Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles

Mobile edge computing has become an effective and fundamental paradigm for futuristic autonomous vehicles to offload computing tasks. However, due to the high mobility of vehicles, the dynamics of the wireless conditions, and the uncertainty of the arrival computing tasks, it is difficult for a single vehicle to determine the optimal offloading strategy. In this paper, we propose a Digital Twin (DT) empowered task offloading framework for Internet of Vehicles. As a software agent residing in the cloud, a DT can obtain both global network information by using communications among DTs, and historical information of a vehicle by using the communications within the twin. The global network information and historical vehicular information can significantly facilitate the offloading. In specific, to preserve the precious computing resource at different levels for most appropriate computing tasks, we integrate a learning scheme based on the prediction of futuristic computing tasks in DT. Accordingly, we model the offloading scheduling process as a Markov Decision Process (MDP) to minimize the long-term cost in terms of a trade off between task latency, energy consumption, and renting cost of clouds. Simulation results demonstrate that our algorithm can effectively find the optimal offloading strategy, as well as achieve the fast convergence speed and high performance, compared with other existing approaches.
Basket-based Softmax

Softmax-based losses have achieved state-of-the-art performances on various tasks such as face recognition and re-identification. However, these methods highly relied on clean datasets with global labels, which limits their usage in many real-world applications. An important reason is that merging and organizing datasets from various temporal and spatial scenarios is usually not realistic, as noisy labels can be introduced and exponential-increasing resources are required. To address this issue, we propose a novel mining-during-training strategy called Basket-based Softmax (BBS) as well as its parallel version to effectively train models on multiple datasets in an end-to-end fashion. Specifically, for each training sample, we simultaneously adopt similarity scores as the clue to mining negative classes from other datasets, and dynamically add them to assist the learning of discriminative features. Experimentally, we demonstrate the efficiency and superiority of the BBS on the tasks of face recognition and re-identification, with both simulated and real-world datasets.
Hiding Behind Backdoors: Self-Obfuscation Against Generative Models

Attack vectors that compromise machine learning pipelines in the physical world have been demonstrated in recent research, from perturbations to architectural components. Building on this work, we illustrate the self-obfuscation attack: attackers target a pre-processing model in the system, and poison the training set of generative models to obfuscate a specific class during inference. Our contribution is to describe, implement and evaluate a generalized attack, in the hope of raising awareness regarding the challenge of architectural robustness within the machine learning community.
Reconstruction for the time-dependent coefficients of a quasilinear dynamical Schr{\"o}dinger equation

We study an inverse problem related to the dynamical Schrödinger equation in a bounded domain of Rn, n ≥ 2. Since the concerned non-linear Schrödinger equation possesses a trivial solution, we linearize the equation around the trivial solution. Demonstrating the well-posedness of the direct problem under appropriate conditions on initial and boundary data, it is observed that the solution admits ε-expansion. By taking into account the fact that the terms O(|∇u(t, x)|) are negligible in this context, we shall reconstruct the time-dependent coefficients such as electric potential and vectorvalued function associated with quadratic nonlinearity from the knowledge of input-output map using the geometric optics solution and Fourier inversion.
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension

Robust subspace recovery (RSR) is a fundamental problem in robust representation learning. Here we focus on a recently proposed RSR method termed Dual Principal Component Pursuit (DPCP) approach, which aims to recover a basis of the orthogonal complement of the subspace and is amenable to handling subspaces of high relative dimension. Prior work has shown that DPCP can provably recover the correct subspace in the presence of outliers, as long as the true dimension of the subspace is known. We show that DPCP can provably solve RSR problems in the {\it unknown} subspace dimension regime, as long as orthogonality constraints -- adopted in previous DPCP formulations -- are relaxed and random initialization is used instead of spectral one. Namely, we propose a very simple algorithm based on running multiple instances of a projected sub-gradient descent method (PSGM), with each problem instance seeking to find one vector in the null space of the subspace. We theoretically prove that under mild conditions this approach will succeed with high probability. In particular, we show that 1) all of the problem instances will converge to a vector in the nullspace of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the nullspace of the subspace thus also revealing its true unknown codimension. We provide empirical results that corroborate our theoretical results and showcase the remarkable implicit rank regularization behavior of PSGM algorithm that allows us to perform RSR without being aware of the subspace dimension.
Shape-Consistent Generative Adversarial Networks for Multi-Modal Medical Segmentation Maps

Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very different characteristics. Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation. We present a segmentation network using synthesised cardiac volumes for extremely limited datasets. Our solution is based on a 3D cross-modality generative adversarial network to share information between modalities and generate synthesized data using unpaired datasets. Our network utilizes semantic segmentation to improve generator shape consistency, thus creating more realistic synthesised volumes to be used when re-training the segmentation network. We show that improved segmentation can be achieved on small datasets when using spatial augmentations to improve a generative adversarial network. These augmentations improve the generator capabilities, thus enhancing the performance of the Segmentor. Using only 16 CT and 16 MRI cardiovascular volumes, improved results are shown over other segmentation methods while using the suggested architecture. Our code is publicly available1.
IMO3: Interactive Multi-Objective Off-Policy Optimization

Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. However, we consider a more practical but challenging setting of unknown objective functions. In industry, optimization under this setting is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose Interactive Multi-Objective Off-policy Optimization (IMO^3). The key idea of IMO^3 is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO^3 identifies a near-optimal policy with high probability, depending on the amount of designer's feedback and training data for off-policy estimation. We demonstrate its effectiveness empirically on several multi-objective optimization problems.
Probing Multiphase Gas in Local Massive Elliptical Galaxies via Multiwavelength Observations

We investigate the cold and warm gas content, kinematics, and spatial distribution of six local massive elliptical galaxies to probe the origin of the multiphase gas in their atmospheres. We report new observations, including Stratospheric Observatory for Infrared Astronomy [C ii], Atacama Large Millimeter/submillimeter Array CO, Multi Unit Spectroscopic Explorer (MUSE) Hα+[N ii], and Very Large Array (VLA) radio observations. These are complemented by a large suite of multiwavelength archival data sets, including thermodynamical properties of the hot gas and radio jets, which are leveraged to investigate the role of active galactic nucleus (AGN) feeding/feedback in regulating the multiphase gas content. Our galactic sample shows a significant diversity in cool gas content, spanning filamentary and rotating structures. In our noncentral galaxies, the distribution of such gas is often concentrated, at variance with the more extended features observed in central galaxies. Misalignment between the multiphase gas and stars suggest that stellar mass loss is not the primary driver. A fraction of the cool gas might be acquired via galaxy interactions, but we do not find quantitative evidence of mergers in most of our systems. Instead, key evidence supports the origin via condensation out of the diffuse halo. Comparing with chaotic cold accretion (CCA) simulations, we find that our cool gas-free galaxies are likely in the overheated phase of the self-regulated AGN cycle, while for our galaxies with cool gas, the k-plot and AGN power correlation corroborate the phase of CCA feeding in which the condensation rain is triggering more vigorous AGN heating. The related C-ratio further shows that central/noncentral galaxies are expected to generate an extended/inner rain, consistent with our sample.
Emotion-based Modeling of Mental Disorders on Social Media

According to the World Health Organization (WHO), one in four people will be affected by mental disorders at some point in their lives. However, in many parts of the world, patients do not actively seek professional diagnosis because of stigma attached to mental illness, ignorance of mental health and its associated symptoms. In this paper, we propose a model for passively detecting mental disorders using conversations on Reddit. Specifically, we focus on a subset of mental disorders that are characterized by distinct emotional patterns (henceforth called emotional disorders): major depressive, anxiety, and bipolar disorders. Through passive (i.e., unprompted) detection, we can encourage patients to seek diagnosis and treatment for mental disorders. Our proposed model is different from other work in this area in that our model is based entirely on the emotional states, and the transition between these states of users on Reddit, whereas prior work is typically based on content-based representations (e.g., n-grams, language model embeddings, etc). We show that content-based representation is affected by domain and topic bias and thus does not generalize, while our model, on the other hand, suppresses topic-specific information and thus generalizes well across different topics and times. We conduct experiments on our model’s ability to detect different emotional disorders and on the generalizability of our model. Our experiments show that while our model performs comparably to content-based models, such as BERT, it generalizes much better across time and topic.
Online Self-Calibration for Visual-Inertial Navigation Systems: Models, Analysis and Degeneracy

