Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibrate to Interpret

Trustworthy machine learning is driving a large number of ML community works in order to improve ML acceptance and adoption. The main aspect of trustworthy machine learning are the followings: fairness, uncertainty, robustness, explainability and formal guaranties. Each of these individual domains gains the ML community interest, visible by the number of related publications. However few works tackle the interconnection between these fields. In this paper we show a first link between uncertainty and explainability, by studying the relation between calibration and interpretation. As the calibration of a given model changes the way it scores samples, and interpretation approaches often rely on these scores, it seems safe to assume that the confidence-calibration of a model interacts with our ability to interpret such model. In this paper, we show, in the context of networks trained on image classification tasks, to what extent interpretations are sensitive to confidence-calibration. It leads us to suggest a simple practice to improve the interpretation outcomes: Calibrate to Interpret.
Semi-unsupervised Learning for Time Series Classification

Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data. In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised). We demonstrate the efficacy of our approach with established time series classification datasets from different domains.
On the Relationship Between Adversarial Robustness and Decision Region in Deep Neural Network

In general, Deep Neural Networks (DNNs) are evaluated by the generalization performance measured on unseen data excluded from the training phase. Along with the development of DNNs, the generalization performance converges to the state-of-the-art and it becomes difficult to evaluate DNNs solely based on this metric. The robustness against adversarial attack has been used as an additional metric to evaluate DNNs by measuring their vulnerability. However, few studies have been performed to analyze the adversarial robustness in terms of the geometry in DNNs. In this work, we perform an empirical study to analyze the internal properties of DNNs that affect model robustness under adversarial attacks. In particular, we propose the novel concept of the Populated Region Set (PRS), where training samples are populated more frequently, to represent the internal properties of DNNs in a practical setting. From systematic experiments with the proposed concept, we provide empirical evidence to validate that a low PRS ratio has a strong relationship with the adversarial robustness of DNNs. We also devise PRS regularizer leveraging the characteristics of PRS to improve the adversarial robustness without adversarial training.
A lensed radio jet at milli-arcsecond resolution I: Bayesian comparison of parametric lens models

We investigate the mass structure of a strong gravitational lens galaxy at z = 0.350, taking advantage of the milli-arcsecond (mas) angular resolution of very long baseline interferometric (VLBI) observations. In the first analysis of its kind at this resolution, we jointly infer the lens model parameters and pixellated radio source surface brightness. We consider several lens models of increasing complexity, starting from an elliptical power-law density profile. We extend this model to include angular multipole structures, a separate stellar mass component, additional nearby field galaxies, and/or a generic external potential. We compare these models using their relative Bayesian log-evidence (Bayes factor). We find strong evidence for angular structure in the lens; our best model is comprised of a power-law profile plus multipole perturbations and external potential, with a Bayes factor of +14984 relative to the elliptical power-law model. It is noteworthy that the elliptical power-law mass distribution is a remarkably good fit on its own, with additional model complexity correcting the deflection angles only at the ∼5 mas level. We also consider the effects of added complexity in the lens model on time-delay cosmography and flux-ratio analyses. We find that an overly simplistic power-law ellipsoid lens model can bias the measurement of H0 by ∼3 per cent and mimic flux ratio anomalies of ∼8 per cent. Our results demonstrate the power of high-resolution VLBI observations to provide strong constraints on the inner density profiles of lens galaxies.
Contrastive Information Transfer for Pre-Ranking Systems

Real-word search and recommender systems usually adopt a multi-stage ranking architecture, including matching, pre-ranking, ranking, and re-ranking. Previous works mainly focus on the ranking stage while very few focus on the pre-ranking stage. In this paper, we focus on the information transfer from ranking to pre-ranking stage. We propose a new Contrastive Information Transfer (CIT) framework to transfer useful information from ranking model to pre-ranking model. We train the pre-ranking model to distinguish the positive pair of representation from a set of positive and negative pairs with a contrastive objective. As a consequence, the pre-ranking model can make full use of rich information in ranking model’s representations. The CIT framework also has the advan-tage of alleviating selection bias and improving the performance of recall metrics, which is crucial for pre-ranking models. We conduct extensive experiments including offline datasets and online A/B testing. Experimental results show that CIT achieves superior results than competitive models. In addition, a strict online A/B testing at one of the world’s largest E-commercial platforms shows that the proposed model achieves 0.63% improvements on CTR and 1.64% improvements on VBR. The proposed model now has been deployed online and serves the main traffic of this system, contributing a remarkable business growth.
On average sizes of Selmer groups and ranks in families of elliptic curves having marked points

