Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Variational Approach to Clustering Survival Data

In this work, we study the problem of clustering survival data $-$ a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms.
C-GLISp: Preference-Based Global Optimization Under Unknown Constraints With Applications to Controller Calibration

Preference-based global optimization algorithms minimize an unknown objective function only based on whether the function is better, worse, or similar for given pairs of candidate optimization vectors. Such optimization problems arise in many real-life examples, such as finding the optimal calibration of the parameters of control law. The calibrator can judge whether a particular combination of parameters leads to a better, worse, or similar closed-loop performance. Often, the search for the optimal parameters is also subject to unknown constraints. For example, the vector of calibration parameters must not lead to closed-loop instability. This article extends an active preference learning algorithm introduced recently to handle unknown constraints. The proposed method, called C-GLISp, looks for an optimizer of the problem only based on preferences expressed on pairs of candidate vectors and on whether a given vector is reported feasible and/or satisfactory. C-GLISp learns a surrogate of the underlying objective function based on the expressed preferences and a surrogate of the probability that a sample is feasible and/or satisfactory based on whether each of the tested vectors was judged as such. The surrogate functions are used iteratively to propose a new candidate vector to test and judge. Numerical benchmarks and a semiautomated control calibration task demonstrate the effectiveness of C-GLISp, showing that it can reach near-optimal solutions within a small number of iterations.
SVMA: A GAN-based model for Monocular 3D Human Pose Estimation

Recovering 3D human pose from 2D joints is a highly un-constrained problem, especially without any video or multi-view information. We present an unsupervised GAN-based model to recover 3D human pose from 2D joint locations extracted from a single image. Our model uses a GAN to learn the mapping of distribution from 2D poses to 3D poses, not the simple 2D-3D correspondence. Considering the repro-jection constraint, our model can estimate the camera so that we can reproject the estimated 3D pose to the original 2D pose. Based on this reprojection method, we can rotate and reproject the generated pose to get our ”new” 2D pose and then use a weight sharing generator to estimate the ”new” 3D pose and a ”new” camera. Through the above estimation process, we can deﬁne the single-view-multi-angle consistency loss during training to simulate multi-view consistency, which means the 3D poses and cameras estimated from two angles of a single view should be able to be mixed to generate rich 2D reprojections, and the 2D reprojections reprojected from the same 3D pose should be consistent. The experimental results on Human3.6M show that our method outperforms all the state-of-the-art meth-ods, and results on MPI-INF-3DHP show that our method outperforms state-of-the-art by approximately 15.0%.
A Neural Tangent Kernel Perspective of GANs

We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs). We reveal a fundamental flaw of previous analyses which, by incorrectly modeling GANs' training scheme, are subject to ill-defined discriminator gradients. We overcome this issue which impedes a principled study of GAN training, solving it within our framework by taking into account the discriminator's architecture. To this end, we leverage the theory of infinite-width neural networks for the discriminator via its Neural Tangent Kernel. We characterize the trained discriminator for a wide range of losses and establish general differentiability properties of the network. From this, we derive new insights about the convergence of the generated distribution, advancing our understanding of GANs' training dynamics. We empirically corroborate these results via an analysis toolkit based on our framework, unveiling intuitions that are consistent with GAN practice.
Investigating the Multidisciplinary Perspective of Perceived Safety in Human-Robot Interaction

Safety is a key issue in human-robot interaction, but perceived safety has not been well-studied in comparison to physical safety. In this paper, we address the multidisciplinary perspective of perceived safety in human-robot interaction. To investigate how the comfort of the user, sense of control of the user, unpredictable robot behaviors, and trust impact the safety perception of the user, we designed a randomized controlled within-subject experiment. We devised ﬁve di ﬀ erent experimental conditions in which we investigated the relationships between perceived safety and comfort, sense of control, and trust. In each condition, we modiﬁed one factor. To extend our previous ﬁndings, the participants were asked to answer questionnaires that measure comfort, sense of control, trust, and perceived safety. The questionnaire results show a strong correlation between these factors and the perceived safety. Since these factors are the main factors that inﬂuence perceived safety, they should be considered in human-robot interaction design decisions. The e ﬀ ect of individual characteristics such as personality and gender on perceived safety was also discussed. Moreover, we analyzed the facial a ﬀ ect and physiological signals of the participants for predicting perceived safety from objective measures. The data from objective measures revealed that physiological signals give better prediction of perceived safety rather than facial a ﬀ ect data. We believe this article can play an important role in the goal of better understanding perceived safety in human-robot interaction.
Steering visible Dyakonov surface waves at the hyperbolic metasurface

