Miracle-dz/UniFAR-FTUs huggingface dataset:
User-friendly introduction to PAC-Bayes bounds

Aggregated predictors are obtained by making a set of basic predictors vote according to some weights, that is, to some probability distribution. Randomized predictors are obtained by sampling in a set of basic predictors, according to some prescribed probability distribution. Thus, aggregated and randomized predictors have in common that they are not defined by a minimization problem, but by a probability distribution on the set of predictors. In statistical learning theory, there is a set of tools designed to understand the generalization ability of such procedures: PAC-Bayesian or PAC-Bayes bounds. Since the original PAC-Bayes bounds of D. McAllester, these tools have been considerably improved in many directions (we will for example describe a simplified version of the localization technique of O. Catoni that was missed by the community, and later rediscovered as"mutual information bounds"). Very recently, PAC-Bayes bounds received a considerable attention: for example there was workshop on PAC-Bayes at NIPS 2017,"(Almost) 50 Shades of Bayesian Learning: PAC-Bayesian trends and insights", organized by B. Guedj, F. Bach and P. Germain. One of the reason of this recent success is the successful application of these bounds to neural networks by G. Dziugaite and D. Roy. An elementary introduction to PAC-Bayes theory is still missing. This is an attempt to provide such an introduction.
CIMTx: An R Package for Causal Inference with Multiple Treatments using Observational Data

CIMTx provides efficient and unified functions to implement modern methods for causal inferences with multiple treatments using observational data with a focus on binary outcomes. The methods include regression adjustment, inverse probability of treatment weighting, Bayesian additive regression trees, regression adjustment with multivariate spline of the generalized propensity score, vector matching and targeted maximum likelihood estimation. In addition, CIMTx illustrates ways in which users can simulate data adhering to the complex data structures in the multiple treatment setting. Furthermore, the CIMTx package offers a unique set of features to address the key causal assumptions: positivity and ignorability. For the positivity assumption, CIMTx demonstrates techniques to identify the common support region for retaining inferential units using inverse probability of treatment weighting, Bayesian additive regression trees and vector matching}. To handle the ignorability assumption, CIMTx provides a flexible Monte Carlo sensitivity analysis approach to evaluate how causal conclusions would be altered in response to different magnitude of departure from ignorable treatment assignment.
Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection

Multimodal learning is an emerging yet challenging research area. In this paper, we deal with multimodal sarcasm and humor detection from conversational videos and image-text pairs. Being a fleeting action, which is reflected across the modalities, sarcasm detection is challenging since large datasets are not available for this task in the literature. Therefore, we primarily focus on resource-constrained training, where the number of training samples is limited. To this end, we propose a novel multimodal learning system, MuLOT (Multimodal Learning using Optimal Transport), which utilizes self-attention to exploit intra-modal correspondence and optimal transport for cross-modal correspondence. Finally, the modalities are combined with multimodal attention fusion to capture the inter-dependencies across modalities. We test our approach for multimodal sarcasm and humor detection on three benchmark datasets - MUStARD [7] (video, audio, text), UR-FUNNY [20] (video, audio, text), MST [3] (image, text) and obtain 2.1%, 1.54% and 2.34% accuracy improvements over state-of-the-art.
2020 CATARACTS Semantic Segmentation Challenge

Surgical scene segmentation is essential for anatomy and instrument localization which can be further used to assess tissue-instrument interactions during a surgical procedure. In 2017, the Challenge on Automatic Tool Annotation for cataRACT Surgery (CATARACTS) released 50 cataract surgery videos accompanied by instrument usage annotations. These annotations included frame-level instrument presence information. In 2020, we released pixel-wise semantic annotations for anatomy and instruments for 4670 images sampled from 25 videos of the CATARACTS training set. The 2020 CATARACTS Semantic Segmentation Challenge, which was a sub-challenge of the 2020 MICCAI Endoscopic Vision (EndoVis) Challenge, presented three sub-tasks to assess participating solutions on anatomical structure and instrument segmentation. Their performance was assessed on a hidden test set of 531 images from 10 videos of the CATARACTS test set.
Dynamic Multi-Person Mesh Recovery From Uncalibrated Multi-View Cameras

Dynamic multi-person mesh recovery has been a hot topic in 3D vision recently. However, few works focus on the multi-person motion capture from uncalibrated cameras, which mainly faces two challenges: the one is that inter-person interactions and occlusions introduce inherent ambiguities for both camera calibration and motion capture; The other is that a lack of dense correspondences can be used to constrain sparse camera geometries in a dynamic multi-person scene. Our key idea is incorporating motion prior knowledge into simultaneous optimization of extrinsic camera parameters and human meshes from noisy human semantics. First, we introduce a physics-geometry consistency to reduce the low and high frequency noises of the detected human semantics. Then a novel latent motion prior is proposed to simultaneously optimize extrinsic camera parameters and coherent human motions from slightly noisy inputs. Experimental results show that accurate camera parameters and human motions can be obtained through one-stage optimization. The codes will be publicly available at https://www.yangangwang. com.
A small radio galaxy at
 z
 = 4.026