We determine average sizes/bounds for the $2$- and $3$-Selmer groups in various families of elliptic curves with marked points, thus confirming several cases of the Poonen--Rains heuristics. As a consequence, we deduce that the average ranks of the elliptic curves in all of these families are bounded. Our proofs are uniform and make use of parametrizations involving various forms of $2 \times 2 \times 2 \times 2$ and $3 \times 3 \times 3$ matrices that we studied in a previous paper. We also deduce that $100\%$ of genus one curves of the form $y^2 = Ax^4 + Bx^2 z^2 + Cz^4$ with $A, B, C \in \mathbb{Z}$, when ordered by $\max\{|B|^2,|AC|\}$, fail the Hasse principle. Other forthcoming applications include proofs that a positive proportion of integers are (respectively, are not) the sum of two rational cubes, and a positive proportion of genus one curves in $\mathbb{P}^1 \times \mathbb{P}^1$ over $\mathbb{Q}$ fail the Hasse principle.
Not All Models Are Equal: Predicting Model Transferability in a Self-challenging Fisher Space

This paper addresses an important problem of ranking the pre-trained deep neural networks and screening the most transferable ones for downstream tasks. It is challenging because the ground-truth model ranking for each task can only be generated by fine-tuning the pre-trained models on the target dataset, which is brute-force and computationally expensive. Recent advanced methods proposed several lightweight transferability metrics to predict the fine-tuning results. However, these approaches only capture static representations but neglect the fine-tuning dynamics. To this end, this paper proposes a new transferability metric, called \textbf{S}elf-challenging \textbf{F}isher \textbf{D}iscriminant \textbf{A}nalysis (\textbf{SFDA}), which has many appealing benefits that existing works do not have. First, SFDA can embed the static features into a Fisher space and refine them for better separability between classes. Second, SFDA uses a self-challenging mechanism to encourage different pre-trained models to differentiate on hard examples. Third, SFDA can easily select multiple pre-trained models for the model ensemble. Extensive experiments on $33$ pre-trained models of $11$ downstream tasks show that SFDA is efficient, effective, and robust when measuring the transferability of pre-trained models. For instance, compared with the state-of-the-art method NLEEP, SFDA demonstrates an average of $59.1$\% gain while bringing $22.5$x speedup in wall-clock time. The code will be available at \url{https://github.com/TencentARC/SFDA}.
Dynamic Complementarity Conditions and Whole-Body Trajectory Optimization for Humanoid Robot Locomotion

This article presents a planner to generate walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot. The interaction between the robot and the walking surface is modeled explicitly via new conditions, the dynamic complementarity conditions. The approach does not require a predefined contact sequence and generates the footsteps automatically. We characterize the robot control objective via a set of tasks, and we address it by solving an optimal control problem. We show that it is possible to achieve walking motions automatically by specifying a minimal set of references, such as a constant desired center of mass velocity and a reference point on the ground. Furthermore, we analyze how the contact modeling choices affect the computational time. We validate the approach by generating and testing walking trajectories for the humanoid robot iCub.
The coalescent in finite populations with arbitrary, fixed structure

The coalescent is a stochastic process representing ancestral lineages in a population undergoing neutral genetic drift. Originally defined for a well-mixed population, the coalescent has been adapted in various ways to accommodate spatial, age, and class structure, along with other features of real-world populations. To unify and further generalize these variants, we introduce an abstract formulation of the coalescent that applies to a broad class of neutral drift models. Our formalism allows for arbitrary -- but fixed -- spatial, age, sex, and class structure, with haploid or diploid genetics and any fixed mating pattern. We represent the coalescent as a random sequence of mappings $\mathcal{C} = \left(C_t\right)_{t=0}^\infty$ from a finite set $G$ to itself, representing the ancestry of alleles at a single genetic locus. $G$ represents the ``sites'' (in individuals, in particular locations and/or classes) at which these alleles can live. The state $C_t:G \to G$ maps each site $g \in G$ to the site containing $g$'s ancestor, $t$ time-steps into the past. Using this representation, we define and analyze coalescence time, coalescence branch length, mutations prior to coalescence, and stationary probabilities of identity-by-descent and identity-by-state. For low mutation, we provide a recipe for computing identity-by-descent and identity-by-state probabilities via the coalescent. Applying our results to a diploid population with arbitrary sex ratio $r$, we find that measures of genetic dissimilarity, among any set of sites, are scaled by $4r(1-r)$ relative to the even sex ratio case.
Asset Trading in Continuous Time: A Cautionary Tale

The continuous time model of dynamic asset trading is the central model of modern ﬁnance. Because trading cannot in fact take place at every moment of time, it would seem desirable to show that the continuous time model can be viewed as the limit of models in which trading can occur only at (many) discrete moments of time. This paper demonstrates that, if we take terminal wealth constraints and self-ﬁnancing constraints as seriously in the discrete model as in the continuous model, then the continuous trading model need not be the limit of discrete trading models. This raises serious foundational questions about the continuous time model.
Incoherent tunneling and topological superconductivity in twisted cuprate bilayers

