snu-aidas/HVMap-dataset huggingface dataset:
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning

In distributed training of deep neural networks, parallel minibatch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker’s local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead. Introduction Consider the distributed training of deep neural networks over multiple workers (Dean et al. 2012), where all workers can access all or partial training data and aim to find a common model that yields the minimum training loss. Such a scenario can be modeled as the following distributed parallel non-convex optimization min x∈Rm f(x) ∆ = 1 N N ∑
An Algorithm to Find Overlapping Community Structure in Networks

Abstract. Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorithm based on the betweenness centrality measure. Like the original algorithm, ours performs hierarchical clustering — partitioning a network into any desired number of clusters — but allows them to overlap. Experiments confirm good performance on randomly generated networks based on a known overlapping community structure, and interesting results have also been obtained on a range of real-world networks.
An Algorithm for Differential File Comparison

The programdiff reports differences between tw o files, expressed as a minimal list of line changes to bring either file into agreement with the other . Diff has been engineered to mak e efficient use of time and space on typical inputs that arise in vetting v ersion-to-version changes in computer-maintained or computer-generated documents. T ime and space usage are observed to v ary about as the sum of the file lengths on real data, although they are known to vary as the product of the file lengths in the worst case. The central algorithm of diff solves the ‘longest common subsequence problem’ to find the lines that do not change between files. Practical ef ficiency is gained by attending only to certain critical ‘candidate’ matches between the files, the breaking of which would shorten the longest subsequence common to some pair of initial segments of the two files. Various techniques of hashing, presorting into equi valence classes, merging by binary search, and dynamic storage allocation are used to obtain good performance. [This document was scanned from Bell Laboratories Computing Science T e hnical Report #41, dated July 1976. Te xt was converted by OCR and hand-corrected (last changed June, 2012). Figures were reconstructed. Some OCR errors may remain, especially in tables and equations. Please report them to doug@cs.dartmouth.edu.] The programdiff creates a list of what lines of one file ha ve to be changed to bring it into agreement with a second file or vice versa. It is based on ideas from se veral sources[1,2,7,8]. As an example of its work, consider the two files, listed horizontally for brevity: a b c d e f g w a b x y z e It is easy to see that the first file can be made into the second by the follo wing prescription, in which an imaginary line 0 is understood at the beginning of each: append after line 0: w, change lines 3 through 4, which were: c d into: x y z, delete lines 6 through 7, which were: f g. Going the other way , the first file can be made from the second this way: delete line 1, which was: w, change lines 4 through 6, which were: x y z into: c d, append after line 7: f g. Delete, change and append are the only operations a vailable to diff . It indicates them by 1-letter
Randomized controlled trial of yoga and exercise in multiple sclerosis.

OBJECTIVE
To determine the effect of yoga and of aerobic exercise on cognitive function, fatigue, mood, and quality of life in multiple sclerosis (MS).


METHODS
Subjects with clinically definite MS and Expanded Disability Status Score less than or equal to 6.0 were randomly assigned to one of three groups lasting 6 months: weekly Iyengar yoga class along with home practice, weekly exercise class using a stationary bicycle along with home exercise, or a waiting-list control group. Outcome assessments performed at baseline and at the end of the 6-month period included a battery of cognitive measures focused on attention, physiologic measures of alertness, Profile of Mood States, State-Trait Anxiety Inventory, Multi-Dimensional Fatigue Inventory (MFI), and Short Form (SF)-36 health-related quality of life.


RESULTS
Sixty-nine subjects were recruited and randomized. Twelve subjects did not finish the 6-month intervention. There were no adverse events related to the intervention. There were no effects from either of the active interventions on either of the primary outcome measures of attention or alertness. Both active interventions produced improvement in secondary measures of fatigue compared to the control group: Energy and Fatigue (Vitality) on the SF-36 and general fatigue on the MFI. There were no clear changes in mood related to yoga or exercise.


