Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Autologous Platelet-Rich Plasma Treatment on Refractory Thin Endometrium During the Frozen Embryo Transfer Cycle: A Pilot Study

Objective: Thin or damaged endometrium remains to be an unsolved problem in the treatment of patients with infertility. The empirical preference for endometrial thickness (EMT) among clinicians is >7 mm, and the refractory thin endometrium, which doesn't respond to standard medical therapies, can be the etiology of recurrent implantation failure (RIF). Autologous platelet-rich plasma (PRP) is known to help tissue regeneration and is widely used in various fields. In the present study, we conducted PRP treatment and investigated its effect on the refractory thin endometrium. Design: Prospective interventional study (https://cris.nih.go.kr/cris, clinical trial registration number: KCT0003375). Methods: Women who had a history of two or more failed IVF cycles and refractory thin endometrium were enrolled in this study. The main inclusion criteria were EMT of <7 mm after more than 2 cycles of previous medical therapy for increasing the EMT. Twenty-four women were enrolled in this study. The subjects were treated with intrauterine infusion of autologous PRP 2 or 3 times from menstrual cycle day 10 of their frozen-thawed embryo transfer (FET) cycle, and ET was performed 3 days after the final autologous PRP infusion. 22 patients underwent FET, and 2 patients were lost to follow up. Results: The ongoing pregnancy rate and LBR were both 20%. The implantation and clinical pregnancy rates were 12.7 and 30%, respectively, and the difference was statistically significant. The average increase in the EMT was 0.6 mm compared with the EMT of their previous cycle. However, this difference was not statistically significant. Further, EMT of 12 patients increased (mean difference: 1.3 mm), while that of seven patients decreased (mean difference: 0.7 mm); the EMT of one patient did not change. There were no adverse effects reported by the patients who were treated with autologous PRP. Conclusions: The use of autologous PRP improved the implantation, pregnancy, and live birth rates (LBR) of the patients with refractory thin endometrium. We assume that the ability of autologous PRP to restore the endometrial receptivity of damaged endometrium has some aspects other than increasing the EMT. The molecular basis of the treatment needs to be revealed in future studies.
How Do Legal Experts Cope With Medical Reports and Forensic Evidence? The Experiences, Perceptions, and Narratives of Swiss Judges and Other Legal Experts

Expert scientific knowledge, including medical knowledge, is relevant for the legal profession and can strongly influence rulings and sentencing in criminal law, civil law, and insurance law. The way in which this medical evidence is understood and evaluated thus has an impact both on individuals and on society as a whole. It enters legal procedures in various forms, for example, as expert witness statements and/or as a legal expert's own acquired medical knowledge. On the other hand, a legal expert may be confronted with expert medical opinions that differ in quality or content and thus have to decide which ones to follow. The aim of this qualitative study was to investigate legal experts' perceptions, experiences, and narratives regarding medical knowledge, particularly the skills and general knowledge used in their branch of the legal profession. A total of 51 semi-structured interviews with judges and prosecutors from different courts of law and from the public prosecutor's office in six different German-speaking (Zurich, Luzern, Aagrau, Obwalden, Nidwalden, Zug) and German/French-speaking (Bern) cantons of Switzerland were conducted, coded, and analyzed using Nvivo. We used a comparison thematic approach identifying common and new themes related to the research aims. Our findings suggest that Swiss judges and prosecutors believe that possessing and developing the skills and terminology required for processing medical information is important but complex, and time-consuming for their work. Additionally, several legal experts reported that their understanding of medical evidence was limited or even non-existent. Moreover, the acquisition of skills related to the assessment of medical reports and forensic evidence appeared to be unstructured. Participants reported having no formal instruction in how to evaluate or deal with medical knowledge. The sources they used to answer questions arising appeared to be in part problematic and non-standardized (internet, newspapers, etc.). Medical literature from peer-reviewed journals was used only rarely. The findings from this study suggest that law departments might wish to evaluate whether their graduates are sufficiently equipped with scientific literacy skills and appropriate skills to evaluate medical information for their later careers. At the same time, medical knowledge pertinent to forensics published in local legal journals may be more effective in reaching the legal expert audience than in medical journals.
Porcine Breed, Sex, and Production Stage Influence the Levels of Health Status Biomarkers in Saliva Samples

