Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Differential Identifiability Improves Connectome Predictive Modeling of Cognitive Deficits in Alzheimer\'s Disease

Functional connectivity, as estimated using resting state fMRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease, we identify and characterize functional networks associated to specific cognitive deficits exhibited in Alzheimer's disease. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
Mini-magnetospheres and Moon-magnetosphere interactions: Overview Moon-magnetosphere Interactions

"Moon-magnetosphere interaction" stands for the interaction of magnetospheric plasma with an orbiting moon. Observations and modeling of moon-magnetosphere interaction is a highly interesting area of space physics because it helps to better understand the basic physics of plasma flows in the universe and it provides geophysical information about the interior of the moons. Moon-magnetosphere interaction is caused by the flow of magnetospheric plasma relative to the orbital motions of the moons. The relative velocity is usually slower than the Alfven velocity of the plasma around the moons. Thus the interaction generally forms Alfven wings instead of bow shocks in front of the moons. The local interaction, i.e., the interaction within several moon radii, is controlled by properties of the atmospheres, ionospheres, surfaces, nearby dust-populations, the interiors of the moons as well as the properties of the magnetospheric plasma around the moons. The far-field interaction, i.e., the interaction further away than a few moon radii, is dominated by the magnetospheric plasma and the fields, but it still carries information about the properties of the moons. In this chapter we review the basic physics of moon-magnetosphere interaction. We also give a short tour through the solar system highlighting the important findings at the major moons.
Critical Casimir interaction between colloidal Janus-type particles in two spatial dimensions

We study colloidal particles with chemically inhomogeneous surfaces suspended in a critical binary liquid mixture. The inhomogeneous particle surface is composed of patches with alternating adsorption preferences for the two components of the binary solvent. By describing the binary liquid mixture at its consolute point in terms of the critical Ising model we exploit its conformal invariance in two spatial dimension. This allows us to determine exactly the universal profiles of the order parameter, the energy density, and the stress tensor as well as some of their correlation functions around a single particle for various shapes and configurations of the surface patches. The formalism encompasses several interesting configurations, including Janus particles of circular and needle shapes with dipolar symmetry and a circular particle with quadrupolar symmetry. From these single-particle properties we construct the so-called small particle operator expansion (SPOE), which enables us to obtain asymptotically exact expressions for the position- and orientation-dependent critical Casimir interactions of the particles with distant objects, such as another particle or the confining walls of a half plane, strip, or wedge, with various boundary conditions for the order parameter. In several cases we compare the interactions at large distances with the ones at close distance (but still large on the molecular scale). We also compare our analytical results for two Janus particles with recent simulation data.
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0,+∞) in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until the count values of sub-regions are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and-Conquer Network (S-DCNet). S-DCNet learns to classify closed-set counts and can generalize to open-set counts via S-DC. S-DCNet is also efﬁcient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTechPart B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github.com/xhp-hust-2018-2011/S-DCNet.
Majorana fermions and the Sensitivity Conjecture

Recently, Hao Huang proved the Sensitivity Conjecture, an important result about complexity measures of Boolean functions. We will discuss how this simple and elegant proof turns out to be closely related to physics concepts of the Jordan-Wigner transformation and Majorana fermions. This note is not intended to contain original results. Instead, it is a translation of the math literature in a language that is more familiar to physicists, which helps our understanding and hopefully may inspire future works along this direction.
Global and Semi-global Regulated State Synchronization for Homogeneous Networks of Non-introspective Agents in Presence of Input Saturation- A Scale-free Protocol Design

This paper studies global and semi-global regulated state synchronization of homogeneous networks of non-introspective agents in presence of input saturation based on additional information exchange where the reference trajectory is given by a so-called exosystem which is assumed to be globally reachable. Our protocol design methodology does not need any knowledge of the directed network topology and the spectrum of the associated Laplacian matrix. Moreover, the proposed protocol is scalable and achieves synchronization for any arbitrary number of agents.
Homogeneous Analysis of Hot Earths: Masses, Sizes, and Compositions