CONCLUSION
Subjects with MS participating in either a 6-month yoga class or exercise class showed significant improvement in measures of fatigue compared to a waiting-list control group. There was no relative improvement of cognitive function in either of the intervention groups.
Object Detection using Deep Learning

Autonomous vehicles, surveillance systems, face detection systems lead to the development of accurate object detection system [1]. These systems recognize, classify and localize every object in an image by drawing bounding boxes around the object [2]. These systems use existing classification models as backbone for Object Detection purpose. Object detection is the process of finding instances of real-world objects such as human faces, animals and vehicles etc., in pictures, images or in videos. An Object detection algorithm uses extracted features and learning techniques to recognize the objects in an image. In this paper, various Object Detection techniques have been studied and some of them are implemented. As a part of this paper, three algorithms for object detection in an image were implemented and their results were compared. The algorithms are “Object Detection using Deep Learning Framework by OpenCV”, “Object Detection using Tensorflow” and “Object Detection using Keras models”.
Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices

BACKGROUND
Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells.


METHODS/PRINCIPAL FINDINGS
The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells.


CONCLUSIONS/SIGNIFICANCE
We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for other probes, such as light-gated channels, genetically encoded photoactivatable proteins, photoactivatable fluorescent proteins, and voltage-sensitive dyes.
Early neurologic assessment in preterm-infants: integration of traditional neurologic examination and observation of general movements.

OBJECTIVE
To evaluate the possible additional benefit in terms of prognostic accuracy of an integrated application of a traditional scorable method of neurologic examination and the Prechtl's method of qualitative assessment of general movements (GMs) in a large population of 903 consecutive preterm infants.


STUDY DESIGN
Infants were enrolled from the Intensive Care Unit of the University of Catania. Inclusion criteria were a gestational age below 37 weeks and the absence of genetic disorders. All infants underwent serial ultrasound and at 3 months performed both the GMs assessment and the Hammersmith Infant Neurologic Examination (HINE). Outcome was assessed at 2 years by the Touwen neurologic examination and the Clinical Adaptive Test/Clinical, Linguistic and Auditory Milestone Scale.


RESULTS
The integration of the two methods was shown to be more effective than the single assessments in predicting neurologic outcome. The additional benefit of combining the two approaches was particularly clear for the discrimination between unilateral and bilateral cerebral palsy.


CONCLUSIONS
The integrated use of a scorable neurological examination and Prechtl's assessment of GMs can improve early prediction of neurodevelopmental outcome in preterm infants and should complement other clinical and instrumental exams in follow-up programs.
Automatic inspection of pavement cracking distress

15. Supplementary Notes Project performed in cooperation with the Texas Department of Transportation and the Federal Highway Administration. Project Title: Implementation of Automated Pavement Distress Rating Systems 16. Abstract This paper presents the image-processing algorithm customized for high-speed, real-time inspection of pavement cracking. In the algorithm, a pavement image is divided into grid cells of 8 x 8 pixels, and each cell is classified as a non-crack or crack cell using the grayscale information of the border pixels. Whether a crack cell can be regarded as a basic element (or seed) depends on its contrast to the neighboring cells. A number of crack seeds can be called a crack cluster if they fall on a linear string. A crack cluster corresponds to a dark strip in the original image that may or may not be a section of a real crack. Additional conditions to verify a crack cluster include the requirements in the contrast, width, and length of the strip. If verified crack clusters are oriented in similar directions, they will be joined to become one crack. Because many operations are performed on crack seeds rather than on the original image, crack detection can be executed simultaneously when the frame grabber is forming a new image, which permits a real-time, online pavement survey. The trial test results show good repeatability and accuracy when multiple surveys were conducted in different driving conditions.
Developing pervasive multi-agent systems with nature-inspired coordination

Pervasive computing systems can bemodelled effectively as populations of interacting autonomous components. The key challenge to realizing suchmodels is in getting separatelyspecified and -developed sub-systems to discover and interoperate with each other in an open and extensible way, supported by appropriate middleware services. In this paper, we argue that nature-inspired coordination models offer a promising way of addressing this challenge.We first frame the various dimensions alongwhich nature-inspired coordination models can be defined, and survey themost relevant proposals in the area.We describe the nature-inspired coordination model developed within the SAPERE project as a synthesis of existing approaches, and show how it can effectively support the multifold requirements of modern and emerging pervasive services. We conclude by identifying what we think are the open research challenges in this area, and identify some research directions that we believe are promising. © 2014 Elsevier B.V. All rights reserved.
Pointwise Prediction and Sequence-Based Reranking for Adaptable Part-of-Speech Tagging