In this study, the influence of several factors such as breed, sex, and production stage over the normal range values of salivary biomarkers of health status was evaluated in pigs. A total of 409 pigs of 2 different breeds (conventional Large White × Duroc and Iberian pigs) were included in the study. Animals were divided into different groups according to their sex (male or female) and the stage of the production cycle they were in (post-weaning, nursery, fattening, and finishing). The levels of an inflammatory marker, adenosine deaminase (ADA), and two acute phase proteins, C-reactive protein (CRP) and haptoglobin (Hp) were measured in saliva samples. Moreover, the total antioxidant capacity level (TAC) was quantified for the first time in porcine saliva; therefore, an analytical validation and stability analysis during storage at −80°C were also performed. Differences according to breed were observed for all the markers studied; thus, the influence of age and sex on the normal range values were studied separately for conventional and Iberian pigs. In Large White × Duroc pigs the overall median values of ADA, CRP, Hp and TAC were 282 U/L, 10.49 ng/mL, 0.88 μg/mL, and 21.73 μM Trolox equivalents, respectively. However, higher values of inflammatory marker and acute phase proteins were observed in males at the initial stages of the production cycle, while females presented higher values when they had reached sexual maturity. In Iberian pigs the overall median values observed were 585 U/L, 4.81 ng/mL, 0.63 μg/mL, and 21.21 μM Trolox equivalents for ADA, CRP, Hp, and TAC respectively with slight differences in the influence of the studied factors. Sex differences were not observed in the levels of acute phase proteins in Iberian pigs, probably due to the castration of males during the first days of life; however, ADA levels were found to be higher in male pigs at the end of the production cycle. It could be concluded that breed, sex, and production stage influence the range values of salivary markers of health status in pigs and should be taken into account to further establish reference intervals.
Handcrafted and Deep Trackers

In recent years, visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real-world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance, and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of different trackers based on the feature extraction methods. The first part of this work includes a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classified into various types based on the architecture and the tracking mechanism. In the second part of this work, we experimentally evaluated 24 recent trackers for robustness and compared handcrafted and deep feature based trackers. We observe that trackers using deep features performed better, though in some cases a fusion of both increased performance significantly. To overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms. We analyze the performance of trackers over 11 different challenges in OTTC and 3 other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform better than the others. Our study also reveals that inclusion of different types of regularizations over DCF often results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in the visual object tracking field.
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps

Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions. However, saliency maps are often visually noisy. Although several hypotheses were proposed to account for this phenomenon, there are few works that provide rigorous analyses of noisy saliency maps. In this paper, we first propose a new hypothesis that noise may occur in saliency maps when irrelevant features pass through ReLU activation functions. Then, we propose Rectified Gradient, a method that alleviates this problem through layer-wise thresholding during backpropagation. Experiments with neural networks trained on CIFAR-10 and ImageNet showed effectiveness of our method and its superiority to other attribution methods.
DeeperLab: Single-Shot Image Parser

We present a single-shot, bottom-up approach for whole image parsing. Whole image parsing, also known as Panoptic Segmentation, generalizes the tasks of semantic segmentation for 'stuff' classes and instance segmentation for 'thing' classes, assigning both semantic and instance labels to every pixel in an image. Recent approaches to whole image parsing typically employ separate standalone modules for the constituent semantic and instance segmentation tasks and require multiple passes of inference. Instead, the proposed DeeperLab image parser performs whole image parsing with a significantly simpler, fully convolutional approach that jointly addresses the semantic and instance segmentation tasks in a single-shot manner, resulting in a streamlined system that better lends itself to fast processing. For quantitative evaluation, we use both the instance-based Panoptic Quality (PQ) metric and the proposed region-based Parsing Covering (PC) metric, which better captures the image parsing quality on 'stuff' classes and larger object instances. We report experimental results on the challenging Mapillary Vistas dataset, in which our single model achieves 31.95% (val) / 31.6% PQ (test) and 55.26% PC (val) with 3 frames per second (fps) on GPU or near real-time speed (22.6 fps on GPU) with reduced accuracy.
Quantifying the Loss of Information from Binning List-Mode Data

