Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Genetic Algorithm with Type-Aware Chromosomes for Traveling Salesman Problems with Drone

There are emerging transportation problems known as the Traveling Salesman Problem with Drone (TSPD) and the Flying Sidekick Traveling Salesman Problem (FSTSP) that involve the use of a drone in conjunction with a truck for package delivery. This study represents a hybrid genetic algorithm for solving TSPD and FSTSP by combining local search methods and dynamic programming. Similar algorithms exist in the literature. Our algorithm, however, considers more sophisticated chromosomes and simpler dynamic programming to enable broader exploration by the genetic algorithm and efficient exploitation through dynamic programming and local searches. The key contribution of this paper is the discovery of how decision-making processes should be divided among the layers of genetic algorithm, dynamic programming, and local search. In particular, our genetic algorithm generates the truck and the drone sequences separately and encodes them in a type-aware chromosome, wherein each customer is assigned to either the truck or the drone. We apply local searches to each chromosome, which is decoded by dynamic programming for fitness evaluation. Our dynamic programming algorithm merges the two sequences by determining optimal launch and landing locations for the drone to construct a TSPD solution represented by the chromosome. We propose novel type-aware order crossover operations and effective local search methods. A strategy to escape from local optima is proposed. Our new algorithm is shown to outperform existing algorithms on most benchmark instances in both quality and time. Our algorithms found the new best solutions for 538 TSPD instances out of 920 and 93 FSTSP instances out of 132.
Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning Platforms

Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address these limitations, we designed a control mechanism and a visualisation of the control’s impact through an iterative design process with adolescents and teachers. Then, we investigated how these functionalities affect adolescents’ trust in an e-learning platform that recommends maths exercises. A randomised controlled experiment with 76 middle school and high school adolescents showed that visualising the impact of exercised control significantly increases trust. Furthermore, having control over their mastery level seemed to inspire adolescents to reasonably challenge themselves and reflect upon the underlying recommendation algorithm. Finally, a significant increase in perceived transparency suggested that visualising steering actions can indirectly explain why recommendations are suitable, which opens interesting research tracks for the broader field of explainable AI.
StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training

In this paper, we present StrucTexTv2, an effective document image pre-training framework, by performing masked visual-textual prediction. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling, based on text region-level image masking. The proposed method randomly masks some image regions according to the bounding box coordinates of text words. The objectives of our pre-training tasks are reconstructing the pixels of masked image regions and the corresponding masked tokens simultaneously. Hence the pre-trained encoder can capture more textual semantics in comparison to the masked image modeling that usually predicts the masked image patches. Compared to the masked multi-modal modeling methods for document image understanding that rely on both the image and text modalities, StrucTexTv2 models image-only input and potentially deals with more application scenarios free from OCR pre-processing. Extensive experiments on mainstream benchmarks of document image understanding demonstrate the effectiveness of StrucTexTv2. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction under the end-to-end scenario.
AR-Assisted Surgical Care via 5G networks for First Aid Responders

Surgeons should play a central role in disaster planning and management due to the overwhelming number of bodily injuries that are typically involved during most forms of disaster. In fact, various types of surgical procedures are performed by emergency medical teams after sudden-onset disasters, such as soft tissue wounds, orthopaedic traumas, abdominal surgeries, etc. HMD-based Augmented Reality (AR), using state-of-the-art hardware such as the Magic Leap or the Microsoft HoloLens, have long been foreseen as a key enabler for clinicians in surgical use cases, especially for procedures performed outside of the operating room. This paper describes the Use Case (UC)"AR-assisted emergency surgical care", identified in the context of the 5G-EPICENTRE EU-funded project. Specifically, the UC will experiment with holographic AR technology for emergency medical surgery teams, by overlaying deformable medical models directly on top of the patient body parts, effectively enabling surgeons to see inside (visualizing bones, blood vessels, etc.) and perform surgical actions following step-by-step instructions. The goal is to combine the computational and data-intensive nature of AR and Computer Vision algorithms with upcoming 5G network architectures deployed for edge computing so as to satisfy real-time interaction requirements and provide an efficient and powerful platform for the pervasive promotion of such applications. By developing the necessary Virtual Network Functions (VNFs) to manage data-intensive services (e.g., prerendering, caching, compression) and by exploiting available network resources and Multi-access Edge Computing (MEC) support, provided by the 5G-EPICENTRE infrastructure, this UC aims to provide powerful AR-based tools, usable on site, to first-aid responders.
Composite Optimization Algorithms for Sigmoid Networks

