Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conformational Switch in Nuclear Hormone Receptors Is Involved in Coupling Hormone Binding to Corepressor Release

Nuclear hormone receptors are ligand-regulated transcription factors that modulate gene expression in response to small, hydrophobic hormones, such as retinoic acid and thyroid hormone. The thyroid hormone and retinoic acid receptors typically repress transcription in the absence of hormone and activate it in the presence of hormone. Transcriptional repression is mediated, in part, through the ability of these receptors to physically associate with ancillary polypeptides called corepressors. We wished to understand the mechanism by which corepressors are recruited to unliganded nuclear hormone receptors and are released on the binding of hormone. We report here that an alpha-helical domain located at the thyroid hormone receptor C terminus appears to undergo a hormone-induced conformational change required for release of corepressor and that amino acid substitutions that abrogate this conformational change can impair or prevent corepressor release. In contrast, retinoid X receptors appear neither to undergo an equivalent conformational alteration in their C termini nor to release corepressor in response to cognate hormone, consistent with the distinct transcriptional regulatory properties displayed by this class of receptors.
CT Prostate Segmentation Based on Continuously Updated Random Forests

It is important to segment prostate automatically in the daily treatment images. However, previous methods often ignore the previous segmented treatment images which contain valuable patient-specific information. To this end, this paper proposes a novel CT prostate segmentation method based on a random forest model which is trained as a classifier to segment prostates. This model can be continuously updated by adding newly segmented prostate shapes into the training pool. In this way, more patient-specific information is incorporated into the training procedure. The experimental results show that the proposed method can improve the accuracy of prostate segmentation efficiently.
Evidence of Genetic Diversity Underlying Rh D−, Weak D (Du), and Partial D Phenotypes as Determined by Multiplex Polymerase Chain Reaction Analysis of the RHD Gene

The human blood group Rh antigens are expressed by proteins encoded by a pair of highly homologous genes located at chromosome 1p34-36. One of the genes ( RHCE ) encodes Rh CcEe antigens, while the other ( RHD ) the D antigen. Point mutations in the RHCE gene generate the C/c and E/e polymorphisms, while it has been shown that an RHD gene deletion can generate the D-negative phenotype. We have analyzed intron 4 of the RHCE and RHD genes and have defined the site of an RHD -specific deletion located in this intron. Using a multiplex RHD typing assay, which combines a reverse polymerase chain reaction (PCR) primer, which straddles this RHD -specific sequence, and a pair of primers located in exon 10 of the RHD gene, we have analyzed 357 different genomic DNA samples derived from individuals expressing D + , D − , weak D, and partial D phenotypes. Of these, we have noted a significant discordance with our multiplex PCR assay in the D − phenotypes dCcee and dccEe (which have been previously described) and weak D phenotypes. Our results suggest that in five serologically D − individuals we have identified an apparently intact RHD gene. Sequence analysis of transcripts obtained from one of these individuals (of phenotype dCCee) illustrates the presence of full-length RHD transcripts, which have a point mutation at nucleotide 121 (C → T), which generates an in-frame stop codon (Gln41Stop). Thus, we describe a different molecular basis for generating the D − phenotype to the complete RHD gene deletion described previously. We also show that there are discordances with serotype and the multiplex assay in weak D and partial D phenotypes, indicating that the underlying molecular basis can be heterogeneous. Existing Rh D PCR assays assume the complete absence of the RHD gene in D − phenotypes. We describe a different molecular basis for generating the D − phenotype to the complete RHD gene deletion described previously.
Semantic Enrichment of Streaming Healthcare Data

In the past decade, the healthcare industry has made significant advances in the digitization of patient information. However, a lack of interoperability among healthcare systems still imposes a high cost to patients, hospitals, and insurers. Currently, most systems pass messages using idiosyncratic messaging standards that require specialized knowledge to interpret. This increases the cost of systems integration and often puts more advanced uses of data out of reach. In this project, we demonstrate how two open standards, FHIR and RDF, can be combined both to integrate data from disparate sources in real time and make that data queryable and susceptible to automated inference. To validate the effectiveness of the semantic engine, we perform simulations of real-time data feeds and demonstrate how they can be combined and used by client-side applications with no knowledge of the underlying sources.
Tidal forces are gravitational waves

