Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Vehicle Re-Identification using CNN Latent Spaces: Metrics Comparison and Track-to-track Extension

This paper addresses the problem of vehicle re-identification using distance comparison of images in CNN latent spaces. First, we study the impact of the distance metrics, comparing performances obtained with different metrics: the minimal Euclidean distance (MED), the minimal cosine distance (MCD), and the residue of the sparse coding reconstruction (RSCR). These metrics are applied using features extracted through five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3 and DenseNet201. We use the specific vehicle re-identification dataset VeRI to fine-tune these CNNs and evaluate results. In overall, independently from the CNN used, MCD outperforms MED, commonly used in the literature. Secondly, the state-of-the-art image-to-track process (I2TP) is extended to a track-to-track process (T2TP) without using complementary metadata. Metrics are extended to measure distance between tracks, enabling the evaluation of T2TP and comparison with I2TP using the same CNN models. Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining DenseNet201 and MCD-based metrics exhibits the best performances, outperforming the state-of-the-art I2TP models that use complementary metadata. Finally, our experiments highlight two main results: i) the importance of the metric choice for vehicle re-identification, and ii) T2TP improves the performances compared to I2TP, especially when coupled with MCD-based metrics.
Latent Variable Model for Multivariate Data with Measure-specific Sample Weights and Its Application in Hospital Compare

We developed a single factor model with measure-specific sample weights for multivariate data with multiple observed indicators clustered within a higher level subject. The factor is therefore a latent variable shared by multiple indicators within a same subject and the sample weights are different across different indicators and different subjects. Even after integrating out the latent variable, the likelihood of the data cannot be written as the sum of weighted likelihood of each subject because a subject has different sample weights respectively for its multiple indicators. In addition, the number of available indicators varies across subjects. We derive a pseudo likelihood for the latent variable model with measure-specific weights. We investigate various statistical properties of the latent variable model with measure-specific sample weights and its connection to the traditional factor analysis. We found that the latent variable model provides consistent estimates for its variances when the measure-specific sample weights are properly re-scaled. Two estimation procedures are developed - EM algorithm for the pseudo likelihood and marginalization of the pseudo likelihood by directly integrating out the latent variable to obtain the parameter estimates. This approach is illustrated by the analysis of publicly reported hospitals with indicators and sample weights. Numerical studies are conducted to investigate the influence of weights and their sample distribution.
User-Aware Folk Popularity Rank: User-Popularity-Based Tag Recommendation That Can Enhance Social Popularity

In this paper we propose a method that can enhance the social popularity of a post (i.e., the number of views or likes) by recommending appropriate hash tags considering both content popularity and user popularity. A previous approach called FolkPopularityRank (FP-Rank) considered only the relationship among images, tags, and their popularity. However, the popularity of an image/video is strongly affected by who uploaded it. Therefore, we develop an algorithm that can incorporate user popularity and users' tag usage tendency into the FP-Rank algorithm. The experimental results using 60,000 training images with their accompanying tags and 1,000 test data, which were actually uploaded to a real social network service (SNS), show that, in ten days, our proposed algorithm can achieve 1.2 times more views than the FP-Rank algorithm. This technology would be critical to individual users and companies/brands who want to promote themselves in SNSs.
An Improved Historical Embedding without Alignment

Many words have evolved in meaning as a result of cultural and social change. Understanding such changes is crucial for modelling language and cultural evolution. Low-dimensional embedding methods have shown promise in detecting words' meaning change by encoding them into dense vectors. However, when exploring semantic change of words over time, these methods require the alignment of word embeddings across different time periods. This process is computationally expensive, prohibitively time consuming and suffering from contextual variability. In this paper, we propose a new and scalable method for encoding words from different time periods into one dense vector space. This can greatly improve performance when it comes to identifying words that have changed in meaning over time. We evaluated our method on dataset from Google Books N-gram. Our method outperformed three other popular methods in terms of the number of words correctly identified to have changed in meaning. Additionally, we provide an intuitive visualization of the semantic evolution of some words extracted by our method
Robustness of Delta Hedging in a Jump-Diffusion Model

Suppose an investor aims at Delta hedging a European contingent claim $h(S(T))$ in a jump-diffusion model, but incorrectly specifies the stock price's volatility and jump sensitivity, so that any hedging strategy is calculated under a misspecified model. When does the erroneously computed strategy super-replicate the true claim in an appropriate sense? If the misspecified volatility and jump sensitivity dominate the true ones, we show that following the misspecified Delta strategy does super-replicate $h(S(T))$ in expectation among a wide collection of models. We also show that if a robust pricing operator with a whole class of models is used, the corresponding hedge is dominating the contingent claim under each model in expectation. Our results rely on proving stochastic flow properties of the jump-diffusion and the convexity of the value function. In the pure Poisson case, we establish that an overestimation of the jump sensitivity results in an almost sure one-sided hedge. Moreover, in general the misspecified price of the option dominates the true one if the volatility and the jump sensitivity are overestimated.
Constructing Privacy Channels from Information Channels