Terrestrial planets have been found orbiting Sun-like stars with extremely short periods—some as short as 4 hr. These “ultra-short-period planets” or “hot Earths” are so strongly irradiated that any initial H/He atmosphere has probably been lost to photoevaporation. As such, the sample of hot Earths may give us a glimpse at the rocky cores that are often enshrouded by thick H/He envelopes on wider-orbiting planets. However, the mass and radius measurements of hot Earths have been derived from a hodgepodge of different modeling approaches, and include several cases of contradictory results. Here, we perform a homogeneous analysis of the complete sample of 11 known hot Earths with an insolation exceeding 650 times that of the Earth. We combine all available data for each planet, incorporate parallax information from Gaia to improve the stellar and planetary parameters, and use Gaussian process regression to account for correlated noise in the radial-velocity data. The homogeneous analysis leads to a smaller dispersion in the apparent composition of hot Earths, although there does still appear to be some intrinsic dispersion. Most of the planets are consistent with an Earth-like composition (35% iron and 65% rock), but two planets (K2-141b and K2-229b) show evidence for a higher iron fraction, and one planet (55 Cnc e) has either a very low iron fraction or an envelope of low-density volatiles. All of the planets are less massive than 8 M⊕, despite the selection bias toward more massive planets, suggesting that 8 M⊕ is the critical mass for runaway accretion.
Beta-Binomial stick-breaking non-parametric prior

A new class of nonparametric prior distributions, termed Beta-Binomial stick-breaking process, is proposed. By allowing the underlying length random variables to be dependent through a Beta marginals Markov chain, an appealing discrete random probability measure arises. The chain's dependence parameter controls the ordering of the stick-breaking weights, and thus tunes the model's label-switching ability. Also, by tuning this parameter, the resulting class contains the Dirichlet process and the Geometric process priors as particular cases, which is of interest for fast convergence of MCMC implementations. Some properties of the model are discussed and a density estimation algorithm is proposed and tested with simulated datasets.
Accelerated learning from recommender systems using multi-armed bandit

Recommendation systems are a vital component of many online marketplaces, where there are often millions of items to potentially present to users who have a wide variety of wants or needs. Evaluating recommender system algorithms is a hard task, given all the inherent bias in the data, and successful companies must be able to rapidly iterate on their solution to maintain their competitive advantage. The gold standard for evaluating recommendation algorithms has been the A/B test since it is an unbiased way to estimate how well one or more algorithms compare in the real world. However, there are a number of issues with A/B testing that make it impractical to be the sole method of testing, including long lead time, and high cost of exploration. We argue that multi armed bandit (MAB) testing as a solution to these issues. We showcase how we implemented a MAB solution as an extra step between offline and online A/B testing in a production system. We present the result of our experiment and compare all the offline, MAB, and online A/B tests metrics for our use case.
Latent User Linking for Collaborative Cross Domain Recommendation

With the widespread adoption of information systems, recommender systems are widely used for better user experience. Collaborative filtering is a popular approach in implementing recommender systems. Yet, collaborative filtering methods are highly dependent on user feedback, which is often highly sparse and hard to obtain. However, such issues could be alleviated if knowledge from a much denser and a related secondary domain could be used to enhance the recommendation accuracy in the sparse target domain. In this publication, we propose a deep learning method for cross-domain recommender systems through the linking of cross-domain user latent representations as a form of knowledge transfer across domains. We assume that cross-domain similarities of user tastes and behaviors are clearly observable in the low dimensional user latent representations. These user similarities are used to link the domains. As a result, we propose a Variational Autoencoder based network model for cross-domain linking with added contextualization to handle sparse data and for better transfer of cross-domain knowledge. We further extend the model to be more suitable in cold start scenarios and to utilize auxiliary user information for additional gains in recommendation accuracy. The effectiveness of the proposed model was empirically evaluated using multiple datasets. The experiments proved that the proposed model outperforms the state of the art techniques.
UDS–DFKI Submission to the WMT2019 Czech–Polish Similar Language Translation Shared Task

