Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Dynamic Network Embedding

Real-world networks are composed of diverse interacting and evolving entities, while most of existing researches simply characterize them as particular static networks, without consideration of the evolution trend in dynamic networks. Recently, significant progresses in tracking the properties of dynamic networks have been made, which exploit changes of entities and links in the network to devise network embedding techniques. Compared to widely proposed static network embedding methods, dynamic network embedding endeavors to encode nodes as low-dimensional dense representations that effectively preserve the network structures and the temporal dynamics, which is beneficial to multifarious downstream machine learning tasks. In this paper, we conduct a systematical survey on dynamic network embedding. In specific, basic concepts of dynamic network embedding are described, notably, we propose a novel taxonomy of existing dynamic network embedding techniques for the first time, including matrix factorization based, Skip-Gram based, autoencoder based, neural networks based and other embedding methods. Additionally, we carefully summarize the commonly used datasets and a wide variety of subsequent tasks that dynamic network embedding can benefit. Afterwards and primarily, we suggest several challenges that the existing algorithms faced and outline possible directions to facilitate the future research, such as dynamic embedding models, large-scale dynamic networks, heterogeneous dynamic networks, dynamic attributed networks, task-oriented dynamic network embedding and more embedding spaces.
Assumption‐lean inference for generalised linear model parameters

Inference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data‐adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences prone to bias, but also failing to give a pure reflection of the information that is contained in the data. Inspired by developments on assumption‐free inference for so‐called projection parameters, we here propose novel nonparametric definitions of main effect estimands and effect modification estimands. These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified, but have the advantage that they continue to capture respectively the (conditional) association between two variables, or the degree to which two variables interact in their association with outcome, even when these models are misspecified. We achieve an assumption‐lean inference for these estimands on the basis of their efficient influence function under the nonparametric model while invoking flexible data‐adaptive (e.g. machine learning) procedures.
Uncertainty Estimation with Infinitesimal Jackknife, Its Distribution and Mean-Field Approximation

Uncertainty quantification is an important research area in machine learning. Many approaches have been developed to improve the representation of uncertainty in deep models to avoid overconfident predictions. Existing ones such as Bayesian neural networks and ensemble methods require modifications to the training procedures and are computationally costly for both training and inference. Motivated by this, we propose mean-field infinitesimal jackknife (mfIJ) -- a simple, efficient, and general-purpose plug-in estimator for uncertainty estimation. The main idea is to use infinitesimal jackknife, a classical tool from statistics for uncertainty estimation to construct a pseudo-ensemble that can be described with a closed-form Gaussian distribution, without retraining. We then use this Gaussian distribution for uncertainty estimation. While the standard way is to sample models from this distribution and combine each sample's prediction, we develop a mean-field approximation to the inference where Gaussian random variables need to be integrated with the softmax nonlinear functions to generate probabilities for multinomial variables. The approach has many appealing properties: it functions as an ensemble without requiring multiple models, and it enables closed-form approximate inference using only the first and second moments of Gaussians. Empirically, mfIJ performs competitively when compared to state-of-the-art methods, including deep ensembles, temperature scaling, dropout and Bayesian NNs, on important uncertainty tasks. It especially outperforms many methods on out-of-distribution detection.
Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0. 18m on a prediction horizon of 5s.
How to Avoid Being Eaten by a Grue: Structured Exploration Strategies for Textual Worlds

Text-based games are long puzzles or quests, characterized by a sequence of sparse and potentially deceptive rewards. They provide an ideal platform to develop agents that perceive and act upon the world using a combinatorially sized natural language state-action space. Standard Reinforcement Learning agents are poorly equipped to effectively explore such spaces and often struggle to overcome bottlenecks---states that agents are unable to pass through simply because they do not see the right action sequence enough times to be sufficiently reinforced. We introduce Q*BERT, an agent that learns to build a knowledge graph of the world by answering questions, which leads to greater sample efficiency. To overcome bottlenecks, we further introduce MC! Q*BERT an agent that uses an knowledge-graph-based intrinsic motivation to detect bottlenecks and a novel exploration strategy to efficiently learn a chain of policy modules to overcome them. We present an ablation study and results demonstrating how our method outperforms the current state-of-the-art on nine text games, including the popular game, Zork, where, for the first time, a learning agent gets past the bottleneck where the player is eaten by a Grue.
Homogeneous quasimorphisms, $C^0$-topology and Lagrangian intersection

