Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Phase for Activated Random Walks on the Lattice in all Dimensions

We show that the critical density of the Activated Random Walk model on Z d is strictly less than one when the sleep rate λ is small enough, and tends to 0 when λ → 0 , in any dimension d (cid:62) 1 . As far as we know, the result is new for d = 2 . We prove this by showing that, for high enough density and small enough sleep rate, the stabilization time of the model on the d dimensional torus is exponentially large. To do so, we ﬁx the the set of sites where the particles eventually fall asleep, which reduces the problem to a simpler model with density one. Taking advantage of the Abelian property of the model, we show that the stabilization time stochastically dominates the escape time of a one-dimensional random walk with a negative drift. We then check that this slow phase for the ﬁnite volume dynamics implies the existence of an active phase on the inﬁnite lattice. grand. Pour cela, nous ﬁxons l’ensemble des sites sur lesquels les particules s’endorment, ce qui réduit le problème à un modèle plus simple avec densité 1. En utilisant la propriété d’Abélianité du modèle, nous montrons que le temps de stabilisation domine stochastiquement le temps d’atteinte de 0 pour une marche aléatoire en dimension 1 avec une dérive négative. Nous vériﬁons ensuite que cette phase de stabilisation lente pour la dynamique en volume ﬁni implique l’existence d’une phase active sur le réseau inﬁni.
From local to global gene co-expression estimation using single-cell RNA-seq data.

In genomics studies, the investigation of gene relationships often brings important biological insights. Currently, the large heterogeneous datasets impose new challenges for statisticians because gene relationships are often local. They change from one sample point to another, may only exist in a subset of the sample, and can be nonlinear or even nonmonotone. Most previous dependence measures do not specifically target local dependence relationships, and the ones that do are computationally costly. In this paper, we explore a state-of-the-art network estimation technique that characterizes gene relationships at the single cell level, under the name of cell-specific gene networks. We first show that averaging the cell-specific gene relationship over a population gives a novel univariate dependence measure, the averaged Local Density Gap (aLDG), that accumulates local dependence and can detect any nonlinear, nonmonotone relationship. Together with a consistent nonparametric estimator, we establish its robustness on both the population and empirical levels. Then, we show that averaging the cell-specific gene relationship over mini-batches determined by some external structure information (eg, spatial or temporal factor) better highlights meaningful local structure change points. We explore the application of aLDG and its minibatch variant in many scenarios, including pairwise gene relationship estimation, bifurcating point detection in cell trajectory, and spatial transcriptomics structure visualization. Both simulations and real data analysis show that aLDG outperforms existing ones.
Interventions, Where and How? Experimental Design for Causal Models at Scale

Causal discovery from observational and interventional data is challenging due to limited data and non-identifiability: factors that introduce uncertainty in estimating the underlying structural causal model (SCM). Selecting experiments (interventions) based on the uncertainty arising from both factors can expedite the identification of the SCM. Existing methods in experimental design for causal discovery from limited data either rely on linear assumptions for the SCM or select only the intervention target. This work incorporates recent advances in Bayesian causal discovery into the Bayesian optimal experimental design framework, allowing for active causal discovery of large, nonlinear SCMs while selecting both the interventional target and the value. We demonstrate the performance of the proposed method on synthetic graphs (Erdos-R\`enyi, Scale Free) for both linear and nonlinear SCMs as well as on the \emph{in-silico} single-cell gene regulatory network dataset, DREAM.
Transformations in Learned Image Compression from a Modulation Perspective

In this paper, a unified transformation method in learned image compression(LIC) is proposed from the perspective of modulation. Firstly, the quantization in LIC is considered as a generalized channel with additive uniform noise. Moreover, the LIC is interpreted as a particular communication system according to the consistency in structures and optimization objectives. Thus, the technology of communication systems can be applied to guide the design of modules in LIC. Furthermore, a unified transform method based on signal modulation (TSM) is defined. In the view of TSM, the existing transformation methods are mathematically reduced to a linear modulation. A series of transformation methods, e.g. TPM and TJM, are obtained by extending to nonlinear modulation. The experimental results on various datasets and backbone architectures verify that the effectiveness and robustness of the proposed method. More importantly, it further confirms the feasibility of guiding LIC design from a communication perspective. For example, when backbone architecture is hyperprior combining context model, our method achieves 3.52$\%$ BD-rate reduction over GDN on Kodak dataset without increasing complexity.
Effect of contour tone on tone normalization in Cantonese