Dyakonov surface wave existing at the interface with anisotropy offers a promising way of guiding light in two-dimension with almost no loss. However, predicted decades ago, the experimental demonstration of the Dyakonov surface wave seems always challenging for the weak anisotropic indices from the natural materials. Here we experimentally demonstrated a Dyakonov surface wave mode propagating in a hyperbolic metasurface at the visible frequency. Dyakonov surface waves at the two surfaces of the metasurface can be supported simultaneously by the hyperbolic anisotropy and form a Dyakonov typed mode with low loss and a large allowed angle band. A detailed theoretical analysis and numerical simulations prove that the electric field of such a surface wave mode shows transverse spin, whose direction is determined by the orientations of the hyperbolic anisotropy and surface normal, based on which we experimentally observed the photonic spin Hall effect of the surface wave mode in our metasurface.
Space-time Mixing Attention for Video Transformer

This paper is on video recognition using Transformers. Very recent attempts in this area have demonstrated promising results in terms of recognition accuracy, yet they have been also shown to induce, in many cases, significant computational overheads due to the additional modelling of the temporal information. In this work, we propose a Video Transformer model the complexity of which scales linearly with the number of frames in the video sequence and hence induces no overhead compared to an image-based Transformer model. To achieve this, our model makes two approximations to the full space-time attention used in Video Transformers: (a) It restricts time attention to a local temporal window and capitalizes on the Transformer's depth to obtain full temporal coverage of the video sequence. (b) It uses efficient space-time mixing to attend jointly spatial and temporal locations without inducing any additional cost on top of a spatial-only attention model. We also show how to integrate 2 very lightweight mechanisms for global temporal-only attention which provide additional accuracy improvements at minimal computational cost. We demonstrate that our model produces very high recognition accuracy on the most popular video recognition datasets while at the same time being significantly more efficient than other Video Transformer models. Code will be made available.
Automatic Construction of Context-Aware Sentiment Lexicon in the Financial Domain Using Direction-Dependent Words

Increasing attention has been drawn to the sentiment analysis of financial documents. The most popular examples of such documents include analyst reports and economic news, the analysis of which is frequently used to capture the trends in market sentiments. On the other hand, the significance of the role sentiment analysis plays in the financial domain has given rise to the efforts to construct a financial domain-specific sentiment lexicon. Sentiment lexicons lend a hand for solving various text mining tasks, such as unsupervised classification of text data, while alleviating the arduous human labor required for manual labeling. One of the challenges in the construction of an effective sentiment lexicon is that the semantic orientation of a word may change depending on the context in which it appears. For instance, the word ``profit"usually conveys positive sentiments; however, when the word is juxtaposed with another word ``decrease,"the sentiment associated with the phrase ``profit decreases"now becomes negative. Hence, the sentiment of a given word may shift as one begins to consider the context surrounding the word. In this paper, we address this issue by incorporating context when building sentiment lexicon from a given corpus. Specifically, we construct a lexicon named Senti-DD for the Sentiment lexicon composed of Direction-Dependent words, which expresses each term a pair of a directional word and a direction-dependent word. Experiment results show that higher classification performance is achieved with Senti-DD, proving the effectiveness of our method for automatically constructing a context-aware sentiment lexicon in the financial domain.
Existence of Strong Solution for the Complexified Non-linear Poisson Boltzmann Equation

We prove the existence and uniqueness of the complexified Nonlinear Poisson-Boltzmann Equation (nPBE) in a bounded domain in $\mathbb{R}^3$. The nPBE is a model equation in nonlinear electrostatics. The standard convex optimization argument to the complexified nPBE no longer applies, but instead, a contraction mapping argument is developed. Furthermore, we show that uniqueness can be lost if the hypotheses given are not satisfied. The complixified nPBE is highly relevant to regularity analysis of the solution of the real nPBE with respect to the dielectric (diffusion) and Debye-H\"uckel coefficients. This approach is also well-suited to investigate the existence and uniqueness problem for a wide class of semi-linear elliptic Partial Differential Equations (PDEs).
Using CMS Open Data in research – challenges and directions