In this paper we show in a covariant and gauge invariant way that in general relativity, tidal forces are actually a hidden form of gravitational waves. This must be so because gravitational effects cannot occur faster than the speed of light. Any two body gravitating system, where the bodies are orbiting around each other, may generate negligible gravitational waves, but it is via these waves that non-negligible tidal forces (causing shape distortions) act on these bodies. Although the tidal forces are caused by the electric part of the Weyl tensor, we transparently show that some small time varying magnetic part of the Weyl tensor with non zero curl must be present in the system that mediates the tidal forces via gravitational wave type effects. The outcome is a new test of whether gravitational effects propagate at the speed of light.
Discovery and Separation of Features for Invariant Representation Learning

Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization. We propose a framework for training robust neural networks that induces invariance to nuisances through learning to discover and separate predictive and nuisance factors of data. We present an information theoretic formulation of our approach, from which we derive training objectives and its connections with previous methods. Empirical results on a wide array of datasets show that the proposed framework achieves state-of-the-art performance, without requiring nuisance annotations during training.
Hospital volunteers – are they satisfied?

We examine satisfaction with HRM practices, namely recruitment, training and rewarding in NPO's and attitudes regarding the appropriateness of these practices. The participants in this study are 76 volunteers, affiliated to 4 different NPO's, which work in hospitals and have direct contact with patients and their families. Analysing aggregate results we show that volunteers are more satisfied with training, and consider that the training strategies are very appropriate. After identifying differences between organisations we discover that in some organizations volunteers are satisfied with rewards, but in opposition they have negative attitudes regarding the appropriateness of the recognition strategies and vice-versa an opposite relation between satisfaction with reward and recognition strategies and the process of reward and recognition. We also name the more and less satisfied volunteers.
A Random Matrix Perspective on Mixtures of Nonlinearities in High Dimensions

One of the distinguishing characteristics of modern deep learning systems is that they typically employ neural network architectures that utilize enormous numbers of parameters, often in the millions and sometimes even in the billions. While this paradigm has inspired significant research on the properties of large networks, relatively little work has been devoted to the fact that these networks are often used to model large complex datasets, which may themselves contain millions or even billions of constraints. In this work, we focus on this high-dimensional regime in which both the dataset size and the number of features tend to infinity. We analyze the performance of random feature regression with features $F=f(WX+B)$ for a random weight matrix $W$ and random bias vector $B$, obtaining exact formulae for the asymptotic training and test errors for data generated by a linear teacher model. The role of the bias can be understood as parameterizing a distribution over activation functions, and our analysis directly generalizes to such distributions, even those not expressible with a traditional additive bias. Intriguingly, we find that a mixture of nonlinearities can improve both the training and test errors over the best single nonlinearity, suggesting that mixtures of nonlinearities might be useful for approximate kernel methods or neural network architecture design.
Gate-Shift Networks for Video Action Recognition

Deep 3D CNNs for video action recognition are designed to learn powerful representations in the joint spatio-temporal feature space. In practice however, because of the large number of parameters and computations involved, they may under-perform in the lack of sufficiently large datasets for training them at scale. In this paper we introduce spatial gating in spatial-temporal decomposition of 3D kernels. We implement this concept with Gate-Shift Module (GSM). GSM is lightweight and turns a 2D-CNN into a highly efficient spatio-temporal feature extractor. With GSM plugged in, a 2D-CNN learns to adaptively route features through time and combine them, at almost no additional parameters and computational overhead. We perform an extensive evaluation of the proposed module to study its effectiveness in video action recognition, achieving state-of-the-art results on Something Something-V1 and Diving48 datasets, and obtaining competitive results on EPIC-Kitchens with far less model complexity.
Borrowing From the Future: An Attempt to Address Double Sampling

