India, to estimate the prevalence and predictors of low physical activity (LPA) and assess their knowledge with regards to being overweight/obesity. Methods: It was a cross-sectional study conducted over 24 months from July 2015 to June 2017. Multi-stage random sampling was done in 12 randomly chosen corporation wards. All consenting individuals above 18 years of age were included. Data were collected using a semi-structured questionnaire incorporating the validated International Physical Activity Questionnaire (IPAQ). Results: For the study, 489 individuals were screened and 461 were included. Prevalence of LPA was 49.9% (95% confidence interval [CI]:45.34%, 54.46%). The significant predictors (adjusted odds ratio [OR] [95% CI]) of LPA were patient education 1.129 (1.006, 1.2670); unemployment (2.418 [1.610, 3.631]) and knowledge score (5.172 [1.314, 9.423] 27). In the knowledge assessment, 60.3% of the participants scored less than 50%. The significant predictors of poor knowledge were marital status (unmarried) (3.857 [1.341, 11.091]), lower educational status (1.191 [1.009, 1.362]) and low socioeconomic status (SES) (1.050 [1.005, 1.121]). Conclusion: Prevalence of LPA in our population is fairly high and there is gross knowledge inadequacy.
Clinical Value of Susceptibility Weighted Imaging of Brain Metastases

MRI is used for screening, initial diagnosis and follow-up of brain metastases. Multiparametric MRI protocols encompass an array of image sequences to depict key aspects of metastases morphology and biology. Given the recent safety concerns of Gd-administration and the retention of linear Gd-agents in the brain, non-contrast sequences are currently evaluated regarding their diagnostic value for brain imaging studies. Susceptibility weighted imaging has been established as a valuable clinical and research tool that is heavily used in clinical practice and utilized in diverse pathologies ranging from neuroinflammation, neurovascular disease to neurooncology. We review the value of SWI in the field of brain metastases with an emphasis on its role in early diagnosis, determination of the primary tumor entity, treatment monitoring and discuss therapy-associated changes that can affect SWI. We also review recent insights on the role of “isolated SWI signals” and the controversy on the specificity of SWI for the early detection of brain metastases.
Fine Mapping of Lr49 Using 90K SNP Chip Array and Flow-Sorted Chromosome Sequencing in Wheat

Leaf rust, caused by Puccinia triticina, threatens global wheat production due to the constant evolution of virulent pathotypes that defeat commercially deployed all stage-resistance (ASR) genes in modern cultivars. Hence, the deployment of combinations of adult plant resistance (APR) and ASR genes in new wheat cultivars is desirable. Adult plant resistance gene Lr49 was previously mapped on the long arm of chromosome 4B of cultivar VL404 and flanked by microsatellite markers barc163 (8.1 cM) and wmc349 (10.1 cM), neither of which was sufficiently closely linked for efficient marker assisted selection. This study used high-density SNP genotyping and flow sorted chromosome sequencing to fine-map the Lr49 locus as a starting point to develop a diagnostic marker for use in breeding and to clone this gene. Marker sunKASP_21 was mapped 0.4 cM proximal to Lr49, whereas a group of markers including sunKASP_24 were placed 0.6 cM distal to this gene. Testing of the linked markers on 75 Australian and 90 European cultivars with diverse genetic backgrounds showed that sunKASP_21 was most strongly associated with Lr49. Our results also show that the Lr49 genomic region contains structural variation relative to the reference stock Chinese Spring, possibly an inverted genomic duplication, which introduces a new set of challenges for the Lr49 cloning.
Representation of Perceptual Evidence in the Human Brain Assessed by Fast, Within-Trial Dynamic Stimuli

In perceptual decision making the brain extracts and accumulates decision evidence from a stimulus over time and eventually makes a decision based on the accumulated evidence. Several characteristics of this process have been observed in human electrophysiological experiments, especially an average build-up of motor-related signals supposedly reflecting accumulated evidence, when averaged across trials. Another recently established approach to investigate the representation of decision evidence in brain signals is to correlate the within-trial fluctuations of decision evidence with the measured signals. We here report results of this approach for a two-alternative forced choice reaction time experiment measured using magnetoencephalography (MEG) recordings. Our results show: (1) that decision evidence is most strongly represented in the MEG signals in three consecutive phases and (2) that posterior cingulate cortex is involved most consistently, among all brain areas, in all three of the identified phases. As most previous work on perceptual decision making in the brain has focused on parietal and motor areas, our findings therefore suggest that the role of the posterior cingulate cortex in perceptual decision making may be currently underestimated.