Reviewerly/exHarmony huggingface dataset:
Fast and accurate segmentation method of active shape model with Rayleigh mixture model clustering for prostate ultrasound images

The prostate cancer interventions, which need an accurate prostate segmentation, are performed under ultrasound imaging guidance. However, prostate ultrasound segmentation is facing two challenges. The first is the low signal-to-noise ratio and inhomogeneity of the ultrasound image. The second is the non-standardized shape and size of the prostate. For prostate ultrasound image segmentation, this paper proposed an accurate and efficient method of Active shape model (ASM) with Rayleigh mixture model Clustering (ASM-RMMC). Firstly, Rayleigh mixture model (RMM) is adopted for clustering the image regions which present similar speckle distributions. These content-based clustered images are then used to initialize and guide the deformation of an ASM model. The performance of the proposed method is assessed on 30 prostate ultrasound images using four metrics as Mean Average Distance (MAD), Dice Similarity Coefficient (DSC), False Positive Error (FPE) and False Negative Error (FNE). The proposed ASM-RMMC reaches high segmentation accuracy with 95% ± 0.81% for DSC, 1.86 ± 0.02 pixels for MAD, 2.10% ± 0.36% for FPE and 2.78% ± 0.71% for FNE, respectively. Moreover, the average segmentation time is less than 8 s when treating a single prostate ultrasound image through ASM-RMMC. This paper presents a method for prostate ultrasound image segmentation, which achieves high accuracy with less computational complexity and meets the clinical requirements.
Resource Preservability

Resource theory is a general, model-independent approach aiming to understand the qualitative notion of resource quantitatively. In a given resource theory, free operations are physical processes that do not create the resource and are considered zero-cost. This brings the following natural question: For a given free operation, what is its ability to preserve a resource? We axiomatically formulate this ability as the resource preservability, which is constructed as a channel resource theory induced by a state resource theory. We provide two general classes of resource preservability monotones: One is based on state resource monotones, and another is based on channel distance measures. Specifically, the latter gives the robustness monotone, which has been recently found to have an operational interpretation. As examples, we show that athermality preservability of a Gibbs-preserving channel can be related to the smallest bath size needed to thermalize all its outputs, and it also bounds the capacity of a classical communication scenario under certain thermodynamic constraints. We further apply our theory to the study of entanglement preserving local thermalization (EPLT) and provide a new family of EPLT which admits arbitrarily small nonzero entanglement preservability and free entanglement preservation at the same time. Our results give the first systematic and general formulation of the resource preservation character of free operations.
Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes

Automated segmentation of kidney and tumor from 3D CT scans is necessary for the diagnosis, monitoring, and treatment planning of the disease.In this paper, we describe a two-stage framework for kidney and tumor segmentation based on 3D fully convolutional network (FCN).The first stage preliminarily locate the kidney and cut off the irrelevant background to reduce class imbalance and computation cost.Then the second stage precisely segment the kidney and tumor on the cropped patch.The proposed method achieves 98.05% and 83.70% of Dice score on the validation set of MICCAI 2019 KiTS Challenge.
Information capacity of continuous variable measurement channel

The present paper is devoted to investigation of the classical capacity of infinite-dimensional quantum measurement channels. A number of usable conditions are introduced that enable us to apply previously obtained general results to specific models, in particular, to the multi-mode bosonic Gaussian measurement channels. An explicit formula for the classical capacity of the Gaussian measurement channel is obtained in this paper without assuming the global gauge symmetry, solely under certain "threshold condition". The result is illustrated by the capacity computation for one-mode squeezed-noise heterodyne measurement channel.
THE ORGANIZATION OF THE UNIVERSITY EDUCATIONAL PROCESS IN TERMS OF DIGITALIZATION OF EDUCATION

Purpose: The paper deals with the optimization of the preparation of educational professional programs of higher education to the processes of accreditation and licensing.&#x0D; Methodology: In this paper, we will consider technical tools that optimize the organization of the management of the university's educational process in the conditions of the “digitalization” of education.&#x0D; Result: Since the verification of compliance of University educational programs with the requirements of Federal state educational standards is carried out in the process of accreditation examination, it is necessary to optimize the organization of management of the educational process of the University on the basis of computer equipment and software.&#x0D; Applications: This research can be used for universities, teachers, and students.&#x0D; Novelty/Originality: In this research, the model of the organization of the university educational process in terms of digitalization of education is presented in a comprehensive and complete manner.
Active deep Q-learning with demonstration