Abstract In this letter, we use composite optimization algorithms to solve sigmoid networks. We equivalently transfer the sigmoid networks to a convex composite optimization and propose the composite optimization algorithms based on the linearized proximal algorithms and the alternating direction method of multipliers. Under the assumptions of the weak sharp minima and the regularity condition, the algorithm is guaranteed to converge to a globally optimal solution of the objective function even in the case of nonconvex and nonsmooth problems. Furthermore, the convergence results can be directly related to the amount of training data and provide a general guide for setting the size of sigmoid networks. Numerical experiments on Franke’s function fitting and handwritten digit recognition show that the proposed algorithms perform satisfactorily and robustly.
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation

We introduce MuAViC, a multilingual audio-visual corpus for robust speech recognition and robust speech-to-text translation providing 1200 hours of audio-visual speech in 9 languages. It is fully transcribed and covers 6 English-to-X translation as well as 6 X-to-English translation directions. To the best of our knowledge, this is the first open benchmark for audio-visual speech-to-text translation and the largest open benchmark for multilingual audio-visual speech recognition. Our baseline results show that MuAViC is effective for building noise-robust speech recognition and translation models. We make the corpus available at https://github.com/facebookresearch/muavic.
Research on Unknown Static Parameter Estimation Problem of State Space Models Based on Particle Filter Algorithm

A novel parameter estimation method for unknown static parameters of the state space model using particle filtering (PF) has proposed in this paper. Traditional methods enlarge state vector by treating the unknown parameter θ as a part of state vector (xk,θ) . But this may cause the degeneration of θ, when some estimates become too small to continue as a result of the non-dynamic character of parameters if θ at time k is only determined by time k-1. Compared to traditional methods, this novel method assumes that the posterior distribution of θ is given by previous observation and state vectors, z1:k and x1:k. Obtain statistics at time k by using the integration of z1:k and x1:k, and solve parameter estimation problem by updating θ recursively. Good results are obtained when this method is used in different models.
Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization

Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approximated surrogates for the fairness metrics in the objective as regularization terms. Although each of the learning schemes has its own strength in terms of applicability or performance, respectively, it is difficult for any method in the either category to be considered as a gold standard since their successful performances are typically limited to specific cases. To that end, we propose a principled method, dubbed as \ours, which unifies the two learning schemes by incorporating a well-justified group fairness metric into the training objective using a class wise distributionally robust optimization (DRO) framework. We then develop an iterative optimization algorithm that minimizes the resulting objective by automatically producing the correct re-weights for each group. Our experiments show that FairDRO is scalable and easily adaptable to diverse applications, and consistently achieves the state-of-the-art performance on several benchmark datasets in terms of the accuracy-fairness trade-off, compared to recent strong baselines.
Asymmetric Learning for Graph Neural Network based Link Prediction

Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper, we first analyze the computation complexity of existing GNN-LP methods, revealing that one reason for the scalability problem stems from their symmetric learning strategy in applying the same class of GNN models to learn representation for both head nodes and tail nodes. We then propose a novel method, called asymmetric learning (AML), for GNN-LP. More specifically, AML applies a GNN model to learn head node representation while applying a multi-layer perceptron (MLP) model to learn tail node representation. To the best of our knowledge, AML is the first GNN-LP method to adopt an asymmetric learning strategy for node representation learning. Furthermore, we design a novel model architecture and apply a row-wise mini-batch sampling strategy to ensure promising model accuracy and training efficiency for AML. Experiments on three real large-scale datasets show that AML is 1.7 × ∼ 7.3 × faster in training than baselines with a symmetric learning strategy while having almost no accuracy loss.
Asymptotic stability of solitons of the subcritical gKdV equations revisited

We consider the generalized Korteweg–de Vries (gKdV) equations in the subcritical cases p = 2, 3 or 4. The first objective of this paper is to present a direct, simplified proof of the asymptotic stability of solitons in the energy space H1, which was proved by the same authors in Martel and Merle (2001 Arch. Ration. Mech. Anal. 157 219–54). Then, in the case of the KdV equation, we show the optimality of the result by constructing a solution which behaves asymptotically as a soliton located on a curve which is a logarithmic perturbation of a line. This example justifies the apparently weak control on the location of the soliton in the asymptotic stability result.
Mixed Order Phase Transitions