1Department of Astronomy, Institute of Geography and Earth Sciences, ELTE Eötvös Loránd University, Budapest, Hungary 2Konkoly Observatory, ELKH Research Centre for Astronomy and Earth Sciences, Budapest, Hungary 3ELKH-ELTE Extragalactic Research Group, ELTE Eötvös Loránd University, Budapest, Hungary 4Shanghai Astronomical Observatory, Shanghai, China 5Shangqiu Normal University, Shangqiu, China 6Joint Institute for VLBI ERIC, Dwingeloo, The Netherlands 7Department of Astrodynamics and Space Missions, Delft University of Technology, Delft, The Netherlands 8Osservatorio Astronomico di Brera, INAF, Merate, Italy
Variational Predictive Routing with Nested Subjective Timescales

Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore hierarchical generative models that can flexibly adapt their layerwise representations in response to datasets with different temporal dynamics. Here, we present Variational Predictive Routing (VPR) - a neural probabilistic inference system that organizes latent representations of video features in a temporal hierarchy, based on their rates of change, thus modeling continuous data as a hierarchical renewal process. By employing an event detection mechanism that relies solely on the system's latent representations (without the need of a separate model), VPR is able to dynamically adjust its internal state following changes in the observed features, promoting an optimal organisation of representations across the levels of the model's latent hierarchy. Using several video datasets, we show that VPR is able to detect event boundaries, disentangle spatiotemporal features across its hierarchy, adapt to the dynamics of the data, and produce accurate time-agnostic rollouts of the future. Our approach integrates insights from neuroscience and introduces a framework with high potential for applications in model-based reinforcement learning, where flexible and informative state-space rollouts are of particular interest.
ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning

Access to 3D point cloud representations has been widely facilitated by LiDAR sensors embedded in various mobile devices. This has led to an emerging need for fast and accurate point cloud processing techniques. In this paper, we revisit and dive deeper into PointNet++, one of the most influential yet under-explored networks, and develop faster and more accurate variants of the model. We first present a novel Separable Set Abstraction (SA) module that disentangles the vanilla SA module used in PointNet++ into two separate learning stages: (1) learning channel correlation and (2) learning spatial correlation. The Separable SA module is significantly faster than the vanilla version, yet it achieves comparable performance. We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy. We later replace the vanilla SA modules in PointNet++ with the proposed ASSA module, and denote the modified network as ASSANet. Extensive experiments on point cloud classification, semantic segmentation, and part segmentation show that ASSANet outperforms PointNet++ and other methods, achieving much higher accuracy and faster speeds. In particular, ASSANet outperforms PointNet++ by $7.4$ mIoU on S3DIS Area 5, while maintaining $1.6 \times $ faster inference speed on a single NVIDIA 2080Ti GPU. Our scaled ASSANet variant achieves $66.8$ mIoU and outperforms KPConv, while being more than $54 \times$ faster.
UPPRESSO: Untraceable and Unlinkable Privacy-PREserving Single Sign-On Services

Single sign-on (SSO) allows a user to maintain only the credential at the identity provider (IdP), to login to numerous RPs. However, SSO introduces extra privacy threats, compared with traditional authentication mechanisms, as ( a ) the IdP could track all RPs which a user is visiting, and ( b ) collusive RPs could learn a user’s online proﬁle by linking his identities across these RPs. This paper proposes a privacy-preserving SSO system, called UPPRESSO , to protect a user’s login activities against both the curious IdP and collusive RPs. We analyze the identity dilemma between the security requirements and these privacy concerns, and convert the SSO privacy problems into an identity transformation challenge. In each login instance, an ephemeral pseudo-identity (denoted as PID RP ) of the RP, is ﬁrstly negotiated between the user and the RP. PID RP is sent to the IdP and designated in the identity token, so the IdP is not aware of the visited RP. Meanwhile, PID RP is used by the IdP to transform the permanent user identity ID U into an ephemeral user pseudo-identity (denoted as PID U ) in the identity token. On receiving the identity token, the RP transforms PID U into a permanent account (denoted as Acct ) of the user, by an ephemeral trapdoor in the negotiation. Given a user, the account at each RP is unique and different from ID U , so
Come Again? Re-Query in Referring Expression Comprehension