Data privacy protection studies how to query a dataset while preserving the privacy of individuals whose sensitive information is contained in the dataset. The information privacy model protects the privacy of an individual by using a noisy channel, called privacy channel, to filter out most information of the individual from the query's output. This paper studies how to construct privacy channels, which is challenging since it needs to evaluate the maximal amount of disclosed information of each individual contained in the query's output, called individual channel capacity. Our main contribution is an interesting result which can transform the problem of evaluating a privacy channel's individual channel capacity, which equals the problem of evaluating the capacities of an infinite number of channels, into the problem of evaluating the capacities of a finite number of channels. This result gives us a way to utilize the results in the information theory to construct privacy channels. As some examples, it is used to construct several basic privacy channels, such as the random response privacy channel, the exponential privacy channel and the Gaussian privacy channel, which are respective counterparts of the random response mechanism, the exponential mechanism and the Gaussian mechanism of differential privacy.
Secrecy and Covert Communications Against UAV Surveillance via Multi-Hop Networks

The deployment of unmanned aerial vehicle (UAV) for surveillance and monitoring gives rise to the confidential information leakage challenge in both civilian and military environments. The security and covert communication problems for a pair of terrestrial nodes against UAV surveillance are considered in this paper. To overcome the information leakage and increase the transmission reliability, a multi-hop relaying strategy is deployed. We aim to optimize the throughput by carefully designing the parameters of the multi-hop network, including the coding rates, transmit power, and required number of hops. In the secure transmission scenario, the expressions of the connection probability and secrecy outage probability of an end-to-end path are derived and the closed-form expressions of the optimal transmit power, transmission and secrecy rates under a fixed number of hops are obtained. In the covert communication problem, under the constraints of the detection error rate and aggregate power, the sub-problem of transmit power allocation is a convex problem and can be solved numerically. Simulation shows the impact of network settings on the transmission performance. The trade-off between secrecy/covertness and efficiency of the multi-hop transmission is discussed which leads to the existence of the optimal number of hops.
Early multiwavelength analysis of the recurrent nova V745 Sco

In recent years, several nova explosions have been detected by Fermi/LAT at E > 100 MeV, mainly early after the explosion and for a short period of time. The first evidence of particle acceleration in novae was found in the 2006 eruption of RS Oph to explain the faster than expected deceleration of the blast wave. As a consequence, emission of high-energy gamma-rays mainly from neutral pion decay and inverse Compton scattering is expected. We aim to understand the early shock evolution, when acceleration of particles can take place, in nova explosions. To achieve this goal, we perform a multiwavelength study of the 2014 outburst of V745 Sco, a symbiotic recurrent nova similar to RS Oph. The analysis of early Swift/XRT observations, simultaneous to the tentative Fermi detection, is combined with Chandra and NuStar data, to get a global picture of the nova ejecta and the red giant wind evolution. Early radio and IR data are also compiled, providing information about the forward shock velocity and its magnetic field. The comparison with the plasma properties of RS Oph shows striking similarities, such as the skipping of the adiabatic phase occurring in supernova remnants, a hint of particle acceleration. The multiwavelength study of V745 Sco provides new insights into the evolution of the hot plasma in novae and its interaction with the circumstellar material, a powerful tool to understand the nature of the high-energy gamma-ray emission from symbiotic recurrent novae.
Coordinated Joint Multimodal Embeddings for Generalized Audio-Visual Zero-shot Classification and Retrieval of Videos

We present an audio-visual multimodal approach for the task of zero-shot learning (ZSL) for classification and retrieval of videos. ZSL has been studied extensively in the recent past but has primarily been limited to visual modality and to images. We demonstrate that both audio and visual modalities are important for ZSL for videos. Since a dataset to study the task is currently not available, we also construct an appropriate multimodal dataset with 33 classes containing 156, 416 videos, from an existing large scale audio event dataset. We empirically show that the performance improves by adding audio modality for both tasks of zero-shot classification and retrieval, when using multi-modal extensions of embedding learning methods. We also propose a novel method to predict the ‘dominant’ modality using a jointly learned modality attention network. We learn the attention in a semi-supervised setting and thus do not require any additional explicit labelling for the modalities. We provide qualitative validation of the modality specific attention, which also successfully generalizes to unseen test classes.
Multi-player Multi-Armed Bandits with non-zero rewards on collisions for uncoordinated spectrum access