Previous studies in Cantonese tone normalization mainly used extrinsic context in the from of precursor sentences made of level tones to investigate the types and amount of acoustic information needed for tone identification. The present study invited 30 subjects aged from 20 to 23 who were with normal hearing to identify a target word with mid level tone. The target word was embedded in two different types of precursor sentences (with contour tone vs. with level tone) which were cut into 4 different lengths. The fundamental frequencies of the precursor sentences were raised for 1 semitone, remained unshifted or lowered for 1.5 semitones to investigate whether tone normalization took place. The performance of the subjects in the two different types of precursor sentences was compared. It was found that 300ms (2 syllables) was the critical length for tone normalization in both types of precursor sentences. Results also support the hypothesis that listeners used average pitch of precursor sentences for lexical tone normalization.
HV-Net: Hypervolume Approximation Based on DeepSets

In this letter, we propose HV-Net, a new method for hypervolume approximation in evolutionary multiobjective optimization. The basic idea of HV-Net is to use DeepSets, a deep neural network with permutation invariant property, to approximate the hypervolume of a nondominated solution set. The input of HV-Net is a nondominated solution set in the objective space, and the output is an approximated hypervolume value of this solution set. The performance of HV-Net is evaluated through computational experiments by comparing it with two commonly used hypervolume approximation methods (i.e., point-based method and line-based method). Our experimental results show that HV-Net outperforms the other two methods in terms of both the approximation error and the runtime, which shows the potential of using deep learning techniques for hypervolume approximation.
A photonic chip-based machine learning approach for the prediction of molecular properties

Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural network with faster data processing and lower energy usage compared to digital computers. Here, we demonstrate the capability of photonic neural networks in predicting the quantum mechanical properties of molecules. Additionally, we show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which we believe is the first of its kind, as most previous works focus on implementing a network for the task of classification. Photonics technology are also naturally capable of implementing complex-valued neural networks at no additional hardware cost and we show that such neural networks outperform conventional real-valued networks for molecular property prediction. Our work opens the avenue for harnessing photonic technology for large-scale machine learning applications in molecular sciences such as drug discovery and materials design.
Empirical structure models of Uranus and Neptune

Uranus and Neptune are still poorly understood. Their gravitational fields, rotation periods, atmosphere dynamics, and internal structures are not well determined. In this paper we present empirical structure models of Uranus and Neptune where the density profiles are represented by polytropes. By using these models, that are set to fit the planetary gravity field, we predict the higher order gravitational coefficients J6 and J8 for various assumed rotation periods, wind depths, and uncertainty of the low-order harmonics. We show that faster rotation and/or deep winds favour centrally concentrated density distributions. We demonstrate that an accurate determination of J6 or J8 with a relative uncertainty no larger than $10{{\ \rm per\ cent}}$ could constrain wind depths of Uranus and Neptune. We also confirm that the Voyager rotation periods are inconsistent with the measured shapes of Uranus and Neptune. We next demonstrate that more accurate determination of the gravity field can significantly reduce the possible range of internal structures. Finally, we suggest that an accurate measurement of the moment of inertia of Uranus and Neptune with a relative uncertainty of $\sim 1{{\ \rm per\ cent}}$ and $\sim 0.1{{\ \rm per\ cent}}$, could constrain their rotation periods and depths of the winds, respectively.
Learning Incrementally to Segment Multiple Organs in a CT Image

There exists a large number of datasets for organ segmentation, which are partially annotated and sequentially constructed. A typical dataset is constructed at a certain time by curating medical images and annotating the organs of interest. In other words, new datasets with annotations of new organ categories are built over time. To unleash the potential behind these partially labeled, sequentially-constructed datasets, we propose to incrementally learn a multi-organ segmentation model. In each incremental learning (IL) stage, we lose the access to previous data and annotations, whose knowledge is assumingly captured by the current model, and gain the access to a new dataset with annotations of new organ categories, from which we learn to update the organ segmentation model to include the new organs. While IL is notorious for its `catastrophic forgetting' weakness in the context of natural image analysis, we experimentally discover that such a weakness mostly disappears for CT multi-organ segmentation. To further stabilize the model performance across the IL stages, we introduce a light memory module and some loss functions to restrain the representation of different categories in feature space, aggregating feature representation of the same class and separating feature representation of different classes. Extensive experiments on five open-sourced datasets are conducted to illustrate the effectiveness of our method.
Gene-Environment Interplay in the Social Sciences