To build a shared perception of the world, humans rely on the ability to resolve misunderstandings by requesting and accepting clariﬁcations. However, when evaluating visiolin-guistic models, metrics such as accuracy enforce the assumption that a decision must be made based on a single piece of evidence. In this work, we relax this assumption for the task of referring expression comprehension by allowing the model to request help when its conﬁdence is low. We consider two ways in which this help can be provided: multimodal re-query , where the user is allowed to point or click to provide additional information to the model, and rephrase re-query , where the user is only allowed to provide another referring expression. We demonstrate the importance of re-query by showing that providing the best referring expression for all objects can increase accuracy by up to 21.9% and that this accuracy can be matched by re-querying only 12% of initial referring expressions. We further evaluate re-query functions for both multimodal and rephrase re-query across three modern approaches and demonstrate combined replacement for rephrase re-query, which improves average single-query performance by up to 6.5% and converges to as close as 1.6% of the upper bound of single-query performance.
Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation

Machine learning training methods depend plentifully and intricately on hyperparameters, motivating automated strategies for their optimisation. Many existing algorithms restart training for each new hyperparameter choice, at considerable computational cost. Some hypergradient-based one-pass methods exist, but these either cannot be applied to arbitrary optimiser hyperparameters (such as learning rates and momenta) or take several times longer to train than their base models. We extend these existing methods to develop an approximate hypergradient-based hyperparameter optimiser which is applicable to any continuous hyperparameter appearing in a differentiable model weight update, yet requires only one training episode, with no restarts. We also provide a motivating argument for convergence to the true hypergradient, and perform tractable gradient-based optimisation of independent learning rates for each model parameter. Our method performs competitively from varied random hyperparameter initialisations on several UCI datasets and Fashion-MNIST (using a one-layer MLP), Penn Treebank (using an LSTM) and CIFAR-10 (using a ResNet-18), in time only 2-3x greater than vanilla training.
Magnetohydrodynamic Winds Driven by Line Force from the Standard Thin Disk around Supermassive Black Holes: II. A Possible Model for Ultra-fast Outflows in Radio-loud AGNs

In radio-loud active galactic nuclei (AGNs), ultra-fast outflows (UFOs) were detected at the inclination angle of ∼10°–70° away from jets. Except for the inclination angle of UFOs, the UFOs in radio-loud AGNs have similar properties to that in radio-quiet AGNs. The UFOs with such low inclination cannot be explained in the line-force mechanism. The magnetic-driving mechanism is suggested to explain the UFOs based on a self-similar solution with radiative transfer calculations. However, the energetics of self-similar solution need to be further confirmed based on numerical simulations. To understand the formation and acceleration of UFOs in radio-loud AGNs, this paper presents a model of the disk winds driven by both line force and magnetic field and implements numerical simulations. Initially, a magnetic field is set to 10 times stronger than the gas pressures at the disk surface. Simulation results imply that the disk winds driven by both line force and magnetic field could describe the properties of UFOs in radio-loud AGNs. Pure magnetohydrodynamics (MHDs) simulation is also implemented. When the initial conditions are the same, the hybrid models of magnetic fields and line force are more helpful to form UFOs than the pure MHD models. It is worth studying the case of a stronger magnetic field to confirm this result.
A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act

This study discusses the interplay between metrics used to measure the explainability of the AI systems and the proposed EU Artificial Intelligence Act. A standardisation process is ongoing: several entities (e.g. ISO) and scholars are discussing how to design systems that are compliant with the forthcoming Act and explainability metrics play a significant role. This study identifies the requirements that such a metric should possess to ease compliance with the AI Act. It does so according to an interdisciplinary approach, i.e. by departing from the philosophical concept of explainability and discussing some metrics proposed by scholars and standardisation entities through the lenses of the explainability obligations set by the proposed AI Act. Our analysis proposes that metrics to measure the kind of explainability endorsed by the proposed AI Act shall be risk-focused, model-agnostic, goal-aware, intelligible & accessible. This is why we discuss the extent to which these requirements are met by the metrics currently under discussion.
Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics

Synopsis: Artificial intelligence (AI) techniques have significant potential to enable effective, robust and automated image phenotyping including identification of subtle patterns. AI-based detection searches the image space to find the regions of interest based on patterns and features. There is a spectrum of tumor histologies from benign to malignant that can be identified by AI-based classification approaches using image features. The extraction of minable information from images gives way to the field of “radiomics” and can be explored via explicit (handcrafted/engineered) and deep radiomics frameworks. Radiomics analysis has the potential to be utilized as a noninvasive technique for accurate characterization of tumors to improve diagnosis and treatment monitoring. This work reviews AI-based techniques, with special focus in oncological PET and PET/CT imaging, for different detection, classification and prediction/prognosis tasks. We also discuss needed efforts to enable translation of AI techniques to routine clinical workflows, and potential improvements and complementary techniques such as the use of natural language processing on electronic health records and neuro-symbolic AI techniques.
Dimensional control of tunneling two-level systems in nanoelectromechanical resonators