In this paper, we study the uncoordinated spectrum access problem using the multi-player multi-armed bandits framework. We consider a model where there is no central control and the users cannot communicate with each other. The environment may appear differently to different users, \textit{i.e.}, the mean rewards as seen by different users for a particular channel may be different. Additionally, in case of a collision, we allow for the colliding users to receive non-zero rewards. With this setup, we present a policy that achieves expected regret of order $O(\log^{2+\delta}{T})$ for some $\delta > 0$.
Invariance principles for local times in regenerative settings

Consider a stochastic process $\mathfrak{X}$, regenerative at a state $x$ which is instantaneous and regular. Let $L$ be a regenerative local time for $\mathfrak{X}$ at $x$. Suppose furthermore that $\mathfrak{X}$ can be approximated by discrete time regenerative processes $\mathfrak{X}^n$ for which $x$ is accesible. We give conditions on $\mathfrak{X}$ and $\mathfrak{X}^n$ so that the naturally defined local time of $\mathfrak{X}^n$ converges weakly to $L$. This limit theorem generalizes previous invariance principles that have appeared in the literature.
Are white dwarf magnetic fields in close binaries generated during common-envelope evolution?

Understanding the origin of the magnetic fields in white dwarfs (WDs) has been a puzzle for decades. A scenario that has gained considerable attention in the past years assumes that such magnetic fields are generated through a dynamo process during common-envelope evolution. We performed binary population models using an up-to-date version of the bse code to confront the predictions of this model with observational results. We found that this hypothesis can explain only the observed distribution of WD magnetic fields in polars and pre-polars and the low-temperature WDs in pre-polars if it is re-scaled to fit the observational data. Furthermore, in its present version, the model fails to explain the absence of young, close detached WD+M-dwarf binaries harbouring hot magnetic WDs and predicts that the overwhelming majority of WDs in close binaries should be strongly magnetic, which is also in serious conflict with the observations. We conclude that either the common-envelope dynamo scenario needs to be substantially revised or a different mechanism is responsible for the generation of strong WD magnetic fields in close binaries.
SpatialFlow: Bridging All Tasks for Panoptic Segmentation

Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues for segmenting and helps the network better understand the scene. How to integrate object location in both thing and stuff segmentation is a crucial problem. In this article, we propose spatial information flows to achieve this objective. The flows can bridge all sub-tasks in panoptic segmentation by delivering the object’s spatial context from the box regression task to others. More importantly, we design four parallel sub-networks to get a preferable adaptation of object spatial information in sub-tasks. Upon the sub-networks and the flows, we present a location-aware and unified framework for panoptic segmentation, denoted as SpatialFlow. We perform a detailed ablation study on each component and conduct extensive experiments to prove the effectiveness of SpatialFlow. Furthermore, we achieve state-of-the-art results, which are 47.9 PQ and 62.5 PQ respectively on MS-COCO and Cityscapes panoptic benchmarks.
MSO-Definable Regular Model Checking

Regular Model Checking (RMC) is a symbolic model checking technique where the set of system states are expressed as regular languages over strings and the transition relation is expressed using rational string-to-string relations. RMC permits verification of non-trivial properties in systems with infinite state spaces. We introduce monadic second-order logic (MSO) definable regular model checking (MSO-RMC), a framework that generalizes RMC by enabling the modeling of systems with more complex transition relations which are definable using nondeterministic MSO-definable string-to-string transformations. While MSO-RMC is in general undecidable, we recover decidability of the bounded model checking problem within this framework. For this decidability result, we introduce nondeterministic streaming $\omega$-string transducers and establish their expressive equivalence to nondeterministic MSO-definable $\omega$-string transformations. We also proof of the decidability of the regular type checking problem for nondeterministic streaming string transducers, both in the setting of finite strings and $\omega$-strings. Since MSO-definable relations are closed under composition, this result implies decidability of the bounded model checking in MSO-RMC.
Good, Better, Best: Textual Distractors Generation for Multi-Choice VQA via Policy Gradient

Textual distractors in current multi-choice VQA datasets are not challenging enough for state-of-the-art neural models. To better assess whether well-trained VQA models are vulnerable to potential attack such as more challenging distractors, we introduce a novel task called \textit{textual Distractors Generation for VQA} (DG-VQA). The goal of DG-VQA is to generate the most confusing distractors in multi-choice VQA tasks represented as a tuple of image, question, and the correct answer. Consequently, such distractors expose the vulnerability of neural models. We show that distractor generation can be formulated as a Markov Decision Process, and present a reinforcement learning solution to unsupervised produce distractors. Our solution addresses the lack of large annotated corpus issue in classical distractor generation methods. Our proposed model receives reward signals from well-trained multi-choice VQA models and updates its parameters via policy gradient. The empirical results show that the generated textual distractors can successfully confuse several cutting-edge models with an average 20% accuracy drop from around 64%. Furthermore, we conduct extra adversarial training to improve the robustness of VQA models by incorporating the generated distractors. The experiment validates the effectiveness of adversarial training by showing a performance improvement of 27% for the multi-choice VQA task
Maximum Probability Principle and Black-Box Priors