In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019. The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish. Participants could choose to participate in any of these three tracks and submit system outputs in any translation direction. We report the results obtained by our system in translating from Czech to Polish and comment on the impact of out-of-domain test data in the performance of our system. UDS-DFKI achieved competitive performance ranking second among ten teams in Czech to Polish translation.
Multi Target Tracking by Learning from Generalized Graph Differences

Formulating the multi object tracking problem as a network flow optimization problem is a popular choice. In this paper an efficient way of learning the weights of such a network is presented. It separates the problem into one embedding of feasible solutions into a one dimensional feature space and one optimization problem. The embedding can be learned using standard SGD type optimization without relying on an additional optimizations within each step. Training data is produced by performing small perturbations of ground truth tracks and representing them using generalized graph differences, which is an efficient way introduced to represent the difference between two graphs. The proposed method is evaluated on DukeMTMCT with competitive results.
Cross-Domain Collaborative Filtering via Translation-based Learning

With the proliferation of social media platforms and e-commerce sites, several cross-domain collaborative filtering strategies have been recently introduced to transfer the knowledge of user preferences across domains. The main challenge of cross-domain recommendation is to weigh and learn users' different behaviors in multiple domains. In this paper, we propose a Cross-Domain collaborative filtering model following a Translation-based strategy, namely CDT. In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains. In doing so, we efficiently compute the transitivity between feature latent embeddings, that is if feature pairs have high interaction weights in the latent space, then feature embeddings with no observed interactions across the domains will be closely related as well. We formulate our objective function as a ranking problem in factorization machines and learn the model's parameters via gradient descent. In addition, to better capture the non-linearity in user preferences across domains we extend the proposed CDT model by using a deep learning strategy, namely DeepCDT. Our experiments on six publicly available cross-domain tasks demonstrate the effectiveness of the proposed models, outperforming other state-of-the-art cross-domain strategies.
ED2: Two-stage Active Learning for Error Detection - Technical Report

Traditional error detection approaches require user-defined parameters and rules. Thus, the user has to know both the error detection system and the data. However, we can also formulate error detection as a semi-supervised classification problem that only requires domain expertise. The challenges for such an approach are twofold: (1) to represent the data in a way that enables a classification model to identify various kinds of data errors, and (2) to pick the most promising data values for learning. In this paper, we address these challenges with ED2, our new example-driven error detection method. First, we present a new two-dimensional multi-classifier sampling strategy for active learning. Second, we propose novel multi-column features. The combined application of these techniques provides fast convergence of the classification task with high detection accuracy. On several real-world datasets, ED2 requires, on average, less than 1% labels to outperform existing error detection approaches. This report extends the peer-reviewed paper "ED2: A Case for Active Learning in Error Detection". All source code related to this project is available on GitHub.
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks

Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient event-driven hardware neuromorphic processors. As an important class of SNNs, recurrent spiking neural networks (RSNNs) possess great computational power. However, the practical application of RSNNs is severely limited by challenges in training. Biologically-inspired unsupervised learning has limited capability in boosting the performance of RSNNs. On the other hand, existing backpropagation (BP) methods suffer from high complexity of unrolling in time, vanishing and exploding gradients, and approximate differentiation of discontinuous spiking activities when applied to RSNNs. To enable supervised training of RSNNs under a well-defined loss function, we present a novel Spike-Train level RSNNs Backpropagation (ST-RSBP) algorithm for training deep RSNNs. The proposed ST-RSBP directly computes the gradient of a rated-coded loss function defined at the output layer of the network w.r. t tunable parameters. The scalability of ST-RSBP is achieved by the proposed spike-train level computation during which temporal effects of the SNN is captured in both the forward and backward pass of BP. Our ST-RSBP algorithm can be broadly applied to RSNNs with a single recurrent layer or deep RSNNs with multiple feed-forward and recurrent layers. Based upon challenging speech and image datasets including TI46, N-TIDIGITS, Fashion-MNIST and MNIST, ST-RSBP is able to train RSNNs with an accuracy surpassing that of the current state-of-art SNN BP algorithms and conventional non-spiking deep learning models.
Screening Current-Induced Field and Field Drift Study in HTS coils using T-A homogenous model