For model-free reinforcement learning, one of the main difficulty of stochastic Bellman residual minimization is the double sampling problem, i.e., while only one single sample for the next state is available in the model-free setting, two independent samples for the next state are required in order to perform unbiased stochastic gradient descent. We propose new algorithms for addressing this problem based on the idea of borrowing extra randomness from the future. When the transition kernel varies slowly with respect to the state, it is shown that the training trajectory of new algorithms is close to the one of unbiased stochastic gradient descent. Numerical results for policy evaluation in both tabular and neural network settings are provided to confirm the theoretical findings.
How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword decoding error probability. In this paper, we propose to orchestrate network resources for a slicing system to guarantee more reliable bursty URLLC transmission. We re-cut physical resource blocks and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword decoding error probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem, which is challenging to be mitigated due to requirements of future channel information and of tackling a two timescale issue. To address the challenges, we develop a resource optimization algorithm based on a sample average approximate technique and a distributed optimization method with provable performance guarantees.
A Hovey triple arising from two cotorsion pairs.

Assume that $(\mathcal{C}, \mathbb{E}, \mathfrak{s})$ is an extriangulated category satisfying Condition (WIC). Let $(\cal Q, \widetilde{\cal R})$ and $(\widetilde{\cal Q}, \cal R)$ be two hereditary cotorsion pairs satisfying conditions $\widetilde{\cal R} \subseteq \cal R$, $\widetilde{\cal Q}\subseteq \cal Q$ and $\widetilde{\cal Q}\cap \cal R = \cal Q \cap \widetilde{\cal R}$. Then there exists a unique thick class $\cal W$ for which $(\cal Q,\cal W,\cal R)$ is a Hovey triple. As an application, this result generalizes the work by Gillespie in an exact case. Moreover, it highlights new phenomena when it applied to triangulated categories.
Mixture Dense Regression for Object Detection and Human Pose Estimation

Mixture models are well-established learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into relatively homogeneous subsets in a data-driven manner. Not only ill-defined but also well-defined complex problems should benefit from them. To this end, we devise a framework for spatial regression using mixture density networks. We realize the framework for object detection and human pose estimation. For both tasks, a mixture model yields higher accuracy and divides the input space into interpretable modes. For object detection, mixture components focus on object scale, with the distribution of components closely following that of ground truth the object scale. This practically alleviates the need for multi-scale testing, providing a superior speed-accuracy trade-off. For human pose estimation, a mixture model divides the data based on viewpoint and uncertainty -- namely, front and back views, with back view imposing higher uncertainty. We conduct experiments on the MS COCO dataset and do not face any mode collapse.
FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, e.g., healthcare and autonomous driving. Inherently, they are considered to be highly error-tolerant. However, recent studies have shown that hardware faults that impact the parameters of a DNN (e.g., weights) can have drastic impacts on its classification accuracy. In this paper, we perform a comprehensive error resilience analysis of DNNs subjected to hardware faults (e.g., permanent faults) in the weight memory. The outcome of this analysis is leveraged to propose a novel error mitigation technique which squashes the high- intensity faulty activation values to alleviate their impact. We achieve this by replacing the unbounded activation functions with their clipped versions. We also present a method to systematically define the clipping values of the activation functions that result in increased resilience of the networks against faults. We evaluate our technique on the AlexNet and the VGG-16 DNNs trained for the CIFAR-10 dataset. The experimental results show that our mitigation technique significantly improves the resilience of the DNNs to faults. For example, the proposed technique offers on average 68.92% improvement in the classification accuracy of resilience-optimized VGG-16 model at 1 × 10-5 fault rate, when compared to the base network without any fault mitigation.
Trans-Planckian censorship conjecture in holographic cosmology

In this paper, we discuss the implications of the recently proposed trans-Planckian Censorship Conjecture (TCC) for holographic cosmology. For models of holographic inflation that are dual to inverse renormalization group (RG) flows, assuming the TCC to be true, we found a bound on the running of the field theory coupling and the corresponding maximum energy scale above which the RG flow should be modified. We also discuss on the trans-Planckian problem in nongeometrical holographic models.
Morphing and Sampling Network for Dense Point Cloud Completion