We present an axiomatic way of assigning probabilities to probabilistic models. In particular, we quantify an upper bound for probability of a model or in terms of information theory, a lower bound for amount of information that is assumed in a model. In our setup, maximizing probabilities of models is equivalent to removing assumptions or information stored in the model. Furthermore, we represent the problem of learning from an alternative view where the underlying probability space is considered directly. In this perspective both the true underlying model (Oracle) and the model at hand are events. subsequently, learning is presented in three perspectives: maximizing the likelihood of oracle given model, intersection of model and the oracle and symmetric difference complement of model and the oracle.
Top-Down Shape Abstraction Based on Greedy Pole Selection

Motivated by the fact that the medial axis transform is able to encode the shape completely, we propose to use as few medial balls as possible to approximate the original enclosed volume by the boundary surface. We progressively select new medial balls, in a top-down style, to enlarge the region spanned by the existing medial balls. The key spirit of the selection strategy is to encourage large medial balls while imposing given geometric constraints. We further propose a speedup technique based on a provable observation that the intersection of medial balls implies the adjacency of power cells (in the sense of the power crust). We further elaborate the selection rules in combination with two closely related applications. One application is to develop an easy-to-use ball-stick modeling system that helps non-professional users to quickly build a shape with only balls and wires, but any penetration between two medial balls must be suppressed. The other application is to generate porous structures with convex, compact (with a high isoperimetric quotient) and shape-aware pores where two adjacent spherical pores may have penetration as long as the mechanical rigidity can be well preserved.
Fast Exact Matrix Completion: A Unifying Optimization Framework

We consider the problem of matrix completion of rank $k$ on an $n\times m$ matrix. We show that both the general case and the case with side information can be formulated as a combinatorical problem of selecting $k$ vectors from $p$ column features. We demonstrate that it is equivalent to a separable optimization problem that is amenable to stochastic gradient descent. We design fastImpute, based on projected stochastic gradient descent, to enable efficient scaling of the algorithm of sizes of $10^5 \times 10^5$. We report experiments on both synthetic and real-world datasets that show fastImpute is competitive in both the accuracy of the matrix recovered and the time needed across all cases. Furthermore, when a high number of entries are missing, fastImpute is over $75\%$ lower in MAPE and $10$x faster than current state-of-the-art matrix completion methods in both the case with side information and without.
CreditPrint: Credit Investigation via Geographic Footprints by Deep Learning

Credit investigation is critical for financial services. Whereas, traditional methods are often restricted as the employed data hardly provide sufficient, timely and reliable information. With the prevalence of smart mobile devices, peoples' geographic footprints can be automatically and constantly collected nowadays, which provides an unprecedented opportunity for credit investigations. Inspired by the observation that locations are somehow related to peoples' credit level, this research aims to enhance credit investigation with users' geographic footprints. To this end, a two-stage credit investigation framework is designed, namely CreditPrint. In the first stage, CreditPrint explores regions' credit characteristics and learns a credit-aware embedding for each region by considering both each region's individual characteristics and cross-region relationships with graph convolutional networks. In the second stage, a hierarchical attention-based credit assessment network is proposed to aggregate the credit indications from a user's multiple trajectories covering diverse regions. The results on real-life user mobility datasets show that CreditPrint can increase the credit investigation accuracy by up to 10% compared to baseline methods.
Two-Dimensional Ferroelastic Semiconductors in Nb2SiTe4 and Nb2GeTe4 with Promising Electronic Properties.

Two-dimensional crystals with coupling of ferroelasticity and attractive electronic properties offer unprecedent opportunities for achieving long-sought controllable devices. But so far, the reported proposals are mainly based on hypothetical structures. Here, using first-principles calculations, we identify single-layer Nb2ATe4 (A = Si, Ge), which could be exfoliated from its layered bulk, is a promising candidate. Single-layer Nb2ATe4 is found to be dynamically, thermally and chemically stable. It possesses excellent ferroelasticity with high reversible ferroelastic strain and moderate ferroelastic transition energy barrier, beneficial for practical applications. Meanwhile, it harbors outstanding anisotropic electronic properties, including anisotropic carrier mobility and optical properties. More importantly, the anisotropic properties of single-layer Nb2ATe4 can be efficiently controlled through ferroelastic switching. These appealing properties combined with the experimental feasibility render single-layer Nb2ATe4 an extraordinary platform for realizing controllable devices.