List-mode data are increasingly being used in single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of information. In this work, we show that this is true for Fisher information and provide a computational method for quantifying the information loss. In the end, we find that the information loss depends on three factors. The first factor is related to the smoothness of the mean data function for the list-mode data. The second factor is the actual object being imaged. Finally, the third factor is the binning scheme in relation to the other two factors.
On Reinforcement Learning Using Monte Carlo Tree Search with Supervised Learning: Non-Asymptotic Analysis

Inspired by the success of AlphaGo Zero (AGZ) which utilizes Monte Carlo Tree Search (MCTS) with Supervised Learning via Neural Network to learn the optimal policy and value function, we focus on establishing formally that such an approach indeed finds the optimal solution asymptotically, as well as establishing non-asymptotic guarantees. We shall focus on infinite-horizon discounted Markov Decision Process (MDP) to establish the results. To start with, this requires establishing that for any given query state, MCTS provides an approximate value function for the state with enough simulation steps of MDP. This property of MCTS was claimed in the literature, but the proof in the seminal works is incomplete. As an important contribution of this work, we establish the correctness of MCTS with appropriate polynomial bonus term in UCB. In the process, we establish polynomial concentration properties of regret for non-stationary Multi-Arm Bandits (MAB), which might be of interest in its own right. Interestingly enough, AGZ utilizes a polynomial form of MCTS as suggested by our result. Using the above result, we argue that MCTS, combined with expressive enough supervised learning techniques, finds the optimal value at nearly minimax optimal rate. Specifically, when using the nearest neighbor supervised learning, we show that MCTS acts as a "policy improvement" operator: it has a natural "bootstrapping" property to improve value function approximation for all states, due to combining with supervised learning, despite evaluating at only finitely many states. To learn an $\epsilon$ approximation of the value function with respect to $\ell_\infty$ norm, MCTS combined with nearest neighbor requires a sample size scaling as $\tilde{O}(\epsilon^{-(d+4)})$, where $d$ is the dimension of the state space. This is nearly optimal due to a minimax lower bound of $\tilde{\Omega} (\epsilon^{-(d+2)}). $
Redundant Loads: A Software Inefficiency Indicator

Modern software packages have become increasingly complex with millions of lines of code and references to many external libraries. Redundant operations are a common performance limiter in these code bases. Missed compiler optimization opportunities, inappropriate data structure and algorithm choices, and developers' inattention to performance are some common reasons for the existence of redundant operations. Developers mainly depend on compilers to eliminate redundant operations. However, compilers' static analysis often misses optimization opportunities due to ambiguities and limited analysis scope; automatic optimizations to algorithmic and data structural problems are out of scope. We develop LoadSpy, a whole-program profiler to pinpoint redundant memory load operations, which are often a symptom of many redundant operations. The strength of LoadSpy exists in identifying and quantifying redundant load operations in programs and associating the redundancies with program execution contexts and scopes to focus developers' attention on problematic code. LoadSpy works on fully optimized binaries, adopts various optimization techniques to reduce its overhead, and provides a rich graphic user interface, which make it a complete developer tool. Applying LoadSpy showed that a large fraction of redundant loads is common in modern software packages despite highest levels of automatic compiler optimizations. Guided by LoadSpy, we optimize several well-known benchmarks and real-world applications, yielding significant speedups.
Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing

Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models still lack the compromise between flexibility and low-dimensional representation that is necessary to properly explore the fact that spectral variability is often confined to a low-dimensional manifold in real scenes. In this article we propose to learn a spectral variability model directly from the observed data, instead of imposing it a priori. This is achieved through a deep generative EM model, which is estimated using a variational autoencoder (VAE). The encoder and decoder that compose the generative model are trained using pure pixel information extracted directly from the observed image, what allows for an unsupervised formulation. The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model. Simulations using both synthetic and real data indicate that the proposed strategy can outperform the competing state-of-the-art algorithms.
Spectre is here to stay: An analysis of side-channels and speculative execution