Mixed order phase transitions are transitions which have common features with both first order and second order transitions. I review some results obtained in the context of one of the prototypical models of mixed order transitions, the one-dimensional Ising model with long-range coupling that decays as truncated inverse square distance between spins. The correspondence between this model and the Poland Scheraga model of DNA denaturation, a subject to which Michael Fisher made substantial contribution, is then outlined.
Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment

Evaluating the quality of facial images is essential for operating face recognition systems with sufficient accuracy. The recent advances in face quality standardisation (ISO/IEC CD3 29794-5) recommend the usage of component quality measures for breaking down face quality into its individual factors, hence providing valuable feedback for operators to re-capture low-quality images. In light of recent advances in 3D-aware generative adversarial networks, we propose a novel dataset, Syn-YawPitch, comprising 1000 identities with varying yaw-pitch angle combinations. Utilizing this dataset, we demonstrate that pitch angles beyond 30 degrees have a significant impact on the biometric performance of current face recognition systems. Furthermore, we propose a lightweight and explainable pose quality predictor that adheres to the draft international standard of ISO/IEC CD3 29794-5 and benchmark it against state-of-the-art face image quality assessment algorithms
Treat Different Negatives Differently: Enriching Loss Functions with Domain and Range Constraints for Link Prediction

Knowledge graph embedding models (KGEMs) are used for various tasks related to knowledge graphs (KGs), including link prediction. They are trained with loss functions that consider batches of true and false triples. However, different kinds of false triples exist and recent works suggest that they should not be valued equally, leading to specific negative sampling procedures. In line with this recent assumption, we posit that negative triples that are semantically valid w.r. t. signatures of relations (domain and range) are high-quality negatives. Hence, we enrich the three main loss functions for link prediction such that all kinds of negatives are sampled but treated differently based on their semantic validity. In an extensive and controlled experimental setting, we show that the proposed loss functions systematically provide satisfying results which demonstrates both the generality and superiority of our proposed approach. In fact, the proposed loss functions (1) lead to better MRR and Hits@10 values, and (2) drive KGEMs towards better semantic correctness as measured by the Sem@K metric. This highlights that relation signatures globally improve KGEMs, and thus should be incorporated into loss functions. Domains and ranges of relations being largely available in schema-defined KGs, this makes our approach both beneficial and widely usable in practice.
(MICRO)DETECTION OF SINGLE PHOTON INTERFERENCE AT BEAM SPLITTER

In this work we theoretically suggest and consider a new realistic experiment of single photon interference at beam splitter where micro-detector of the photon trajectories is presented by a pair of distant identical atoms. It admits studying of the quantum entanglement between photon and detector (Schrödinger cat effect) and modeling of the detection procedure (collapse) by spontaneous unitary symmetry (superposition) breaking. (Practically, collapse can be considered as an especial case of the general formalism of spontaneous symmetry breaking.) Finally, the same model of the collapse is applied at detection of single photon interference by photographic plate. It admits a simple solution of the quantum mechanics foundation problem
Non-linear bi-algebraic curves and surfaces in moduli spaces of Abelian differentials

The strata of the moduli spaces of Abelian differentials are non-homogenous spaces carrying natural bi-algebraic structures. Partly inspired by the case of homogenous spaces carrying bi-algebraic structures (such as torii, Abelian varieties and Shimura varieties), Klingler and Lerer recently showed that any bi-algebraic curve in a stratum of the moduli space of Abelian differentials is linear provided that the so-called condition $(\star)$ is fulfilled. In this note, we construct a non-linear bi-algebraic curve, resp. surface, of Abelian differentials of genus $7$, resp. $10$.
Stagnation Flow in Thin Streamer Boundaries

Slow solar wind is believed to arise in the legs or near the cusp of streamers, inside the brightness boundary. In an earlier study, we used an analytic model of flow in this layer to analyze the effect of the magnetic field on the geometry of the flow. That study successfully described those conditions that can lead to a decrease of the flow speed with increasing height near the cusp of the closed magnetic helmet inside a streamer. The model was, however, restricted to a radial brightness boundary on the streamer and hence to a relatively thick outflow region near the cusp. Here this restriction is relaxed through the explicit introduction of a coronal hole-like region outside the brightness boundary. We use the model to describe flow solutions for outflow in a thin layer inside the brightness boundary. The flow geometry now can be constrictive just above the cusp, and we show solutions of this type. Many solutions then show a diverging geometry at greater heights above the cusp, out to at least 5 R☉. We fail to find solutions in which the geometry alone leads to slow flow but give a more general description than before of conditions favoring slow flow and, consequently, gravitational settling in the legs of streamers.
Is Planted Coloring Easier than Planted Clique?

