Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for TeV emission from the Fermi Bubbles at low Galactic latitudes with H.E.S.S. inner Galaxy survey observations

The Fermi Bubbles were discovered about a decade ago in the Fermi-LAT data as a double-lobe structure extending up to 55° in Galactic latitudes above and below the Galactic Center. At the moment their origin is still unknown. The H.E. S.S. collaboration is currently performing the first ever survey in TeV gamma rays of the Milky Way inner region: the Inner Galaxy Survey. This survey is intended to achieve the best sensitivity to faint and diffuse emissions in a region of several degrees around the Galactic Centre. It provides an unprecedented sensitivity to dark matter signals, new diffuse emissions, and TeV outflows from the Galactic Centre. Understanding the properties of the Fermi Bubbles at low Galactic latitudes will provide key insights into their origin. We search for TeV emission at the base of the Fermi Bubbles using low-latitude spatial templates. The first results obtained with the 2014-2020 H.E. S.S. observations will be reported.
Using Large Pre-Trained Models with Cross-Modal Attention for Multi-Modal Emotion Recognition

Recently, self-supervised pre-training has shown significant improvements in many areas of machine learning, including speech and NLP. We propose using large self-supervised pre-trained models for both audio and text modality with cross-modality attention for multimodal emotion recognition. We use Wav2Vec2.0 [1] as an audio encoder base for robust speech features extraction and the BERT model [2] as a text encoder base for better contextual representation of text. These high capacity models trained on large amounts of unlabeled data contain rich feature representations and improve the downstream task's performance. We use the cross-modal attention [3] mechanism to learn alignment between audio and text representations from self-supervised models. Cross-modal attention also helps in extracting interactive information between audio and text features. We obtain utterance-level feature representation from frame-level features using statistics pooling for both audio and text modality and combine them using the early fusion technique. Our experiments show that the proposed approach obtains a 1.88% absolute improvement in accuracy compared to the previous state-of-the-art method [3] on the IEMOCAP dataset [35]. We also conduct unimodal experiments for both audio and text modalities and compare them with previous best methods.
Big Data Analytics in Humanitarian and Disaster Operations: A Systematic Review

By the outset of this review, 168 million people needed humanitarian aid, and the number grew to 235 million by the end of the completion of this review. There is no time to lose, definitely no data to lose. Humanitarian relief is crucial not just to contend with a pandemic once a century but also to provide help during civil conflicts, ever-increasing natural disasters, and other forms of crisis. Reliance on technology has never been so relevant and critical than now. The creation of more data and advancements in data analytics provides an opportunity to the humanitarian field. This review aimed at providing a holistic understanding of big data analytics in a humanitarian and disaster setting. A systematic literature review method is used to examine the field and the results of this review explain research gaps, and opportunities available for future research. This study has shown a significant research imbalance in the disaster phase, highlighting how the emphasis is on responsive measures than preventive measures. Such reactionary measures would only exacerbate the disaster, as is the case in many nations with COVID-19. Overall this research details the current state of big data analytics in a humanitarian and disaster setting.
Spatial Transformer Networks for Curriculum Learning

Curriculum learning is a bio-inspired training technique that is widely adopted in machine learning for improved optimization and better training of neural networks regarding the convergence rate or obtained accuracy. The main concept in curriculum learning is to start the training with simpler tasks and gradually increase the level of difficulty. Therefore, a natural question is how to determine or generate these simpler tasks. In this work, we take inspiration from Spatial Transformer Networks (STNs) in order to form an easy-to-hard curriculum. As STNs have been proved capable of removing the clutter from the input images and obtaining higher accuracy in image classification tasks, we hypothesize that images processed by STNs can be seen as easier tasks and utilized in the interest of curriculum learning. To this end, we study multiple strategies developed for shaping the training curriculum, using the data generated by STNs. We perform various experiments on cluttered MNIST and Fashion-MNIST datasets, where on the former, we obtain an improvement of 3.8pp in classification accuracy compared to the baseline, indicating that STNs can be considered as a tool for generating the easy-to-hard training schedule required for curriculum learning.
ODAM: Object Detection, Association, and Mapping using Posed RGB Video

Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics. We present ODAM, a system for 3D Object Detection, Association, and Mapping using posed RGB videos. The proposed system relies on a deep learning front-end to detect 3D objects from a given RGB frame and associate them to a global object-based map using a graph neural network (GNN). Based on these frame-to-model associations, our back-end optimizes object bounding volumes, represented as super-quadrics, under multi-view geometry constraints and the object scale prior. We validate the proposed system on ScanNet where we show a significant improvement over existing RGB-only methods.
Convex Latent Effect Logit Model via Sparse and Low-rank Decomposition