The CMS experiment at CERN has released research-quality data from particle collisions at the LHC since 2014. Almost all data from the first LHC run in 2010–2012 with the corresponding simulated samples are now in the public domain, and several scientific studies have been performed using these data. This paper summarizes the available data and tools, reviews the challenges in using them in research, and discusses measures to improve their usability.
Hybrid Spherical- and Planar-Wave Channel Modeling and DCNN-powered Estimation for Terahertz Ultra-massive MIMO Systems

The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarray. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies.The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity.
Parameter and Feature Selection in Stochastic Linear Bandits

We study two model selection settings in stochastic linear bandits (LB). In the ﬁrst setting, which we refer to as feature selection , the expected reward of the LB problem is in the linear span of at least one of M feature maps (models). In the second setting, the reward parameter of the LB problem is arbitrarily selected from M models represented as (possibly) overlapping balls in R d . However, the agent only has access to misspeciﬁed models, i.e., estimates of the centers and radii of the balls. We refer to this setting as parameter selection . For each setting, we develop and analyze an algorithm that is based on a reduction from bandits to full-information problems. This allows us to obtain regret bounds that are not worse (up to a √ log M factor) than the case where the true model is known. The regret of our parameter selection algorithm also scales logarithmically with model uncertainty. Finally, we empirically show the eﬀectiveness of our algorithms using synthetic and real-world experiments.
Spatiotemporal Spike-Pattern Selectivity in Single Mixed-Signal Neurons with Balanced Synapses

—Realizing the potential of mixed-signal neuromorphic processors for ultra-low-power inference and learning requires efﬁcient use of their inhomogeneous analog circuitry as well as sparse, time-based information encoding and processing. Here, we investigate spike-timing-based spatiotemporal receptive ﬁelds of output-neurons in the Spatiotemporal Correlator (STC) network, for which we used excitatory–inhibitory balanced disy-naptic inputs instead of dedicated axonal or neuronal delays. We present hardware-in-the-loop experiments with a mixed-signal DYNAP-SE neuromorphic processor, in which ﬁve-dimensional receptive ﬁelds of hardware neurons were mapped by randomly sampling input spike-patterns from a uniform distribution. We ﬁnd that, when the balanced disynaptic elements are randomly programmed, some of the neurons display distinct receptive ﬁelds. Furthermore, we demonstrate how a neuron was tuned to detect a particular spatiotemporal feature, to which it initially was non-selective, by activating a different subset of the inhomogeneous analog synaptic circuits. The energy dissipation of the balanced synaptic elements is one order of magnitude lower per lateral connection (0.65 nJ vs 9.3 nJ per spike) than former delay-based neuromorphic hardware implementations. Thus, we show how the inhomogeneous synaptic circuits could be utilized for resource-efﬁcient implementation of STC network layers, in a way that enables synapse-address reprogramming as a discrete mechanism for feature tuning.
Cross-Modal Discrete Representation Learning

In contrast to recent advances focusing on high-level representation learning across modalities, in this work we present a self-supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by visual objects or spoken words. Our framework relies on a discretized embedding space created via vector quantization that is shared across different modalities. Beyond the shared embedding space, we propose a Cross-Modal Code Matching objective that forces the representations from different views (modalities) to have a similar distribution over the discrete embedding space such that cross-modal objects/actions localization can be performed without direct supervision. We show that the proposed discretized multi-modal fine-grained representation (e.g., pixel/word/frame) can complement high-level summary representations (e.g., video/sentence/waveform) for improved performance on cross-modal retrieval tasks. We also observe that the discretized representation uses individual clusters to represent the same semantic concept across modalities.
Correlation functions in 2D and 3D as descriptors of the cosmic web