Nature (one’s genes) and nurture (one’s environment) jointly contribute to the formation and evolution of health and human capital over the life cycle. This complex interplay between genes and environment can be estimated and quantified using genetic information readily available in a growing number of social science data sets. To help the novice reader interested in understanding individual decision making, public policy, and inequality using genetic data, we introduce essential genetic terminology, review the literature in economics and social-science genetics—with a focus on the interplay between genes and environment—and discuss policy implications and future prospects of the use of genetic data in the social sciences and economics.
SimKGC: Simple Contrastive Knowledge Graph Completion with Pre-trained Language Models

Knowledge graph completion (KGC) aims to reason over known facts and infer the missing links. Text-based methods such as KGBERT (Yao et al., 2019) learn entity representations from natural language descriptions, and have the potential for inductive KGC. However, the performance of text-based methods still largely lag behind graph embedding-based methods like TransE (Bordes et al., 2013) and RotatE (Sun et al., 2019b). In this paper, we identify that the key issue is efficient contrastive learning. To improve the learning efficiency, we introduce three types of negatives: in-batch negatives, pre-batch negatives, and self-negatives which act as a simple form of hard negatives. Combined with InfoNCE loss, our proposed model SimKGC can substantially outperform embedding-based methods on several benchmark datasets. In terms of mean reciprocal rank (MRR), we advance the state-of-the-art by +19% on WN18RR, +6.8% on the Wikidata5M transductive setting, and +22% on the Wikidata5M inductive setting. Thorough analyses are conducted to gain insights into each component. Our code is available at https://github.com/intfloat/SimKGC .
Baba is Y'all 2.0: Design and Investigation of a Collaborative Mixed-Initiative System

This article describes a new version of the mixed-initiative collaborative level-designing system, i.e., Baba Is Y'all, as well as the results of a user study on the system. Baba is Y'all is a prototype for artificial intelligence (AI)-assisted game design in collaboration with others. The updated version includes a more user-friendly interface, a better level evolver and recommendation system, and extended site features. The system was evaluated via a user study where participants were required to play a previously submitted level from the site, and then create their own levels using the editor. They reported on their individual process creating the level and their overall experience interacting with the site. The results have shown both the benefits and limitations of this mixed-initiative system and how it can help with creating a diversity of Baba is You levels that are both human and AI designed while maintaining their quality.
Learning Time-optimized Path Tracking with or without Sensory Feedback

In this paper, we present a learning-based approach that allows a robot to quickly follow a reference path defined in joint space without exceeding limits on the position, velocity, acceleration and jerk of each robot joint. Contrary to offline methods for time-optimal path parameterization, the reference path can be changed during motion execution. In addition, our approach can utilize sensory feedback, for instance, to follow a reference path with a bipedal robot without losing balance. With our method, the robot is controlled by a neural network that is trained via reinforcement learning using data generated by a physics simulator. From a mathematical perspective, the problem of tracking a reference path in a time-optimized manner is formalized as a Markov decision process. Each state includes a fixed number of waypoints specifying the next part of the reference path. The action space is designed in such a way that all resulting motions comply with the specified kinematic joint limits. The reward function finally reflects the trade-off between the execution time, the deviation from the desired reference path and optional additional objectives like balancing. We evaluate our approach with and without additional objectives and show that time-optimized path tracking can be successfully learned for both industrial and humanoid robots. In addition, we demonstrate that networks trained in simulation can be successfully transferred to a real robot.
Semi-parametric Makeup Transfer via Semantic-aware Correspondence

The large discrepancy between the source non-makeup image and the reference makeup image is one of the key challenges in makeup transfer. Conventional approaches for makeup transfer either learn disentangled representation or perform pixel-wise correspondence in a parametric way between two images. We argue that non-parametric techniques have a high potential for addressing the pose, expression, and occlusion discrepancies. To this end, this paper proposes a \textbf{S}emi-\textbf{p}arametric \textbf{M}akeup \textbf{T}ransfer (SpMT) method, which combines the reciprocal strengths of non-parametric and parametric mechanisms. The non-parametric component is a novel \textbf{S}emantic-\textbf{a}ware \textbf{C}orrespondence (SaC) module that explicitly reconstructs content representation with makeup representation under the strong constraint of component semantics. The reconstructed representation is desired to preserve the spatial and identity information of the source image while"wearing"the makeup of the reference image. The output image is synthesized via a parametric decoder that draws on the reconstructed representation. Extensive experiments demonstrate the superiority of our method in terms of visual quality, robustness, and flexibility. Code and pre-trained model are available at \url{https://github.com/AnonymScholar/SpMT.
Computer-Aided Road Inspection: Systems and Algorithms