In this paper, we propose a convex formulation for learning logistic regression model (logit) with latent heterogeneous effect on sub-population. In transportation, logistic regression and its variants are often interpreted as discrete choice models under utility theory (McFadden, 2001). Two prominent applications of logit models in the transportation domain are traffic accident analysis and choice modeling. In these applications, researchers often want to understand and capture the individual variation under the same accident or choice scenario. The mixed effect logistic regression (mixed logit) is a popular model employed by transportation researchers. To estimate the distribution of mixed logit parameters, a non-convex optimization problem with nested high-dimensional integrals needs to be solved. Simulation-based optimization is typically applied to solve the mixed logit parameter estimation problem. Despite its popularity, the mixed logit approach for learning individual heterogeneity has several downsides. First, the parametric form of the distribution requires domain knowledge and assumptions imposed by users, although this issue can be addressed to some extent by using a non-parametric approach. Second, the optimization problems arise from parameter estimation for mixed logit and the non-parametric extensions are non-convex, which leads to unstable model interpretation. Third, the simulation size in simulation-assisted estimation lacks finite-sample theoretical guarantees and is chosen somewhat arbitrarily in practice. To address these issues, we are motivated to develop a formulation that models the latent individual heterogeneity while preserving convexity, and avoids the need for simulation-based approximation. Our setup is based on decomposing the parameters into a sparse homogeneous component in the population and low-rank heterogeneous parts for each individual.
Containments in families with forbidden subposets

We consider the problem of determining the maximum number of pairs F ⊆ F ′ in a family F ⊆ 2[n] that avoids certain posets P of height 2. We show that for any such P the number of pairs is O(n ( n ⌊n/2⌋ ) ) and we find the exact value for the butterfly poset and the N poset. Also, we determine the asymptotics of the maximum number of pairs in containment for some posets of which the Hasse diagram is a path.
EEG-based Classification of Drivers Attention using Convolutional Neural Network

Accurate detection of a driver’s attention state can help develop assistive technologies that respond to unexpected hazards in real time and therefore improve road safety. This study compares the performance of several attention classifiers trained on participants’ brain activity. Participants performed a driving task in an immersive simulator where the car randomly deviated from the cruising lane. They had to correct the deviation and their response time was considered as an indicator of attention level. Participants repeated the task in two sessions; in one session they received kinesthetic feedback and in another session no feedback. Using their EEG signals, we trained three attention classifiers; a support vector machine (SVM) using EEG spectral band powers, and a Convolutional Neural Network (CNN) using either spectral features or the raw EEG data. Our results indicated that the CNN model trained on raw EEG data obtained under kinesthetic feedback achieved the highest accuracy (89%). While using a participant’s own brain activity to train the model resulted in the best performances, inter-subject transfer learning still performed high (75%), showing promise for calibration-free Brain-Computer Interface (BCI) systems. Our findings show that CNN and raw EEG signals can be employed for effective training of a passive BCI for real-time attention classification.
FEDI: Few-shot learning based on Earth Mover's Distance algorithm combined with deep residual network to identify diabetic retinopathy

Diabetic retinopathy(DR) is the main cause of blindness in diabetic patients. However, DR can easily delay the occurrence of blindness through the diagnosis of the fundus. In view of the reality, it is difficult to collect a large amount of diabetic retina data in clinical practice. This paper proposes a few-shot learning model of a deep residual network based on Earth Mover's Distance algorithm to assist in diagnosing DR. We build training and validation classification tasks for few-shot learning based on 39 categories of 1000 sample data, train deep residual networks, and obtain experience maximization pre-training models. Based on the weights of the pre-trained model, the Earth Mover's Distance algorithm calculates the distance between the images, obtains the similarity between the images, and changes the model's parameters to improve the accuracy of the training model. Finally, the experimental construction of the small sample classification task of the test set to optimize the model further, and finally, an accuracy of 93.5667% on the 3wayl0shot task of the diabetic retina test set. For the experimental code and results, please refer to: https://github.com/panliangrui/few-shot-learning-funds.
L-DQN: An Asynchronous Limited-Memory Distributed Quasi-Newton Method

This work proposes a distributed algorithm for solving empirical risk minimization problems, called L-DQN, under the master/worker communication model. L-DQN is a distributed limited-memory quasi-Newton method that supports asynchronous computations among the worker nodes. Our method is efficient both in terms of storage and communication costs, i.e., in every iteration, the master node and workers communicate vectors of size O(d), where d is the dimension of the decision variable, and the amount of memory required on each node is O(md), where m is an adjustable parameter. To our knowledge, this is the first distributed quasi-Newton method with provable global linear convergence guarantees in the asynchronous setting where delays between nodes are present. Numerical experiments are provided to illustrate the theory and the practical performance of our method.
Integer-arithmetic-only Certified Robustness for Quantized Neural Networks