Recent research has shown that although Reinforcement Learning (RL) can benefit from expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The efforts prevent training decent RL agents with expert demonstration in practice. In this work, we propose Active Reinforcement Learning with Demonstration (ARLD), a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training. Under the framework, we propose Active Deep Q-Network, a novel query strategy which adapts to the dynamically-changing distributions during the RL training process by estimating the uncertainty of recent states. The expert demonstration data within Active DQN are then utilized by optimizing supervised max-margin loss in addition to temporal difference loss within usual DQN training. We propose two methods of estimating the uncertainty based on two state-of-the-art DQN models, namely the divergence of bootstrapped DQN and the variance of noisy DQN. The empirical results validate that both methods not only learn faster than other passive expert demonstration methods with the same amount of demonstration and but also reach super-expert level of performance across four different tasks.
Safety, Challenges, and Performance of Motion Planners in Dynamic Environments

Providing safety guarantees for autonomous vehicle navigation is an ultimate goal for motion planning in dynamic environments. However, due to factors such as robot and obstacle dynamics, e.g., speed and nonlinearity, obstacle motion uncertainties, and a large number of moving obstacles, identifying complete motion planning solutions with collision-free safety guarantees is practically unachievable. Since complete motion planning solutions are intractable, it is critical to explore the factors that impact planning success. One such factor is the planning environment, e.g., obstacle speed, obstacle motion uncertainty, and number of obstacles. In this paper, we explore the impact of the environmental parameters on the performance of a set of thirteen planning algorithms for navigating in dynamic environments. We aim to answer: (1) How do these algorithms perform relative to each other under increasingly more challenging environments? (2) What factors in an environment make planning in dynamic environments challenging? We classify and compare the algorithms in two planning environments with varying types and magnitudes of environmental challenges. Results show that state of the art planning algorithms were unable to consistently identify collision-free paths even in simple geometric planning problems with moving obstacles with stochastic dynamics. Results also demonstrate that given accurate obstacle predictions, planning algorithms that work in state-time space can typically generate real-time solutions in a limited planning horizon with higher success rates than other methods. In addition, in the presence of obstacle motion uncertainty, accepting paths with non-zero collision probability may lead to higher success rates.
Digital Students: Myths and Reality

In connection with the transition of the Russian higher school to the Federal state educational standards FSES HE 3 ++, the process of designing new educational programs for training specialists is currently underway. Designers of educational programs require the knowledge of psychological characteristics of modern students, which actualize new pedagogical approaches and practices in the educational process. The article discusses psychological features characterizing the behavior of the new generation of students in the educational and cognitive spheres and special aspects of pedagogical work with them. This generation is referred to as the “digital generation” (generation Z). The article presents the results of the survey on the “digital behavior” of modern students. The students of Volgograd State Technical University from first year to fourth year (N=495) responded to the survey. The data obtained showed that modern students are active users of digital technologies both in the everyday sphere and in educational and professional fields, but they are not always superior to the older generation (generation X, Y). The widespread and active use of various gadgets and devices leads to a change in the mechanisms of cognitive processes and to the formation of new behavioral trends. It actualizes the need for innovative teaching methods adequate to the new reality.
EHSOD: CAM-Guided End-to-End Hybrid-Supervised Object Detection with Cascade Refinement

Object detectors trained on fully-annotated data currently yield state of the art performance but require expensive manual annotations. On the other hand, weakly-supervised detectors have much lower performance and cannot be used reliably in a realistic setting. In this paper, we study the hybrid-supervised object detection problem, aiming to train a high quality detector with only a limited amount of fully-annotated data and fully exploiting cheap data with image-level labels. State of the art methods typically propose an iterative approach, alternating between generating pseudo-labels and updating a detector. This paradigm requires careful manual hyper-parameter tuning for mining good pseudo labels at each round and is quite time-consuming. To address these issues, we present EHSOD, an end-to-end hybrid-supervised object detection system which can be trained in one shot on both fully and weakly-annotated data. Specifically, based on a two-stage detector, we proposed two modules to fully utilize the information from both kinds of labels: 1) CAM-RPN module aims at finding foreground proposals guided by a class activation heat-map; 2) hybrid-supervised cascade module further refines the bounding-box position and classification with the help of an auxiliary head compatible with image-level data. Extensive experiments demonstrate the effectiveness of the proposed method and it achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data, e.g. 37.5% mAP on COCO. We will release the code and the trained models.
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation

Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously. However, in the context of non-autoregressive translation, the word-level cross-entropy loss cannot model the target-side sequential dependency properly, leading to its weak correlation with the translation quality. As a result, NAT tends to generate influent translations with over-translation and under-translation errors. In this paper, we propose to train NAT to minimize the Bag-of-Ngrams (BoN) difference between the model output and the reference sentence. The bag-of-ngrams training objective is differentiable and can be efficiently calculated, which encourages NAT to capture the target-side sequential dependency and correlates well with the translation quality. We validate our approach on three translation tasks and show that our approach largely outperforms the NAT baseline by about 5.0 BLEU scores on WMT14 En↔De and about 2.5 BLEU scores on WMT16 En↔Ro.
Copy or Rewrite: Hybrid Summarization with Hierarchical Reinforcement Learning

Jointly using the extractive and abstractive summarization methods can combine their complementary advantages, generating both informative and concise summary. Existing methods that adopt an extract-then-abstract strategy have achieved impressive results, yet they suffer from the information loss in the abstraction step because they compress all the selected sentences without distinguish. Especially when the whole sentence is summary-worthy, salient content would be lost by compression. To address this problem, we propose HySum, a hybrid framework for summarization that can flexibly switch between copying sentence and rewriting sentence according to the degree of redundancy. In this way, our approach can effectively combine the advantages of two branches of summarization, juggling informativity and conciseness. Moreover, we based on Hierarchical Reinforcement Learning, propose an end-to-end reinforcing method to bridge together the extraction module and rewriting module, which can enhance the cooperation between them. Automatic evaluation shows that our approach significantly outperforms the state-of-the-arts on the CNN/DailyMail corpus. Human evaluation also demonstrates that our generated summaries are more informative and concise than popular models.
Review on the Reliability of Medical Contents on YouTube

&lt;p class="0abstract"&gt;&lt;strong&gt;Abstract—&lt;/strong&gt; Social media and YouTube, in particular, has become an avenue for quick dissemination of information. Patients now search the YouTube website for information on diseases, treatment options, surgery, and general health information. This paper reviews the different reliability methods, results, conclusions and recommendations of contributions on the medical videos on YouTube. A keyword search was done on different databases such as PubMed, Scopus, Web of Science and Google Scholar to generate articles related to the subject matter. No eligibility criteria were defined because the research is partly systematic. Descriptive statistics were used to present the information obtained from the analysis of the previously published papers in this context. � � The results are as follows: (i). DISCERN, JAMAS and GQS are the most frequent assessment tools used by authors in the determination of the reliability of medical videos on YouTube. (ii). 60% of the independent reviewers that assessed the reliability of the YouTube videos are often two in number. (iii). 65% of the articles concluded that medical videos on YouTube contain misleading information. (iv). User engagements for low and high-quality videos are 58% and 42% respectively. (v). 36.3 % of the total videos were uploaded by trusted sources such as medical and health professionals from recognized or prestigious hospitals, while 63.7% were uploaded by other sources whose affiliations cannot be independently verified. (vi). Out of the total 2675 medical videos assessed, 1589 (59%) are categorized as having useful contents that can influence positively on patient education while 1086 (41%) are categorized as misleading and (vii). Only 35% of the papers strongly recommended that medical videos on YouTube are useful and can be a good source of patient education. Awareness is needed to educate patients on the benefits and dangers of assessing medical videos on YouTube. Videos uploaded by authentic medical personnel or organizations are strongly recommended. &lt;/p&gt;
Statistical and economic analysis of solar radiation and climatic data for the development of solar PV system in Nigeria

The growth in energy demand and global concern about the environment has resulted in the drive towards alternative energy sources and consequently this research concerning solar energy harvesting of radiation received at the earth's surface. Analysis of solar radiation data is an important tool in the accurate designing/sizing of solar Photovoltaic (PV) systems and conducting performance analysis of the system. This paper presents the statistical and economic analysis of solar radiation and climatic data for the development of Solar PV systems in Nigeria. The data for three locations, one from each of the radiation regions in Nigeria were analysed using Minitab 17. The analysis shows that Maiduguri is more viable for solar energy conversion system than the two other locations, and for the same energy demand of 1.1MWh, the peak watt (Wp) of solar PV array required are 619419.27 Wp, 821142.52 Wp and 1219489.32 Wp for Maiduguri, Minna and Port Harcourt respectively. This leads to a difference of $460,984.72 in the total project cost between Maiduguri and Minna for the same energy demand.
Virtual reality and its role in improving student knowledge, self-efficacy, and attitude in the materials testing laboratory