We construct an example of a non-trivial homogeneous quasimorphism on the group of Hamiltonian diffeomorphisms of the $2$- and $4$-dimensional quadric which is continuous with respect to both $C^0$-topology and the Hofer metric. This answers a variant of a question of Entov-Polterovich-Py which is one of the open problems listed in the monograph of McDuff-Salamon. One of the key ideas is to work with quantum cohomology rings with different coefficient fields which might be of independent interest. As another application of this idea, we answer a question of Polterovich-Wu. Some by-products about Lagrangian intersection and the existence of Lagrangian submanifolds that are diffeomorphic but not Hamiltonian isotopic for the $4$-dimensional quadric are also discussed.
An adversarial learning algorithm for mitigating gender bias in face recognition

State-of-the-art face recognition networks implicitly encode gender information while being trained for identity classification. Gender is often viewed as an important face attribute to recognize humans. But, the expression of gender information in deep facial features appears to contribute to gender bias in face recognition, i.e. we find a significant difference in the recognition accuracy of DCNNs on male and female faces. We hypothesize that reducing implicitly encoded gender information will help reduce this gender bias. Therefore, we present a novel approach called `Adversarial Gender De-biasing (AGD)' to reduce the strength of gender information in face recognition features. We accomplish this by introducing a bias reducing classification loss $L_{br}$. We show that AGD significantly reduces bias, while achieving reasonable recognition performance. The results of our approach are presented on two state-of-the-art networks.
Learning Expected Reward for Switched Linear Control Systems: A Non-Asymptotic View

In this work, we show existence of invariant ergodic measure for switched linear dynamical systems (SLDSs) under a norm-stability assumption of system dynamics in some unbounded subset of $\mathbb{R}^{n}$. Consequently, given a stationary Markov control policy, we derive non-asymptotic bounds for learning expected reward (w.r.t the invariant ergodic measure our closed-loop system mixes to) from time-averages using Birkhoff's Ergodic Theorem. The presented results provide a foundation for deriving non-asymptotic analysis for average reward-based optimal control of SLDSs. Finally, we illustrate the presented theoretical results in two case-studies.
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel

Bayesian optimisation (BO) has been widely used for hyperparameter optimisation but its application in neural architecture search (NAS) is limited due to the non-continuous, high-dimensional and graph-like search spaces. Current approaches either rely on encoding schemes, which are not scalable to large architectures and ignore the implicit topological structure of architectures, or use graph neural networks, which require additional hyperparameter tuning and a large amount of observed data, which is particularly expensive to obtain in NAS. We propose a neat BO approach for NAS, which combines the Weisfeiler-Lehman graph kernel with a Gaussian process surrogate to capture the topological structure of architectures, without having to explicitly define a Gaussian process over high-dimensional vector spaces. We also harness the interpretable features learnt via the graph kernel to guide the generation of new architectures. We demonstrate empirically that our surrogate model is scalable to large architectures and highly data-efficient; competing methods require 3 to 20 times more observations to achieve equally good prediction performance as ours. We finally show that our method outperforms existing NAS approaches to achieve state-of-the-art results on NAS datasets.
Spaces of Random Plane Triangulations and the Density of States

Tiling spaces are constructed using a metric in which two tilings of $\mathbb{R}^n$ are close if and only if, after a small translation, they agree on a large ball around the origin. We construct analogous spaces to study random triangulations of the plane. We construct a continuous space which is a foliated space equipped with a transverse measure, and a discrete space which is a transversal of that space. Measures on these triangulations can be constructed as limits of measures on spheres. We consider von Neumann algebras associated with these spaces. Under certain conditions, we show that the von Neumann algebra associated with the discrete space is a hyperfinite type $II_1$ factor. We also show that the density of states of certain operators is well-behaved with respect to the convergence of measures, and in particular can be computed by approximating it on spheres, where eigenvalues can be directly computed. Additionally, we prove a connection between jumps of the integrated density of states and compactly supported eigenfunctions analogous to arXiv:0709.2836.
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey

Mobile network is evolving from a communication-only network towards one with joint communication and radar/radio sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers, and it may go beyond the functions of localization, tracking, and object recognition of traditional radar. In PMNs, JCAS integrates sensing into communications, sharing a majority of system modules and the same transmitted signals. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications, whilst providing non-compromised communications. In this paper, we present a broad picture of the motivation, methodologies, challenges, and research opportunities of realizing PMN, by providing a comprehensive survey for systems and technologies developed mainly in the last ten years. Beginning by reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context and envisage its potential applications. We continue to provide a brief review of three types of JCAS systems, with particular attention to their differences in design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing. Subsequently, we discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under nine topics: performance bounds, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, resolution of sensing ambiguity, pattern analysis, networked sensing under cellular topology, and sensing-assisted communications. We conclude the paper by listing key open research problems for the aforementioned topics and sharing some lessons that we have learned.
Line-of-Sight MIMO for High Capacity Millimeter Wave Backhaul in FDD Systems

Wireless backhaul is considered to be the key part of the future wireless network with dense small cell traffic and high capacity demand. In this paper, we focus on the design of a high spectral efficiency line-of-sight (LoS) multiple-input multiple-output (MIMO) system for millimeter wave backhaul using dual-polarized frequency division duplex (FDD). High spectral efficiency is very challenging to achieve for the system due to various physical impairments such as phase noise (PHN), timing offset (TO) as well as the poor condition number of the LoS MIMO. In this paper, we propose a holistic solution containing TO compensation, PHN estimation, precoder/decorrelator optimization of the LoS MIMO for wireless backhaul, and the interleaving of each part. We show that the proposed solution has a robust performance with end-to-end spectral efficiency of 60 bit/s/Hz for 8 × 8 MIMO.
Cascaded Deep Monocular 3D Human Pose Estimation With Evolutionary Training Data

End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data. This paper proposes a novel data augmentation method that: (1) is scalable for synthesizing massive amount of training data (over 8 million valid 3D human poses with corresponding 2D projections) for training 2D-to-3D networks, (2) can effectively reduce dataset bias. Our method evolves a limited dataset to synthesize unseen 3D human skeletons based on a hierarchical human representation and heuristics inspired by prior knowledge. Extensive experiments show that our approach not only achieves state-of-the-art accuracy on the largest public benchmark, but also generalizes significantly better to unseen and rare poses. Relevant files and tools are available at the project website.
Hereditary Hyperferritinemia-Cataract Syndrome in 3 Generations of a Family in East Tennessee

Hereditary hyperferritinemia and cataracts syndrome (HHCS) without iron overload is a syndrome first identified less than 3 decades ago. While investigators have dissected the gene where several responsible mutations reside, it remains a relatively unknown genetic disorder to clinicians. The result is often an expensive, invasive evaluation for iron overload, followed by a well-intended prescription for a series of phlebotomies that delivers morbidity instead of benefit. We present a father with an elevated ferritin and heterozygosity for H63D HFE mutation whose clinical course followed this path. His treatment rendered him symptomatic from iron deficiency with no reduction in his ferritin. On re-evaluation, a review of his past medical history clarified the cataract surgery noted in his record had occurred at a young age. Furthermore, one of his daughters required cataract surgery as a teenager. With this information, we strongly suspected HHCS. His phlebotomies were discontinued, and within weeks, his iatrogenic iron deficiency resolved and his health returned to normal.
Mismatched light and temperature cues disrupt locomotion and energetics via thyroid-dependent mechanisms