Tunneling two-level systems affect damping, noise, and decoherence in a wide range of devices, including nanoelectromechanical resonators, optomechanical systems, and qubits. Theoretically, this interaction is usually described within the tunneling state model. The dimensions of such devices are often small compared to the relevant phonon wavelengths at low temperatures, and extensions of the theoretical description to reduced dimensions have been proposed, but lack conclusive experimental veriﬁcation. We have measured the intrinsic damping and the frequency shift in magnetomotively driven aluminum nanoelectromechanical resonators of various sizes at millikelvin temperatures. We ﬁnd good agreement of the experimental results with a model where the tunneling two-level systems couple to ﬂexural phonons that are restricted to one or two dimensions by geometry of the device. This model can thus be used as an aid when optimizing the geometrical parameters of devices affected by tunneling two-level systems.
Indirect NRDF for Partially Observable Gauss-Markov Processes with MSE Distortion: Complete Characterizations and Optimal Solutions

In this paper we study the problem of characterizing and computing the nonanticipative rate distortion function (NRDF) for partially observable multivariate Gauss-Markov processes with hard mean squared error (MSE) distortion constraints. For the finite time horizon case, we first derive the complete characterization of this problem and its corresponding optimal realization which is shown to be a linear functional of the current time sufficient statistic of the past and current observations signals. We show that when the problem is strictly feasible, it can be computed via semidefinite programming (SDP) algorithm. For time-varying scalar processes with average total MSE distortion we derive an optimal closed form expression by means of a dynamic reverse-waterfilling solution that we also implement via an iterative scheme that convergences linearly in finite time, and a closed-form solution under pointwise MSE distortion constraint. For the infinite time horizon, we give necessary and sufficient conditions to sure that asymptotically the sufficient statistic process of the observation signals achieves a steady-state solution for the corresponding covariance matrices and impose conditions that allow existence of a time-invariant solution. Then, we show that when a finite solution exists in the asymptotic limit, it can be computed via SDP algorithm. We also give strong structural properties on the characterization of the problem in the asymptotic limit that allow for an optimal solution via a reverse-waterfilling algorithm that we implement via an iterative scheme that converges linearly under a finite number of spatial components. Subsequently, we compare the computational time needed to execute for both SDP and reverse-waterfilling algorithms when these solve the same problem to show that the latter is a scalable optimization technique. Our results are corroborated with various simulation studies and are also compared with existing results in the literature.
A Self-Explainable Stylish Image Captioning Framework via Multi-References

In this paper, we propose to build a stylish image captioning model through a Multi-style Multi modality mechanism (2M). We demonstrate that with 2M, we can build an effective stylish captioner and that multi-references produced by the model can also support explaining the model through identifying erroneous input features on faulty examples. We show how this 2M mechanism can be used to build stylish captioning models and show how these models can be utilized to provide explanations of likely errors in the models.
Quantum Markov monogamy inequalities

Markovianity lies at the heart of communication problems. This in turn makes the information-theoretic characterization of Markov processes worthwhile. Data processing inequalities are ubiqui-tous in this sense, assigning necessary conditions for all Markov processes. We address here the problem of the information-theoretic analysis of constraints on Markov processes in the quantum regime. We show the existence of a novel class of quantum data processing inequalities called here quantum Markov monogamy inequalities. This new class of necessary conditions on quantum Markov processes is inspired by its counterpart for classical Markov processes, and thus providing a strong link between classical and quantum constraints on Markovianity. We go on to construct a family of multitime quantum Markov monogamy inequalities, based on the process tensor formalism and that exploits multitime correlations. We then show, by means of an explicit example, that the Markov monogamy inequalities can be stronger than the usual quantum data processing inequalities.
Interaction of hERG Channels and Syntaxin 1A

The human ether-à-go-go related gene (hERG) encodes the pore-forming voltage-gated K channel that is essential for cardiac repolarization. Dr. Tsushima’s laboratory has previously characterized the endogenous expression of SNARE proteins in the mammalian heart, and the interaction of the SNARE protein syntaxin 1A (STX1A) with several cardiac ion channels. Here, we utilize a multi-disciplinary approach to describe the inhibitory effect of STX1A on hERG channel function. STX1A impairs hERG channel maturation and trafficking to the plasma membrane and induces a hyperpolarizing shift in the voltage-sensitivity of steady-state inactivation. We identify the residues involved in this proteinprotein interaction through the use of hERG truncation mutations. We also describe the pharmacological and temperature-mediated rescue of hERG channel trafficking in the presence of STX1A. The regulation of cardiac ion channels by SNARE proteins represents a novel biological mechanism that may have universally intrinsic implications for normal and diseased heart function.