This paper proposes an accurate method for partof-speech (POS) tagging that is highly domain-adaptable. The method is based on an assumption that the POS transition tendencies do not depend on domains, and has the following three characteristics: 1) it is trainable from partially annotated data, 2) it uses efficiently trainable pointwise POS taggers to allow for active learning, and 3) is more accurate than the pointwise or sequence-based POS taggers. The proposed method estimates POS tags by stacking pointwise and sequence-based predictors. In the experiments we deal with the joint problem of word segmentation and POS tagging in Japanese. We show that our proposed stacking process improves over pointwise and sequence-based methods (hidden Markov models and conditional random fields) both in the general domain and the target domain. In addition we show the learning curve in a domain adaptation scenario. The result shows that our method outperforms state-of-the-art methods in the same domain as the training data and is better than them in domain adaptation situations as well. Keywords-Active learning; Reranking; Word segmentation; Part-of-speech tagging; Pointwise prediction;
Visual analysis of self-organizing maps

Abstract. In the article, an additional visualization of self-organizing maps (SOM) has been investigated. The main objective of self-organizing maps is data clustering and their graphical presentation. Opportunities of SOM visualization in four systems (NeNet, SOM-Toolbox, Databionic ESOM and Viscovery SOMine) have been investigated. Each system has its additional tools for visualizing SOM. A comparative analysis has been made for two data sets: Fisher’s iris data set and the economic indices of the European Union countries. A new SOM system is also introduced and researched. The system has a specific visualization tool. It is missing in other SOM systems. It helps to see the proportion of neurons, corresponding to the data items, belonging to the different classes, and fallen in the same SOM cell.
The Colletotrichum boninense species complex

UNLABELLED
Although only recently described, Colletotrichum boninense is well established in literature as an anthracnose pathogen or endophyte of a diverse range of host plants worldwide. It is especially prominent on members of Amaryllidaceae, Orchidaceae, Proteaceae and Solanaceae. Reports from literature and preliminary studies using ITS sequence data indicated that C. boninense represents a species complex. A multilocus molecular phylogenetic analysis (ITS, ACT, TUB2, CHS-1, GAPDH, HIS3, CAL) of 86 strains previously identified as C. boninense and other related strains revealed 18 clades. These clades are recognised here as separate species, including C. boninense s. str., C. hippeastri, C. karstii and 12 previously undescribed species, C. annellatum, C. beeveri, C. brassicicola, C. brasiliense, C. colombiense, C. constrictum, C. cymbidiicola, C. dacrycarpi, C. novae-zelandiae, C. oncidii, C. parsonsiae and C. torulosum. Seven of the new species are only known from New Zealand, perhaps reflecting a sampling bias. The new combination C. phyllanthi was made, and C. dracaenae Petch was epitypified and the name replaced with C. petchii. Typical for species of the C. boninense species complex are the conidiogenous cells with rather prominent periclinal thickening that also sometimes extend to form a new conidiogenous locus or annellations as well as conidia that have a prominent basal scar. Many species in the C. boninense complex form teleomorphs in culture.


TAXONOMIC NOVELTIES
New combination - Colletotrichum phyllanthi (H. Surendranath Pai) Damm, P.F. Cannon & Crous. Name replacement - C. petchii Damm, P.F. Cannon & Crous. New species - C. annellatum Damm, P.F. Cannon & Crous, C. beeveri Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. brassicicola Damm, P.F. Cannon & Crous, C. brasiliense Damm, P.F. Cannon, Crous & Massola, C. colombiense Damm, P.F. Cannon, Crous, C. constrictum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. cymbidiicola Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. dacrycarpi Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. novae-zelandiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. oncidii Damm, P.F. Cannon & Crous, C. parsonsiae Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir, C. torulosum Damm, P.F. Cannon, Crous, P.R. Johnst. & B. Weir. Typifications: Epitypifications - C. dracaenae Petch.
Pareto improvement and joint cash management optimisation for banks and cash-in-transit firms