The emergence of second generation (2G) high-temperature superconductor (HTS) tapes has favored the development of HTS magnets for their applications in areas such as NMR, MRI and high field magnets. The screening current-induced field and the field drift are two major problems hindering the use of HTS tapes in the mentioned areas. Both problems are caused by the screening current, then it is necessary to have a modeling strategy capable to estimate such phenomena. Thus far, the H formulation has been a widely used approach to model medium-size systems (hundreds of tapes). However, its application to large-scale systems is still impaired by excessive computation times and memory requirements. Homogenization and multi-scaling strategies have been successfully implemented to increase the computational efficiency. In this contribution, we show that using the homogenization technique with the recently developed T-A formulation allows reducing the computation time and the amount of memory up to the point that realtime simulations of slow ramping cycles of large-scale systems are possible. At the same time, the T-A homogeneous model provides enough resolution to investigate the screening current using numerical simulations.
Quantitative convergence rates for reversible Markov chains via strong random times

Let $(X_t)$ be a discrete time Markov chain on a general state space. It is well-known that if $(X_t)$ is aperiodic and satisfies a drift and minorization condition, then it converges to its stationary distribution $\pi$ at an exponential rate. We consider the problem of computing upper bounds for the distance from stationarity in terms of the drift and minorization data. Baxendale showed that these bounds improve significantly if one assumes that $(X_t)$ is reversible with nonnegative eigenvalues (i.e. its transition kernel is a self-adjoint operator on $L^2(\pi)$ with spectrum contained in $[0,1]$). We identify this phenomenon as a special case of a general principle: for a reversible chain with nonnegative eigenvalues, any strong random time gives direct control over the convergence rate. We formulate this principle precisely and deduce from it a stronger version of Baxendale's result. Our approach is fully quantitative and allows us to convert drift and minorization data into explicit convergence bounds. We show that these bounds are tighter than those of Rosenthal and Baxendale when applied to a well-studied example.
Clause-Wise and Recursive Decoding for Complex and Cross-Domain Text-to-SQL Generation

Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries over a single table. We focus on the Spider dataset, a complex and cross-domain text-to-SQL task, which includes complex queries over multiple tables. In this paper, we propose a SQL clause-wise decoding neural architecture with a self-attention based database schema encoder to address the Spider task. Each of the clause-specific decoders consists of a set of sub-modules, which is defined by the syntax of each clause. Additionally, our model works recursively to support nested queries. When evaluated on the Spider dataset, our approach achieves 4.6% and 9.8% accuracy gain in the test and dev sets, respectively. In addition, we show that our model is significantly more effective at predicting complex and nested queries than previous work.
Electron-phonon coupling and hot electron thermalization in titanium nitride

We have studied the thermalization of hot carriers in both pristine and defective titanium nitride (TiN) using a two-temperature model. All parameters of this model, including the electron-phonon coupling parameter, were obtained from first-principles density-functional theory calculations. The virtual crystal approximation was used to describe defective systems. We find that thermalization of hot carriers occurs on much faster time scales than in gold as a consequence of the significantly stronger electron-phonon coupling in TiN. Specifically, the largest thermalization times, on the order of 200 femtoseconds, are found in TiN with nitrogen vacancies for electron temperatures around 4000 K.
Video Synthesis of Human Upper Body with Realistic Face

This paper presents a generative adversarial learning-based human upper body video synthesis approach to generate an upper body video of target person that is consistent with the body motion, face expression, and pose of the person in source video. We use upper body keypoints, facial action units and poses as intermediate representations between source video and target video. Instead of directly transferring the source video to the target video, we firstly map the source person's facial action units and poses into the target person's facial landmarks, then combine the normalized upper body keypoints and generated facial landmarks with spatio-temporal smoothing to generate the corresponding target video's image. Experimental results demonstrated the effectiveness of our method.