Twisting two monolayers of a high- T c cuprate superconductor can engender a chiral topological state with spontaneously broken time reversal symmetry T . A crucial ingredient required for the emergence of a gapped topological phase is electron tunneling between the CuO 2 planes, whose explicit form (in an ideal clean sample) is dictated by the symmetry of the atomic orbitals. However, a large body of work on the interlayer transport in cuprates indicates importance of disorder-mediated incoherent tunneling which evades the symmetry constraints present in an idealized crystal. The latter arises even in the cleanest single-crystal samples through oxygen vacancies in layers separating the CuO 2 planes, introduced to achieve the hole doping necessary for superconductivity. Here we assess the inﬂuence of incoherent tunneling on the phase diagram of a twisted bilayer. We show that the model continues to support a fully gapped topological phase with broken T , even in the limit of disorder-mediated interlayer coupling. Compared to the model with a constant, momentum conserving interlayer coupling, the extent of the topological phase around the 45 ◦ twist decreases with increasing incoherence, but remains robustly present for parameters likely relevant to Bi 2 Sr 2 CaCu 2 O 8+ δ .
Comparative Evaluation of AR-based, VR-based, and Traditional Basic Life Support Training

Basic life support (BLS) is crucial in the emergency response system as sudden cardiac arrest is still a major cause of death worldwide. In the majority of cases, cardiac arrest is witnessed out-of-hospital where execution of BLS including resuscitation through by-standers becomes indispensable. However, survival rates of cardiac arrest victims could significantly increase if people were more familiar with the BLS process. In this context, technology-enhanced BLS training approaches utilizing Augmented (AR) and Virtual Reality (VR) have been proposed in recent works. However, most of these approaches are not compliant with the medical BLS guidelines or focus only on specific steps of BLS training such as resuscitation. Furthermore, most of the existing training approaches do not focus on automated assessment to enhance training effectiveness through fine-grained real-time feedback. To overcome these issues, we present a novel AR- and VR-based training environment which supports a comprehensive BLS training compliant with the medical guidelines. Our training environment combines AR-NR-based BLS training with an interactive haptic manikin that supports automated assessment, real-time feedback, and debriefing in an integrated environment. We have conducted a usability evaluation where we analyze the training effectiveness and cognitive workload of BLS training based on our AR and VR environment against traditional BLS training. Results of the evaluation indicate that AR and VR technology have the potential to increase engagement in BLS training and reduce the cognitive workload compared to traditional training.
A conditional gradient homotopy method with applications to Semidefinite Programming

We propose a new homotopy-based conditional gradient method for solving convex optimization problems with a large number of simple conic constraints. Instances of this template naturally appear in semidefinite programming problems arising as convex relaxations of combinatorial optimization problems. Our method is a double-loop algorithm in which the conic constraint is treated via a self-concordant barrier, and the inner loop employs a conditional gradient algorithm to approximate the analytic central path, while the outer loop updates the accuracy imposed on the temporal solution and the homotopy parameter. Our theoretical iteration complexity is competitive when confronted to state-of-the-art SDP solvers, with the decisive advantage of cheap projection-free subroutines. Preliminary numerical experiments are provided for illustrating the practical performance of the method.
Boosting the interpretability of clinical risk scores with intervention predictions

Machine learning systems show significant promise for forecasting patient adverse events via risk scores. However, these risk scores implicitly encode assumptions about future interventions that the patient is likely to receive, based on the intervention policy present in the training data. Without this important context, predictions from such systems are less interpretable for clinicians. We propose a joint model of intervention policy and adverse event risk as a means to explicitly communicate the model's assumptions about future interventions. We develop such an intervention policy model on MIMIC-III, a real world de-identified ICU dataset, and discuss some use cases that highlight the utility of this approach. We show how combining typical risk scores, such as the likelihood of mortality, with future intervention probability scores leads to more interpretable clinical predictions.
$H$-factors in graphs with small independence number

Let H be an h -vertex graph. The vertex arboricity ar ( H ) of H is the least integer r such that V ( H ) can be partitioned into r parts and each part induces a forest in H . We show that for suﬃciently large n ∈ h N , every n -vertex graph G with δ ( G ) ≥ (cid:16) max (cid:110) 1 − 2 f ( H ) , 12 (cid:111) + o (1) (cid:17) n and α ( G ) = o ( n ) contains an H -factor, where f ( H ) = 2 ar ( H ) or 2 ar ( H ) − 1. The result can be viewed an analogue of the Alon–Yuster theorem [1] in Ramsey–Tur´an theory, which generalises the results of Balogh–Molla–Sharifzadeh [2] and Knierm–Su [21] on clique factors. In particular the degree conditions are asymptotically sharp for inﬁnitely many graphs H which are not cliques.
Efficient fine-grained road segmentation using superpixel-based CNN and CRF models