Improving the ATM cash management techniques of banks has already received significant attention in the literature as a separate optimisation problem for banks and the independent firms that supply cash to automated teller machines. This article concentrates instead on a further possibility of cost reduction: optimising the cash management problem as one single problem. Doing so, contractual prices between banks and the cash in transit firms can be in general modified allowing for further cost reduction relative to individual optimisations. In order to show the pertinence of this procedure, we have determined possible Pareto-improvement re-contracting schemes based on a Baumol-type cash demand forecast for a Hungarian commercial bank resulting in substantial cost reduction. © 2016 Elsevier B.V. All rights reserved.
Efficacy and safety of a hyaluronic acid filler in subjects treated for correction of midface volume deficiency: a 24 month study

BACKGROUND
Hyaluronic acid (HA) fillers are an established intervention for correcting facial volume deficiency. Few studies have evaluated treatment outcomes for longer than 6 months. The purpose of this study was to determine the durability of an HA filler in the correction of midface volume deficiency over 24 months, as independently evaluated by physician investigators and subjects.


METHODS
Subjects received treatment with Juvéderm(™) Voluma(™) to the malar area, based on the investigators' determination of baseline severity and aesthetic goals. The treatment was administered in one or two sessions over an initial 4-week period. Supplementary treatment was permissible at week 78, based on protocol-defined criteria. A clinically meaningful response was predefined as at least a one-point improvement on the MidFace Volume Deficit Scale (MFVDS) and on the Global Aesthetic Improvement Scale (GAIS).


RESULTS
Of the 103 subjects enrolled, 84% had moderate or significant volume deficiency at baseline. At the first post-treatment evaluation (week 8), 96% were documented to be MFVDS responders, with 98% and 100% graded as GAIS responders when assessed by the subjects and investigators, respectively. At week 78, 81.7% of subjects were still MFVDS responders, with 73.2% and 78.1% being GAIS responders, respectively. Seventy-two subjects completed the 24-month study, of whom 45 did not receive supplementary Voluma(™) at week 78. Forty-three of the 45 (95.6%) subjects were MFVDS responders, with 82.2% and 91.1% being GAIS responders, respectively. At end of the study, 66/72 subjects were either satisfied or very satisfied with Voluma(™), with 70/72 indicating that they would recommend the product to others. Adverse events were transient and infrequent, with injection site bruising and swelling being the most commonly reported.


CONCLUSION
Voluma(™) is safe and effective in the correction of mild to severe facial volume deficiency, achieving long-term clinically meaningful results. There was a high degree of satisfaction with the treatment outcome over the 24 months of the study.
Robust Mapping and Localization in Indoor Environments Using Sonar Data

In this paper we describe a new technique for the creation of featurebased stochastic maps using standard Polaroid sonar sensors. The fundamental contributions of our proposal are: (1) a perceptual grouping process that permits the robust identification and localization of environmental features, such as straight segments and corners, from the sparse and noisy sonar data; (2) a map joining technique that allows the system to build a sequence of independent limited-size stochastic maps and join them in a globally consistent way; (3) a robust mechanism to determine which features in a stochastic map correspond to the same environment feature, allowing the system to update the stochastic map accordingly, and perform tasks such as revisiting and loop closing. We demonstrate the practicality of this approach by building a geometric map of a medium size, real indoor environment, with several people moving around the robot. Maps built from laser data for the same experiment are provided for comparison. KEY WORDS—map building, local maps, data association, sonar sensors, Hough transform
Kilobot: A low cost robot with scalable operations designed for collective behaviors

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousands of robots; however, for reasons of cost, time, or complexity, they are generally validated in simulation only, or on a group of a few tens of robots. To address this issue, this paper presents Kilobot, an open-source, low cost robot designed to make testing collective algorithms on hundreds or thousands of robots accessible to robotics researchers. To enable the possibility of large Kilobot collectives where the number of robots is an order of magnitude larger than the largest that exist today, each robot is made with only $14 worth of parts and takes 5 min to assemble. Furthermore, the robot design allows a single user to easily operate a large Kilobot collective, such as programming, powering on, and charging all robots, which would be difficult or impossible to do with many existing robotic systems. We demonstrate the capabilities of the Kilobot as a collective robot, by using a small robot test collective to implement four popular swarm behaviors: foraging, formation control, phototaxis, and synchronization. © 2013 Elsevier B.V. All rights reserved.