Abstract Animals integrate information from different environmental cues to maintain performance across environmental gradients. Increasing average temperature and variability induced by climate change can lead to mismatches between seasonal cues. We used mosquitofish (Gambusia holbrooki) to test the hypotheses that mismatches between seasonal temperature and light regimes (short days and warm temperature and vice versa) decrease swimming performance, metabolic rates and mitochondrial efficiency and that the responses to light and temperature are mediated by thyroid hormone. We show that day length influenced thermal acclimation of swimming performance through thyroid-dependent mechanisms. Oxygen consumption rates were influenced by acclimation temperature and thyroid hormone. Mitochondrial substrate oxidation rates (state three rates) were modified by the interaction between temperature and day length, and mitochondrial efficiency (P/O ratios) increased with warm acclimation. Using P/O ratios to calibrate metabolic (oxygen consumption) scope showed that oxygen consumption did not predict adenosine triphosphate (ATP) production. Unlike oxygen consumption, ATP production was influenced by day length in a thyroid-dependent manner. Our data indicate that oxygen consumption alone should not be used as a predictor of ATP production. Overall, the effects of thyroid hormone on locomotion and energetics were reversed by mismatches such as warm temperatures on short days. We predict that mid to high latitudes in North America and Asia will be particularly affected by mismatches as a result of high seasonality and predicted warming over the next 50 years.
Repeatability and Reproducibility of ADC Histogram Metrics from the ACRIN 6698 Breast Cancer Therapy Response Trial

Mean tumor apparent diffusion coefficient (ADC) of breast cancer showed excellent repeatability but only moderate predictive power for breast cancer therapy response in the ACRIN 6698 multicenter imaging trial. Previous single-center studies have shown improved predictive performance for alternative ADC histogram metrics related to low ADC dense tumor volume. Using test/retest (TT/RT) 4 b-value diffusion-weighted imaging acquisitions from pretreatment or early-treatment time-points on 71 ACRIN 6698 patients, we evaluated repeatability for ADC histogram metrics to establish confidence intervals and inform predictive models for future therapy response analysis. Histograms were generated using regions of interest (ROIs) defined separately for TT and RT diffusion-weighted imaging. TT/RT repeatability and intra- and inter-reader reproducibility (on a 20-patient subset) were evaluated using wCV and Bland–Altman limits of agreement for histogram percentiles, low-ADC dense tumor volumes, and fractional volumes (normalized to total histogram volume). Pearson correlation was used to reveal connections between metrics and ROI variability across the sample cohort. Low percentiles (15th and 25th) were highly repeatable and reproducible, wCV < 8.1%, comparable to mean ADC values previously reported. Volumetric metrics had higher wCV values in all cases, with fractional volumes somewhat better but at least 3 times higher than percentile wCVs. These metrics appear most sensitive to ADC changes around a threshold of 1.2 μm2/ms. Volumetric results were moderately to strongly correlated with ROI size. In conclusion, Lower histogram percentiles have comparable repeatability to mean ADC, while ADC-thresholded volumetric measures currently have poor repeatability but may benefit from improvements in ROI techniques.
Identification of DEGs and transcription factors involved in H. pylori-associated inflammation and their relevance with gastric cancer

Background Previous studies have indicated that chronic inflammation linked to H. pylori infection is the leading causes for gastric cancer (GC). However, the exact mechanism is not entirely clear until now. Purpose To identify the key molecules and TFs involved in H. pylori infection and to provide new insights into H. pylori-associated carcinogenesis and lay the groundwork for the prevention of GC. Results GO and KEGG analysis revealed that the DEGs of Hp+-NAG were mainly associated with the immune response, chemokine activity, extracellular region and rheumatoid arthritis pathway. The DEGs of Hp+-AG-IM were related to the apical plasma membrane, intestinal cholesterol absorption, transporter activity and fat digestion and absorption pathway. In Hp+-NAG network, the expression of TNF, CXCL8, MMP9, CXCL9, CXCL1, CCL20, CTLA4, CXCL2, C3, SAA1 and FOXP3, JUN had statistical significance between normal and cancer in TCGA database. In Hp+-AG-IM network the expression of APOA4, GCG, CYP3A4, XPNPEP2 and FOXP3, JUN were statistically different in the comparison of normal and cancer in TCGA database. FOXP3 were negatively associated with overall survival, and the association for JUN was positive. Conclusion The current study identified key DEGs and their transcriptional regulatory networks involved in H. pylori-associated NAG, AG-IM and GC and found that patients with higher expressed FOXP3 or lower expressed JUN had shorter overall survival time. Our study provided new directions for inflammation-associated oncogenic transformation involved in H. pylori infection.