The recent discovery of the Spectre and Meltdown attacks represents a watershed moment not just for the field of Computer Security, but also of Programming Languages. This paper explores speculative side-channel attacks and their implications for programming languages. These attacks leak information through micro-architectural side-channels which we show are not mere bugs, but in fact lie at the foundation of optimization. We identify three open problems, (1) finding side-channels, (2) understanding speculative vulnerabilities, and (3) mitigating them. For (1) we introduce a mathematical meta-model that clarifies the source of side-channels in simulations and CPUs. For (2) we introduce an architectural model with speculative semantics to study recently-discovered vulnerabilities. For (3) we explore and evaluate software mitigations and prove one correct for this model. Our analysis is informed by extensive offensive research and defensive implementation work for V8, the production JavaScript virtual machine in Chrome. Straightforward extensions to model real hardware suggest these vulnerabilities present formidable challenges for effective, efficient mitigation. As a result of our work, we now believe that speculative vulnerabilities on today's hardware defeat all language-enforced confidentiality with no known comprehensive software mitigations, as we have discovered that untrusted code can construct a universal read gadget to read all memory in the same address space through side-channels. In the face of this reality, we have shifted the security model of the Chrome web browser and V8 to process isolation.
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters

Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle interaction scenarios (called driving encounters) into an interpretable representation from which new driving encounter scenarios are generated by sampling. The MTG consists of a bi-directional encoder and a multi-branch decoder. A new disentanglement metric is then developed for model analyses and comparisons in terms of model robustness and the independence of the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN demonstrates that the MTG has stronger capability to purposely generate rational vehicle-to-vehicle encounters through operating the disentangled latent codes. Thus the MTG could provide more data for engineers and researchers to develop testing and evaluation scenarios for autonomous vehicles.
Stochastic Gradient Descent Escapes Saddle Points Efficiently

This paper considers the perturbed stochastic gradient descent algorithm and shows that it finds $\epsilon$-second order stationary points ($\left\|\nabla f(x)\right\|\leq \epsilon$ and $\nabla^2 f(x) \succeq -\sqrt{\epsilon} \mathbf{I}$) in $\tilde{O}(d/\epsilon^4)$ iterations, giving the first result that has linear dependence on dimension for this setting. For the special case, where stochastic gradients are Lipschitz, the dependence on dimension reduces to polylogarithmic. In addition to giving new results, this paper also presents a simplified proof strategy that gives a shorter and more elegant proof of previously known results (Jin et al. 2017) on perturbed gradient descent algorithm.
Flash: efficient dynamic routing for offchain networks

Offchain networks emerge as a promising solution to address the scalability challenge of blockchain. Participants make payments through offchain networks instead of committing transactions on-chain. Routing is critical to the performance of offchain networks. Existing solutions use either static routing with poor performance or dynamic routing with high overhead to obtain the dynamic channel balance information. In this paper, we propose Flash, a new dynamic routing solution that leverages the unique transactions characteristics in offchain networks to strike a better tradeoff between path optimality and probing overhead. By studying the traces of real offchain networks, we find that the payment sizes are heavy-tailed, and most payments are highly recurrent. Flash thus differentiates the treatment of elephant payments from that of mice payments. It uses a modified max-flow algorithm for elephant payments to find paths with sufficient capacity, and strategically routes the payment across paths to minimize the transaction fees. Mice payments are sent directly by looking up a routing table with a few precomputed paths to reduce probing overhead. Testbed experiments and trace-driven simulations show that Flash improves the success volume of payments by up to 2.3x compared to the state-of-the-art routing algorithm.
Performance Analysis of Series Elastic Actuator Based on Maximum Torque Transmissibility

Series elastic actuators (SEAs) have had sound contributions in the robotics field, and significant breakthroughs have been achieved. Despite this fact, there are still drawbacks to their performance. The elastic spring, which provides compliance, limits the SEA’s performance by lowering the bandwidth of force/torque generation, and this bandwidth decreases even further when the SEA has to render large torques. This problem originates from the constraints in motor and motor driver, such as torque and velocity limits. In this brief, the mathematical tools to analyze how these limitations influence the force control performance of SEA are proposed. To this end, a novel criterion called maximum torque transmissibility (MTT) is defined to assess the ability of SEA by fully utilizing the SEA dynamics, a maximum continuous torque, and permissible velocity of the servo motor. Further, based on MTT, a new concept called the maximum torque bandwidth that can indicate the frequency limit in which SEA can generate the maximum torque is proposed. Various design parameters, including the load condition and mechanical and controller design parameters of SEA, are evaluated. Experimental results verify that MTT can indicate the limitation of the performance of SEA precisely.