Adversarial data examples have drawn significant attention from the machine learning and security communities. A line of work on tackling adversarial examples is certified robustness via randomized smoothing that can provide a theoretical robustness guarantee. However, such a mechanism usually uses floating-point arithmetic for calculations in inference and requires large memory footprints and daunting computational costs. These defensive models cannot run efficiently on edge devices nor be deployed on integer-only logical units such as Turing Tensor Cores or integer-only ARM processors. To overcome these challenges, we propose an integer randomized smoothing approach with quantization to convert any classifier into a new smoothed classifier, which uses integer-only arithmetic for certified robustness against adversarial perturbations. We prove a tight robustness guarantee under ℓ2-norm for the proposed approach. We show our approach can obtain a comparable accuracy and 4× ∼ 5× speedup over floating-point arithmetic certified robust methods on general-purpose CPUs and mobile devices on two distinct datasets (CIFAR-10 and Caltech-101).
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors

We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background. DSP-SLAM takes as input the 3D point cloud reconstructed by a feature-based SLAM system and equips it with the ability to enhance its sparse map with dense reconstructions of detected objects. Objects are detected via semantic instance segmentation, and their shape and pose are estimated using category-specific deep shape embeddings as priors, via a novel second order optimization. Our object-aware bundle adjustment builds a pose-graph to jointly optimize camera poses, object locations and feature points. DSP-SLAM can operate at 10 frames per second on 3 different input modalities: monocular, stereo, or stereo+LiDAR. We demonstrate DSP-SLAM operating at almost frame rate on monocular-RGB sequences from the Friburg and Redwood-OS datasets, and on stereo+LiDAR sequences on the KITTI odometry dataset showing that it achieves high-quality full object reconstructions, even from partial observations, while maintaining a consistent global map. Our evaluation shows improvements in object pose and shape reconstruction with respect to recent deep prior-based reconstruction methods and reductions in camera tracking drift on the KITTI dataset. More details and demonstrations are available at our project page: https://jingwenwang95.github. io/dsp-slam/
A Sparse Structure Learning Algorithm for Bayesian Network Identification from Discrete High-Dimensional Data

This paper addresses the problem of learning a sparse structure Bayesian network from high-dimensional discrete data. Compared to continuous Bayesian networks, learning a discrete Bayesian network is a challenging problem due to the large parameter space. Although many approaches have been developed for learning continuous Bayesian networks, few approaches have been proposed for the discrete ones. In this paper, we address learning Bayesian networks as an optimization problem and propose a score function that satisﬁes the sparsity and the DAG property simultaneously. Besides, we implement a block-wised stochastic coordinate descent algorithm to optimize the score function. Speciﬁcally, we use a variance reducing method in our optimization algorithm to make the al-gorithm work eﬃciently in high-dimensional data. The proposed approach is applied to synthetic data from well-known benchmark networks. The quality, scalability, and robustness of the constructed network are measured. Compared to some competitive approaches, the results reveal that our algorithm outperforms the others in evaluation metrics.
Self-Regulation for Semantic Segmentation

In this paper, we seek reasons for the two major failure cases in Semantic Segmentation (SS): 1) missing small objects or minor object parts, and 2) mislabeling minor parts of large objects as wrong classes. We have an interesting finding that Failure-1 is due to the underuse of detailed features and Failure-2 is due to the underuse of visual contexts. To help the model learn a better trade-off, we introduce several Self-Regulation (SR) losses for training SS neural networks. By "self", we mean that the losses are from the model per se without using any additional data or supervision. By applying the SR losses, the deep layer features are regulated by the shallow ones to preserve more details; meanwhile, shallow layer classification logits are regulated by the deep ones to capture more semantics. We conduct extensive experiments on both weakly and fully supervised SS tasks, and the results show that our approach consistently surpasses the baselines. We also validate that SR losses are easy to implement in various state-of-the-art SS models, e.g., SPGNet [7] and OCRNet [62], incurring little computational overhead during training and none for testing1.
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark And Lateral Inhibition Module For Prohibited Items Detection

Prohibited items detection in X-ray images often plays an important role in protecting public safety, which often deals with color-monotonous and luster-insufficient objects, resulting in unsatisfactory performance. Till now, there have been rare studies touching this topic due to the lack of specialized high-quality datasets. In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. It is the largest dataset of high quality for prohibited items detection, gathered from the real-world airport security inspection and annotated by professional security inspectors. Besides, for accurate prohibited item detection, we further propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information and focusing on identifiable characteristics, especially when objects are overlapped with each other. Specifically, LIM, the elaborately designed flexible additional module, suppresses the noisy information flowing maximumly by the Bidirectional Propagation (BP) module and activates the most identifiable charismatic, boundary, from four directions by Boundary Activation (BA) module. We evaluate our method extensively on HiXray and OPIXray and the results demonstrate that it outperforms SOTA detection methods.1
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning

—While recent works have indicated that federated learning (FL) is vulnerable to poisoning attacks by compromised clients, we show that these works make a number of unrealistic assumptions and arrive at somewhat misleading conclusions. For instance, they often use impractically high percentages of compromised clients or assume unrealistic capabilities for the adversary. We perform the ﬁrst critical analysis of poisoning attacks under practical production FL environments by carefully characterizing the set of realistic threat models and adversarial capabilities. Our ﬁndings are rather surprising: contrary to the established belief, we show that FL, even without any defenses, is highly robust in practice. In fact, we go even further and propose novel, state-of-the-art poisoning attacks under two realistic threat models, and show via an extensive set of experiments across three benchmark datasets how (in)effective poisoning attacks are, especially when simple defense mechanisms are used. We correct previous misconceptions and give concrete guidelines that we hope will encourage our community to conduct more accurate research in this space and build stronger (and more realistic) attacks and defenses.
Reddening and the shape of the variable component of the continua of active galactic nuclei from the optical to the far-ultraviolet. I.

We analyse the photometric variability of 4,611 active galactic nuclei (AGNs) from the Sloan Digital Sky Survey Stripe 82. We recover the spectral energy distribution (SED) of the variable flux as a function of wavelength. For rest wavelengths longer than ∼2200Å we find that the SED of the variable component of the bluest AGNs is consistent with the Fν∝ν+1/3 spectrum predicted for an externally-illuminated accretion disc. We confirm there is some residual variable emission corresponding to the ‘small blue bump’ and other broad-line region variability. We interpret steeper optical spectra of the variable component as being due to intrinsic reddening. This is supported by the correlation of the Balmer decrement with the colour excess of the variable component. We find the median internal reddening of SDSS AGNs in Stripe 82 with z < 0.4 to be E(B − V) ≈ 0.10 in agreement with the reddening derived from the Balmer decrement. Individual AGNs in the sample can have E(B − V) > 0.4.
Pulse-Width Modulation Neuron Implemented by Single Positive-Feedback Device

1 Abstract—Positive-feedback (PF) device and its operation scheme to implement pulse width modulation (PWM) function was proposed and demonstrated, and the device operation mechanism for implementing PWM function was analyzed. By adjusting the amount of the charge stored in the n floating body (Qn), the potential of the floating body linearly changes with time. When Qn reaches to a threshold value (Qth), the PF device turns on abruptly. From the linear time-varying property of Qn and the gate bias dependency of Qth, fully functionable PWM neuron properties including voltage to pulse width conversion and hard-sigmoid activation function were successfully obtained from a single PF device. A PWM neuron can be implemented by using a single PF device, thus it is beneficial to extremely reduce the area of a PWM neuron circuit than the previously reported one.
Charged BTZ-type solutions in Eddington-inspired Born-Infeld gravity

We construct an axially symmetric solution of Eddington-inspired Born-Infeld gravity coupled to an electromagnetic field in 2+1 dimensions including a (negative) cosmological constant term. This is achieved by using a recently developed mapping procedure that allows to generate solutions in certain families of metric-affine gravity theories starting from a known seed solution of General Relativity, which in the present case corresponds to the electrically charged Banados-Teitelboim-Zanelli (BTZ) solution. We discuss the main features of the new configurations, including the modifications to the ergospheres and horizons, the emergence of wormhole structures, and the consequences for the regularity (or not) of these space-times via geodesic completeness.
Sequence-to-Sequence Learning on Keywords for Efficient FAQ Retrieval

Frequently-Asked-Question (FAQ) retrieval provides an effective procedure for responding to user's natural language based queries. Such platforms are becoming common in enterprise chatbots, product question answering, and preliminary technical support for customers. However, the challenge in such scenarios lies in bridging the lexical and semantic gap between varied query formulations and the corresponding answers, both of which typically have a very short span. This paper proposes TI-S2S, a novel learning framework combining TF-IDF based keyword extraction and Word2Vec embeddings for training a Sequence-to-Sequence (Seq2Seq) architecture. It achieves high precision for FAQ retrieval by better understanding the underlying intent of a user question captured via the representative keywords. We further propose a variant with an additional neural network module for guiding retrieval via relevant candidate identification based on similarity features. Experiments on publicly available dataset depict our approaches to provide around 92% precision-at-rank-5, exhibiting nearly 13% improvement over existing approaches.