The aim of this work is to evaluate the efficacy of using virtual reality to develop training modules for students who are participating in mechanical engineering laboratory classes. We investigated the feasibility of applying virtual reality and augmented reality technologies in manufacturing and material testing training programs to increase academic achievement, engagement, attitudes and self-efficacy toward engineering and also in fostering more effective use of expensive lab-equipment. We hypothesized that providing students with virtual reality and augmented reality enabled personalized manufacturing labs would increase students’ engagement and learning in this laboratory course. To validate the prototype and test our hypotheses, we conducted pilot testing at Texas A&amp;M University. The total number of students with complete data analyzed for this study was 118. A two-group, randomized control group experiment was conducted. No significant differences were found for pre-existing factors including pretest and demographic factors. Five measures were administered to participants including subject matter measures and survey measures relating to attitudes, self-efficacy, engagement, and background demographics. Results of the pilot test data suggest an overall positive outcome for students participating in the virtual reality activity first (experimental condition) compared to those who performed physical machine activity first (control condition). Students who participated in the virtual reality activity first scored significantly higher on multiple-choice subject matter midtest scores. Analysis of open-ended subject matter responses indicates significantly higher gains for participants in the virtual reality activity compared to the physical activity. Subgroup analysis indicated a different performance pattern for female participants. Specifically, when pretest to midtest gain scores were analyzed, females in the experimental group gained twice as much over males after the virtual reality activity. Action data also revealed that female participants spent more time in the tutorial.
Simple Unsupervised Multi-Object Tracking.

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation costs for developing better and larger labeled datasets. In this work, we remove the need for annotated datasets by proposing an unsupervised re-identification network, thus sidestepping the labeling costs entirely, required for training. Given unlabeled videos, our proposed method (SimpleReID) first generates tracking labels using SORT and trains a ReID network to predict the generated labels using crossentropy loss. We demonstrate that SimpleReID performs substantially better than simpler alternatives, and we recover the full performance of its supervised counterpart consistently across diverse tracking frameworks. The observations are unusual because unsupervised ReID is not expected to excel in crowded scenarios with occlusions, and drastic viewpoint changes. By incorporating our unsupervised SimpleReID with CenterTrack trained on augmented still images, we establish a new state-of-the-art performance on popular datasets like MOT16/17 without using tracking supervision, beating current best (CenterTrack) by 0.2-0.3 MOTA and 4.4-4.8 IDF1 scores. We further provide evidence for limited scope for improvement in IDF1 scores beyond our unsupervised ReID in the studied settings. Our investigation suggests reconsideration towards more sophisticated, supervised, end-to-end trackers by showing promise in simpler unsupervised alternatives.
Twin beam quantum-enhanced correlated interferometry for testing fundamental physics

Quantum metrology deals with improving the resolution of instruments that are otherwise limited by shot noise and it is therefore a promising avenue for enabling scientific breakthroughs. The advantage can be even more striking when quantum enhancement is combined with correlation techniques among several devices. Here, we present and realize a correlation interferometry scheme exploiting bipartite quantum correlated states injected in two independent interferometers. The scheme outperforms classical analogues in detecting a faint signal that may be correlated/uncorrelated between the two devices. We also compare its sensitivity with that obtained for a pair of two independent squeezed modes, each addressed to one interferometer, for detecting a correlated stochastic signal in the MHz frequency band. Being the simpler solution, it may eventually find application to fundamental physics tests, e.g., searching for the effects predicted by some Planck scale theories.
Visual Grounding of Learned Physical Models

Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models. In this work, we present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. The visual prior predicts a particle-based representation of the system from visual observations. An inference module operates on those particles, predicting and refining estimates of particle locations, object states, and physical parameters, subject to the constraints imposed by the dynamics prior, which we refer to as visual grounding. We demonstrate the effectiveness of our method in environments involving rigid objects, deformable materials, and fluids. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.