We study the computational complexity of two related problems: recovering a planted $q$-coloring in $G(n,1/2)$, and finding efficiently verifiable witnesses of non-$q$-colorability (a.k.a. refutations) in $G(n,1/2)$. Our main results show hardness for both these problems in a restricted-but-powerful class of algorithms based on computing low-degree polynomials in the inputs. The problem of recovering a planted $q$-coloring is equivalent to recovering $q$ disjoint planted cliques that cover all the vertices -- a potentially easier variant of the well-studied planted clique problem. Our first result shows that this variant is as hard as the original planted clique problem in the low-degree polynomial model of computation: each clique needs to have size $k \gg \sqrt{n}$ for efficient recovery to be possible. For the related variant where the cliques cover a $(1-\epsilon)$-fraction of the vertices, we also show hardness by reduction from planted clique. Our second result shows that refuting $q$-colorability of $G(n,1/2)$ is hard in the low-degree polynomial model when $q \gg n^{2/3}$ but easy when $q \lesssim n^{1/2}$, and we leave closing this gap for future work. Our proof is more subtle than similar results for planted clique and involves constructing a non-standard distribution over $q$-colorable graphs. We note that while related to several prior works, this is the first work that explicitly formulates refutation problems in the low-degree polynomial model. The proofs of our main results involve showing low-degree hardness of hypothesis testing between an appropriately constructed pair of distributions. For refutation, we show completeness of this approach: in the low-degree model, the refutation task is precisely as hard as the hardest associated testing problem, i.e., proving hardness of refutation amounts to finding a"hard"distribution.
Baroclinic Instability of Realistic Zonal-Mean States to Planetary Waves1

Abstract It is demonstrated that realistic zonal mean states on a sphere are unstable to perturbations of zonal wavenumbers 2 and 3. Moreover, these waves have significant growth rates and are of such a narrow meridional scale that they fall into the class of unstable modes first discovered by Charney. For zonal mean wind cross sections characteristic of the Southern Hemisphere, wavenumber 2 has an unstable mode with an e-folding time of about 9 days and a period of about 2 weeks. The growth rate of the unstable wave 2 is relatively insensitive to changes in wind structure and to topography representative of the Antarctic continent. The unstable waves extend well into the stratosphere, and it is suggested that the eastward traveling wave 2 and wave 3 components observed in the Southern Hemisphere may be baroclinically unstable modes of the Charney type. The relatively large growth rates of these waves are made possibly by a relatively narrow meridional scale in the troposphere. and their propagation into .
Modeling Low- and High-Frequency Noise in Transmon Qubits with Resource-Efficient Measurement

Transmon qubits experience open system effects that manifest as noise at a broad range of frequencies. We present a model of these effects using the Redfield master equation with a hybrid bath consisting of low and high-frequency components. We use two-level fluctuators to simulate 1/f-like noise behavior, which is a dominant source of decoherence for superconducting qubits. By measuring quantum state fidelity under free evolution with and without dynamical decoupling (DD), we can fit the low- and high-frequency noise parameters in our model. We train and test our model using experiments on quantum devices available through IBM quantum experience. Our model accurately predicts the fidelity decay of random initial states, including the effect of DD pulse sequences. We compare our model with two simpler models and confirm the importance of including both high-frequency and 1/f noise in order to accurately predict transmon behavior.
Positive real analysis and synthesis of uncertain discrete time systems

The positive real analysis and synthesis problems for a class of uncertain discrete-time systems are considered. The uncertainties are expressed in a linear fractional form. The analysis problem is to characterize conditions for a which the uncertain discrete-time system is positive real for all admissible uncertainties. A linear matrix inequality (LMI)-based analysis result is derived. The synthesis problem is to design linear state-feedback and dynamic output-feedback controllers that robustly stabilize the uncertain discrete-time system and achieve the strict positive realness for the closed-loop transfer functions. It has been established that the problems above can be cast into a class of parameterized strict positive real control problems without uncertainties.