In this paper, we study in-depth the problem of online self-calibration for robust and accurate visual-inertial state estimation. In particular, we first perform a complete observability analysis for visual-inertial navigation systems (VINS) with full calibration of sensing parameters, including IMU and camera intrinsics and IMU-camera spatial-temporal extrinsic calibration, along with readout time of rolling shutter (RS) cameras (if used). We investigate different inertial model variants containing IMU intrinsic parameters that encompass most commonly used models for low-cost inertial sensors. The observability analysis results prove that VINS with full sensor calibration has four unobservable directions, corresponding to the system's global yaw and translation, while all sensor calibration parameters are observable given fully-excited 6-axis motion. Moreover, we, for the first time, identify primitive degenerate motions for IMU and camera intrinsic calibration. Each degenerate motion profile will cause a set of calibration parameters to be unobservable and any combination of these degenerate motions are still degenerate. Extensive Monte-Carlo simulations and real-world experiments are performed to validate both the observability analysis and identified degenerate motions, showing that online self-calibration improves system accuracy and robustness to calibration inaccuracies. We compare the proposed online self-calibration on commonly-used IMUs against the state-of-art offline calibration toolbox Kalibr, and show that the proposed system achieves better consistency and repeatability. Based on our analysis and experimental evaluations, we also provide practical guidelines for how to perform online IMU-camera sensor self-calibration.
Magnetic Spirals in Accretion Flows Originated from Misaligned Magnetic Fields

Misalignment between rotation and magnetic fields has been suggested to be one type of physical mechanism that can ease the effects of magnetic braking during the collapse of cloud cores leading to the formation of protostellar disks. However, its essential factors are poorly understood. Therefore, we perform a more detailed analysis of the physics involved. We analyze existing simulation data to measure the system torques, mass accretion rates, and Toomre Q parameters. We also examine the presence of shocks in the system. While advective torques are generally the strongest, we find that magnetic and gravitational torques can play substantial roles in how angular momentum is transferred during the disk formation process. Magnetic torques can shape the accretion flows, creating two-armed magnetized inflow spirals aligned with the magnetic field. We find evidence of an accretion shock that is aligned according to the spiral structure of the system. Inclusion of ambipolar diffusion as explored in this work has shown a slight influence in the small-scale structures but not in the main morphology. We discuss potential candidate systems where some of these phenomena could be present.
Disentangling Style and Speaker Attributes for TTS Style Transfer

End-to-end neural TTS has shown improved performance in speech style transfer. However, the improvement is still limited by the available training data in both target styles and speakers. Additionally, degenerated performance is observed when the trained TTS tries to transfer the speech to a target style from a new speaker with an unknown, arbitrary style. In this paper, we propose a new approach to seen and unseen style transfer training on disjoint, multi-style datasets, i. e. , datasets of different styles are recorded, one individual style by one speaker in multiple utterances. An inverse autoregressive flow (IAF) technique is first introduced to improve the variational inference for learning an expressive style representation. A speaker encoder network is then developed for learning a discriminative speaker embedding, which is jointly trained with the rest neural TTS modules. The proposed approach of seen and unseen style transfer is effectively trained with six specifically-designed objectives: reconstruction loss, adversarial loss, style distortion loss, cycle consistency loss, style classification loss, and speaker classification loss. Experiments demonstrate, both objectively and subjectively, the effectiveness of the proposed approach for seen and unseen style transfer tasks. The performance of our approach is superior to and more robust than those of four other reference systems of prior art.
Constraining the Baryon Loading Factor of AGN Jets: Implication from the γ-Ray Emission of the Coma Cluster

High-energy cosmic rays (CRs) can be accelerated in the relativistic jets of active galactic nuclei (AGNs) powered by supermassive black holes. The baryon loading efficiency onto relativistic CR baryons from the accreting black holes is poorly constrained by observations so far. In this paper, we suggest that the γ-ray emission of galaxy clusters can be used to study the baryon loading factor of AGN jets, since CRs injected by AGN jets are completely confined in the galaxy clusters and sufficiently interact with the intracluster medium via a hadronic process, producing diffuse γ-rays. We study the propagation of CRs in the galaxy clusters and calculate the radial distribution of the γ-rays in the galaxy cluster with different injection rates from AGNs. By comparison with the γ-ray flux and upper limits of the Coma cluster measured by Fermi-LAT and VERITAS, we find the upper limit of the average baryon loading factor (defined as the efficiency with which the gravitational energy is converted into relativistic particles) to be η p,grav < 0.1. The upper limit is much lower than that required to account for diffuse neutrino flux in the conventional blazar models.
Differential Geometry in Neural Implicits