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).
Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients with HCC treated by transarterial chemoembolization (TACE). It achieves Accuracy of $0.713 \pm 0.075$, F1 of $0.702 \pm 0.082$ and AUC of $0.710 \pm 0.108$. In addition, the pipeline incorporates uncertainty estimation to select hard cases and most align with the misclassified cases. The proposed pipeline arrives at more informed intra-arterial therapeutic decisions for patients with HCC via improving model accuracy and incorporating uncertainty estimation.
SG-NN: Sparse Generative Neural Networks for Self-Supervised Scene Completion of RGB-D Scans

We present a novel approach that converts partial and noisy RGB-D scans into high-quality 3D scene reconstructions by inferring unobserved scene geometry. Our approach is fully self-supervised and can hence be trained solely on incomplete, real-world scans. To achieve, self-supervision, we remove frames from a given (incomplete) 3D scan in order to make it even more incomplete; self-supervision is then formulated by correlating the two levels of partialness of the same scan while masking out regions that have never been observed. Through generalization across a large training set, we can then predict 3D scene completions even without seeing any 3D scan of entirely complete geometry. Combined with a new 3D sparse generative convolutional neural network architecture, our method is able to predict highly detailed surfaces in a coarse-to-fine hierarchical fashion that outperform existing state-of-the-art methods by a significant margin in terms of reconstruction quality.
Deep Learning for Visual Tracking: A Comprehensive Survey

Visual target tracking is one of the most sought-after yet challenging research topics in computer vision. Given the ill-posed nature of the problem and its popularity in a broad range of real-world scenarios, a number of large-scale benchmark datasets have been established, on which considerable methods have been developed and demonstrated with significant progress in recent years – predominantly by recent deep learning (DL)-based methods. This survey aims to systematically investigate the current DL-based visual tracking methods, benchmark datasets, and evaluation metrics. It also extensively evaluates and analyzes the leading visual tracking methods. First, the fundamental characteristics, primary motivations, and contributions of DL-based methods are summarized from nine key aspects of: network architecture, network exploitation, network training for visual tracking, network objective, network output, exploitation of correlation filter advantages, aerial-view tracking, long-term tracking, and online tracking. Second, popular visual tracking benchmarks and their respective properties are compared, and their evaluation metrics are summarized. Third, the state-of-the-art DL-based methods are comprehensively examined on a set of well-established benchmarks of OTB2013, OTB2015, VOT2018, LaSOT, UAV123, UAVDT, and VisDrone2019. Finally, by conducting critical analyses of these state-of-the-art trackers quantitatively and qualitatively, their pros and cons under various common scenarios are investigated. It may serve as a gentle use guide for practitioners to weigh when and under what conditions to choose which method(s). It also facilitates a discussion on ongoing issues and sheds light on promising research directions.
Learning Driving Decisions by Imitating Drivers' Control Behaviors

Classical autonomous driving systems are modularized as a pipeline of perception, decision, planning, and control. The driving decision plays a central role in processing the observation from the perception as well as directing the execution of downstream planning and control modules. Commonly the decision module is designed to be rule-based and is difficult to learn from data. Recently end-to-end neural control policy has been proposed to replace this pipeline, given its generalization ability. However, it remains challenging to enforce physical or logical constraints on the decision to ensure driving safety and stability. In this work, we propose a hybrid framework for learning a decision module, which is agnostic to the mechanisms of perception, planning, and control modules. By imitating the low-level control behavior, it learns the high-level driving decisions while bypasses the ambiguous annotation of high-level driving decisions. We demonstrate that the simulation agents with a learned decision module can be generalized to various complex driving scenarios where the rule-based approach fails. Furthermore, it can generate driving behaviors that are smoother and safer than end-to-end neural policies.
Latent Semantic Search and Information Extraction Architecture

The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI.
Properties of nowhere dense graph classes related to independent set problem

A set is called r-independent, if every two vertices of it are in distance greater then r. In the r-independent set problem with parameter k, we ask whether in a given graph G there exists an r-independent set of size k. In this work we present an algorithm for this problem, which applied to a graph from any fixed nowhere dense class, works in time bounded by f(k, r)*G, for some function f. We also present alternative algorithm, with running time bounded by g(k, r)*G, working on slightly more general classes of graphs.