—Towards a safe and comfortable driving, road scene segmentation is a rudimentary problem in camera-based advanced driver assistance systems (ADAS). Despite of the great achievement of Convolutional Neural Networks (CNN) for semantic segmentation task, the high computational efforts of CNN- based methods is still a challenging area. In recent work, we proposed a novel approach to utilize the advantages of CNN’s for the task of road segmentation at reasonable computational effort. The runtime beneﬁts from using irregular superpixels as basis for the input for the CNN rather than the image grid, which tremendously reduces the input size. Although, this method achieved remarkable low computational time in both training and testing phases, the lower resolution of the superpixel domain yields naturally lower accuracy compared to high cost state of the art methods. In this work, we focus on an reﬁnement of the road segmentation utilizing a Conditional Random Field (CRF). The reﬁnement procedure is limited to the superpixels touching the predicted road boundary to keep the additional computational effort low. Reducing the input to the super-pixel domain allows the CNN’s structure to stay small and efﬁcient to compute while keeping the advantage of convolutional layers and makes them eligible for ADAS. Applying CRF compensates the trade-off between accuracy and computational efﬁciency. The proposed system obtains comparable performance among the top-performing algorithms on the KITTI road benchmark and it’s fast inference makes it particularly suitable for real-time applications.
Two-dimensional quadratic double Weyl semimetal

Unconventional Weyl semimetals have attracted intensive research interest in condensed matter physics and materials science, but they are very rare in two dimensions. In this work, based on symmetry analysis and the first-principles electronic structure calculations, we predict that the Si/Bi van der Waals heterostructure is a two-dimensional unconventional quadratic double Weyl semimetal with strong spin-orbit coupling (SOC). Although unprotected by the C3v double group symmetry of the heterostructure, the two-dimensional quadratic double Weyl semimetal is stable for compressive strains up to 6.64%. The system transforms into a trivial semimetal with further increasing strain, where the phase boundary is a two-dimensional triple degenerate semimetal state. Furthermore, the Kane-Mele tight-binding model calculations show that the quadratic double Weyl phase is derived from the competition between the Rashba SOC and the proximity-effect-enhanced intrinsic SOC. On the other hand, by breaking mirror symmetry, the quadratic double Weyl semimetal transforms into a quantum spin Hall insulator as well as a quantum valley Hall insulator phase. Thus, the Si/Bi heterostructure is an excellent platform for studying the exotic physics of two-dimensional double Weyl semimetal and other novel topological phases.
Generalized minimizing movements for the varifold Canham–Helfrich flow

Abstract The gradient flow of the Canham–Helfrich functional is tackled via the generalized minimizing movements approach. We prove the existence of solutions in Wasserstein spaces of varifolds, as well as upper and lower diameter bounds. In the more regular setting of multiply covered C 1 , 1 {C^{1,1}} surfaces, we provide a Li–Yau-type estimate for the Canham–Helfrich energy and prove the conservation of multiplicity along the evolution.
Machine learning of percolation models using graph convolutional neural networks

Percolation is an important topic in climate, physics, materials science, epidemiology, finance, and so on. Prediction of percolation thresholds with machine learning methods remains challenging. In this paper, we build a powerful graph convolutional neural network to study the percolation in both supervised and unsupervised ways. From a supervised learning perspective, the graph convolutional neural network simultaneously and correctly trains data of different lattice types, such as the square and triangular lattices. For the unsupervised perspective, combining the graph convolutional neural network and the confusion method, the percolation threshold can be obtained by the"W"shaped performance. The finding of this work opens up the possibility of building a more general framework that can probe the percolation-related phenomenon.
Self-injection-locked second-harmonic integrated source

High coherence visible and near-visible laser sources are centrally important to the operation of advanced position/navigation/timing systems as well as classical/quantum sensing systems. However, the complexity and size of these bench-top lasers is an impediment to their transitioning beyond the laboratory. Here, a system-on-a-chip that emits high-coherence visible and near-visible lightwaves is demonstrated. The devices rely upon a new approach wherein wavelength conversion and coherence increase by self-injection-locking are combined within in a single nonlinear resonator. This simplified approach is demonstrated in a hybridly-integrated device and provides a short-term linewidth around 10-30 kHz. On-chip, converted optical power over 2 mW is also obtained. Moreover, measurements show that heterogeneous integration can result in conversion efficiency higher than 25% with output power over 11 mW. Because the approach uses mature III-V pump lasers in combination with thin-film lithium niobate, it can be scaled for low-cost manufacturing of high-coherence visible emitters. Also, the coherence generation process can be transferred to other frequency conversion processes including optical parametric oscillation, sum/difference frequency generation, and third-harmonic generation.