We introduce a neural implicit framework that bridges discrete diﬀerential geometry of triangle meshes and continuous diﬀerential geometry of neural implicit surfaces. It exploits the diﬀerentiable properties of neural networks and the discrete geometry of triangle meshes to approximate them as the zero-level sets of neural implicit functions . To train a neural implicit function, we propose a loss function that allows terms with high-order derivatives, such as the alignment between the principal directions, to learn more geometric details. During training, we consider a non-uniform sampling strategy based on the discrete curvatures of the triangle mesh to access points with more geometric details. This sampling implies faster learning while preserving geometric accuracy. We present the analytical diﬀerential geometry formulas for neural surfaces, such as normal vectors and curvatures. We use them to render the surfaces using sphere tracing. Additionally, we propose a network optimization based on singular value decomposition to reduce the number of parameters.
LTC-GIF: Attracting More Clicks on Feature-length Sports Videos

This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from full-length sports videos. In addition, instead of processing the entire video, small video segments are processed to generate artistic media. This makes the proposed approach more computationally efficient compared to the baseline approaches that create artistic media using the entire video. The proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data used during artistic media generation. When extensive experiments were conducted on the Nvidia Jetson TX2, the computational complexity of the proposed method was 3.57 times lower than that of the SoA method. In the qualitative assessment, GIFs generated using the proposed method received 1.02 higher overall ratings compared to the SoA method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services even on resource-constrained devices.
Imputing Missing Values in the Occupational Requirements Survey

The U.S. Bureau of Labor Statistics allows public access to much of the data acquired through its Occupational Requirements Survey (ORS). This data can be used to draw inferences about the requirements of various jobs and job classes within the United States workforce. However, the dataset contains a multitude of missing observations and estimates, which somewhat limits its utility. Here, we propose a method by which to impute these missing values that leverages many of the inherent features present in the survey data, such as known population limit and correlations between occupations and tasks. An iterative regression fit, implemented with a recent version of XGBoost and executed across a set of simulated values drawn from the distribution described by the known values and their standard deviations reported in the survey, is the approach used to arrive at a distribution of predicted values for each missing estimate. This allows us to calculate a mean prediction and bound said estimate with a 95% confidence interval. We discuss the use of our method and how the resulting imputations can be utilized to inform and pursue future areas of study stemming from the data collected in the ORS. 1 Finally, we conclude with an outline of WIGEM, a generalized version of our weighted, iterative imputation algorithm that could be applied to other contexts.
ASCNet: Action Semantic Consistent Learning of Arbitrary Progress Levels for Early Action Prediction

—Early action prediction aims to recognize human actions from only a part of action execution, which is an important video analysis task for many practical applications. Most prior works treat partial or full videos as a whole, which neglects the semantic consistencies among partial videos of various progress levels due to their large intra-class variances. In contrast, we partition original partial or full videos to form a series of new partial videos and mine the Action Semantic Consistent Knowledge (ASCK) among these new partial videos evolving in arbitrary progress levels. Moreover, a novel Action Semantic Consistent learning network (ASCNet) under the teacher-student framework is proposed for early action prediction. Speciﬁcally, we treat partial videos as nodes and their action semantic consistencies as edges. Then we build a bi-directional fully connected graph for the teacher network and a single-directional fully connected graph for the student network to model ASCK among partial videos. The MSE and MMD losses are incorporated as our distillation loss to further transfer the ASCK from the teacher to the student network. Extensive experiments and ablative studies have been conducted, demonstrating the effectiveness of modeling ASCK for early action prediction. With the proposed ASCNet, we have achieved state-of-the-art performance on two benchmarks. The code will be released if the paper is accepted.
Reinforcement Learning for Personalized Drug Discovery and Design for Complex Diseases: A Systems Pharmacology Perspective

Many multi-genic systemic diseases such as neurological disorders, inflammatory diseases, and the majority of cancers do not have effective treatments yet. Reinforcement learning powered systems pharmacology is a potentially effective approach to design personalized therapies for untreatable complex diseases. In this survey, state-of-the-art reinforcement learning methods and their latest applications to drug design are reviewed. The challenges on harnessing reinforcement learning for systems pharmacology and personalized medicine are discussed. Potential solutions to overcome the challenges are proposed. In spite of successful application of advanced reinforcement learning techniques to target-based drug discovery, new reinforcement learning strategies are needed to address systems pharmacology-oriented personalized de novo drug design.