Road damage is an inconvenience and a safety hazard, severely affecting vehicle condition, driving comfort, and traffic safety. The traditional manual visual road inspection process is pricey, dangerous, exhausting, and cumbersome. Also, manual road inspection results are qualitative and subjective, as they depend entirely on the inspector's personal experience. Therefore, there is an ever-increasing need for automated road inspection systems. This chapter first compares the five most common road damage types. Then, 2-D/3-D road imaging systems are discussed. Finally, state-of-the-art machine vision and intelligence-based road damage detection algorithms are introduced.
Kleinian sphere packings, reflection groups, and arithmeticity

In this paper we study crystallographic sphere packings and Kleinian sphere packings, introduced first by Kontorovich and Nakamura in 2017 and then studied further by Kapovich and Kontorovich in 2021. In particular, we solve the problem of existence of crystallographic sphere packings in certain higher dimensions posed by Kontorovich and Nakamura. In addition, we present a geometric doubling procedure allowing to obtain sphere packings from some Coxeter polyhedra without isolated roots, and study “properly integral” packings (that is, ones which are integral but not superintegral). Our techniques rely extensively on computations with Lorentzian quadratic forms, their orthogonal groups, and associated higher–dimensional hyperbolic polyhedra.
Differentiable Causal Discovery Under Latent Interventions

Recent work has shown promising results in causal discovery by leveraging interventional data with gradient-based methods, even when the intervened variables are unknown. However, previous work assumes that the correspondence between samples and interventions is known, which is often unrealistic. We envision a scenario with an extensive dataset sampled from multiple intervention distributions and one observation distribution, but where we do not know which distribution originated each sample and how the intervention affected the system, \textit{i.e.}, interventions are entirely latent. We propose a method based on neural networks and variational inference that addresses this scenario by framing it as learning a shared causal graph among an infinite mixture (under a Dirichlet process prior) of intervention structural causal models. Experiments with synthetic and real data show that our approach and its semi-supervised variant are able to discover causal relations in this challenging scenario.
Region-of-Interest Based Neural Video Compression

Humans do not perceive all parts of a scene with the same resolution, but rather focus on few regions of interest (ROIs). Traditional Object-Based codecs take advantage of this biological intuition, and are capable of non-uniform allocation of bits in favor of salient regions, at the expense of increased distortion the remaining areas: such a strategy allows a boost in perceptual quality under low rate constraints. Recently, several neural codecs have been introduced for video compression, yet they operate uniformly over all spatial locations, lacking the capability of ROI-based processing. In this paper, we introduce two models for ROI-based neural video coding. First, we propose an implicit model that is fed with a binary ROI mask and it is trained by de-emphasizing the distortion of the background. Secondly, we design an explicit latent scaling method, that allows control over the quantization binwidth for different spatial regions of latent variables, conditioned on the ROI mask. By extensive experiments, we show that our methods outperform all our baselines in terms of Rate-Distortion (R-D) performance in the ROI. Moreover, they can generalize to different datasets and to any arbitrary ROI at inference time. Finally, they do not require expensive pixel-level annotations during training, as synthetic ROI masks can be used with little to no degradation in performance. To the best of our knowledge, our proposals are the first solutions that integrate ROI-based capabilities into neural video compression models.
dApps: Distributed Applications for Real-Time Inference and Control in O-RAN

The Open Radio Access Network (Open RAN) - being standardized, among others, by the O-RAN Alliance - brings a radical transformation to the cellular ecosystem through disaggregation and RAN intelligent controllers (RICs). The latter enable closed-loop control through custom logic applications (xApps and rApps), supporting control decisions at different timescales. However, the current O-RAN specifications lack a practical approach to execute real-time control loops operating at timescales below 10 ms. In this article, we propose the notion of dApps, distributed applications that complement existing xApps/rApps by allowing operators to implement fine-grained data-driven management and control in real time at the central/distributed units (DUs). dApps receive real-time data from the RAN, as well as enrichment information from the near-real-time RIC, and execute inference and control of lower-layer functionalities, thus enabling use cases with stricter timing requirements than those considered by the RICs, such as beam management and user scheduling. We propose feasible ways to integrate dApps in the O-RAN architecture by leveraging and extending interfaces and components already present therein. Finally, we discuss challenges specific to dApps, and provide preliminary results that show the benefits of executing network intelligence through dApps.