Aims. Our goal is to find the relation between the two-point correlation functions (CFs) of projected and spatial density fields of galaxies in the context of the cosmic web. Methods. To investigate relations between spatial (3D) and projected (2D) CFs of galaxies we used density fields of two simulations: a Λ-dominated cold dark matter model with known particle data, and the Millennium simulation with know data on simulated galaxies. We compare 3D and 2D correlation functions. In the 2D case, we use samples of various thickness to find the dependence of 2D CFs on the thickness of samples. We also compare 3D CFs in real and redshift space. Results. The dominant elements of the cosmic web are clusters and filaments, separated by voids filling most of the volume. In individual 2D sheets, the positions of clusters and filaments do not coincide. As a result, in projection, the clusters and filaments fill in 2D voids. This leads to a decrease in the amplitudes of CFs (and power spectra) in projection. For this reason, the amplitudes of 2D CFs are lower than the amplitudes of 3D correlation functions: the thicker the 2D sample, the greater the difference. Conclusions. Spatial CFs of galaxies contain valuable information about the geometrical properties of the cosmic web that cannot be found from projected CFs.
Ollivier Ricci-flow on weighted graphs

We study the existence of solutions of Ricci flow equations of Ollivier-Lin-Lu-Yau curvature defined on weighted graphs. Our work is motivated by\cite{NLLG} in which the discrete time Ricci flow algorithm has been applied successfully as a discrete geometric approach in detecting complex networks. Our main result is the existence and uniqueness theorem for solutions to a continuous time normalized Ricci flow. We also display possible solutions to the Ricci flow on path graph and prove the Ricci flow on finite star graph with at least three leaves converges to constant-weighted star.
Energy Conversion from Heat to Electricity by Highly Reversible Phase-Transforming Ferroelectrics

Searching for performant multiferroic materials attracts general research interests in energy science as they have been increasingly exploited as the conversion media among thermal, electric, magnetic and mechanical energies by using their temperature-dependent ferroic properties. Here we report a material development strategy that guides us to discover a reversible phase-transforming ferroelectric material exhibiting enduring energy harvesting from small temperature differences. The material satisfies the crystallographic compatibility condition between polar and nonpolar phases, which shows only 2.5◦C thermal hysteresis and high figure of merit. It stably generates 15μA electricity in consecutive thermodynamic cycles in absence of any bias fields. We demonstrate our device to consistently generate 6μA/cm2 current density near 100◦C over 540 complete phase transformation cycles without any electric and functional degradation. The energy conversion device can light up a LED directly without attaching an external power source. This promising material candidate brings the low-grade waste heat harvesting closer to a practical realization, e.g. small temperature fluctuations around the water boiling point can be considered as a clean energy source.
Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners

Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label information is owned by a single data owner. In this paper, we propose a novel method, Multi Vertical Federated Learning (Multi-VFL), to train VFL models when there are multiple data and label owners. Our approach is the first to consider the setting where $D$-data owners (across which features are distributed) and $K$-label owners (across which labels are distributed) exist. This proposed configuration allows different entities to train and learn optimal models without having to share their data. Our framework makes use of split learning and adaptive federated optimizers to solve this problem. For empirical evaluation, we run experiments on the MNIST and FashionMNIST datasets. Our results show that using adaptive optimizers for model aggregation fastens convergence and improves accuracy.
Informative Policy Representations in Multi-Agent Reinforcement Learning via Joint-Action Distributions

In multi-agent reinforcement learning, the inherent non-stationarity of the environment caused by other agents’ actions posed signiﬁcant difﬁculties for an agent to learn a good policy independently. One way to deal with non-stationarity is agent modeling, by which the agent takes into consideration the inﬂuence of other agents’ policies. Most existing work relies on predicting other agents’ actions or goals, or discriminating between their policies. However, such modeling fails to capture the similarities and differences between policies simultaneously and thus cannot provide useful information when generalizing to unseen policies. To address this, we propose a general method to learn representations of other agents’ policies via the joint-action distributions sampled in interactions. The similarities and differences between policies are naturally captured by the policy distance inferred from the joint-action distributions and deliberately reﬂected in the learned representations. Agents conditioned on the policy representations can well generalize to unseen agents. We empirically demonstrate that our method outperforms existing work in multi-agent tasks when facing unseen agents.