Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Congestion Control for Demand-Based Optimization in Precoded Multi-Beam High Throughput Satellite Communications

High-throughput satellite communication systems are growing in strategic importance thanks to their role in delivering broadband services to mobile platforms and residences and/or businesses in rural and remote regions globally. Although precoding has emerged as a prominent technique to meet ever-increasing user demands, there is a lack of studies dealing with congestion control. This paper enhances the performance of multi-beam high throughput geostationary satellite systems under congestion, where the users’ quality of service (QoS) demands cannot be fully satisfied with limited resources. In particular, we propose congestion control strategies, relying on simple power control schemes. We formulate a multi-objective optimization framework balancing the system sum-rate and the number of users satisfying their QoS requirements. Next, we propose two novel approaches that effectively handle the proposed multi-objective optimization problem. The former is a model-based approach that relies on the weighted sum method to enrich the number of satisfied users by solving a series of the sum-rate optimization problems in an iterative manner. The latter is a data-driven approach that offers a low-cost solution by utilizing supervised learning and exploiting the optimization structures as continuous mappings. The proposed general framework is evaluated for different linear precoding techniques, for which the low computational complexity algorithms are designed. Numerical results manifest that our proposed framework effectively handles the congestion issue and brings superior improvements of rate satisfaction to many users than previous works. Furthermore, the proposed algorithms show low run-time and make them realistic for practical systems.
A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment

Dietary studies showed that dietary problems such as obesity are associated with other chronic diseases, including hypertension, irregular blood sugar levels, and increased risk of heart attacks. The primary cause of these problems is poor lifestyle choices and unhealthy dietary habits, which are manageable using interactive mHealth apps. However, traditional dietary monitoring systems using manual food logging suffer from imprecision, underreporting, time consumption, and low adherence. Recent dietary monitoring systems tackle these challenges by automatic assessment of dietary intake through machine learning methods. This survey discusses the best-performing methodologies that have been developed so far for automatic food recognition and volume estimation. Firstly, the paper presented the rationale of visual-based methods for food recognition. Then, the core of the study is the presentation, discussion, and evaluation of these methods based on popular food image databases. In this context, this study discusses the mobile applications that are implementing these methods for automatic food logging. Our findings indicate that around 66.7% of surveyed studies use visual features from deep neural networks for food recognition. Similarly, all surveyed studies employed a variant of convolutional neural networks (CNN) for ingredient recognition due to recent research interest. Finally, this survey ends with a discussion of potential applications of food image analysis, existing research gaps, and open issues of this research area. Learning from unlabeled image datasets in an unsupervised manner, catastrophic forgetting during continual learning, and improving model transparency using explainable AI are potential areas of interest for future studies.
Universality for free fermions and the local Weyl law for semiclassical Schr\"odinger operators

We study local asymptotics for the spectral projector associated to a Schr\"odinger operator $-\hbar^2\Delta+V$ on $\mathbb{R}^n$ in the semiclassical limit as $\hbar\to0$. We prove local uniform convergence of the rescaled integral kernel of this projector towards a universal model, inside the classically allowed region as well as on its boundary. This implies universality of microscopic fluctuations for the corresponding free fermions (determinantal) point processes, both in the bulk and around regular boundary points. Our results apply for a general class of smooth potentials in arbitrary dimension $n\ge 1$. These results are complemented by studying both macroscopic and mesoscopic fluctuations of the point process. We obtain tail bounds for macroscopic linear statistics and, provided $n\geq 2$, a central limit theorem for both macroscopic and mesoscopic linear statistics in the bulk.
MoB2
 under pressure: Superconducting Mo enhanced by boron

The discovery of the first high critical temperature (Tc) transition metal diboride superconductor, MgB2 structure alpha-MoB2 under pressure with Tc up to 32 K at 100 GPa, provides new input into some unexplained aspects of electron-phonon coupling in intermetallic compounds. We establish that MoB2 is a phonon-mediated superconductor but has little in common with MgB2 (Tc=40 K at zero pressure). MoB2 is a strongly metallic, three dimensional, multi-Fermi surface material, becoming of additional interest because it displays a frequency separation of Mo and B vibrations that mirrors that of metal superhydrides with Tc approaching room temperature. This separation, which is unusual in intermetallic compounds, allows an analysis separately for Mo and B providing, amongst the other parameters essential for understanding phonon coupling, the matrix elements for scattering by the individual atoms. Strong coupling (lambda(Mo)=1.48) is provided by Mo (total lambda=1.67). A factor of 15 weaker coupling to each B atom is compensated by that coupling being to mean high frequency modes around 85-90 meV (maximum of 140 meV), versus 18-20 meV for Mo. As a result, B enhances Tc by 43% over the Mo-only value, to 33 K, corresponding to the experimental value. These results provide a guideline for designing higher Tc materials from a cooperation of strong coupling from heavy atoms with weakly coupled light atoms. The new high Tc paradigm discovered here highlights the need for studying and engineering larger ionic scattering matrix elements.
Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning

Designing optimal reward functions has been desired but extremely difficult in reinforcement learning (RL). When it comes to modern complex tasks, sophisticated reward functions are widely used to simplify policy learning yet even a tiny adjustment on them is expensive to evaluate due to the drastically increasing cost of training. To this end, we propose a hindsight reward tweaking approach by designing a novel paradigm for deep reinforcement learning to model the influences of reward functions within a near-optimal space. We simply extend the input observation with a condition vector linearly correlated with the effective environment reward parameters and train the model in a conventional manner except for randomizing reward configurations, obtaining a hyper-policy whose characteristics are sensitively regulated over the condition space. We demonstrate the feasibility of this approach and study one of its potential application in policy performance boosting with multiple MuJoCo tasks.
Automated Robustness with Adversarial Training as a Post-Processing Step

Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging. As a step towards practical automation, this work explores the efficacy of a simple post processing step in yielding robust deep learning model. To achieve this, we adopt adversarial training as a post-processing step for optimised network architectures obtained from a neural architecture search algorithm. Specific policies are adopted for tuning the hyperparameters of the different steps, resulting in a fully automated pipeline for generating adversarially robust deep learning models. We evidence the usefulness of the proposed pipeline with extensive experimentation across 11 image classification and 9 text classification tasks.
Some minimization problems for mean field models with competing forces

We review recent results on three families of minimization problems, defined on subsets of nonnegative functions with fixed integral. The competition between attractive and repulsive forces leads to transitions between parameter regimes, where minimizers exist and where they do not. The problems considered are generalized liquid drop models, swarming models and generalized Keller-Segel models.
The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021

This report describes our submission to the track 1 and track 2 of the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC 2021). Both track 1 and track 2 share the same speaker verification system, which only uses VoxCeleb2-dev as our training set. This report explores several parts, including data augmentation, network structures, domain-based large margin fine-tuning, and back-end refinement. Our system is a fusion of 9 models and achieves first place in these two tracks of VoxSRC 2021. The minDCF of our submission is 0.1034, and the corresponding EER is 1.8460%.
Investigating possible unification of Seyfert galaxies and blazars in
 Fermi
 ‐Large Area Telescope sample

Statistical analyses are invaluable methods used to understand intrinsic emission processes and the unification of extragalactic radio sources. In this paper, we collected radio, X-ray and γ-ray data of blazars from the Fermi-LAT and Seyfert galaxies from the INTEGRAL survey and investigated the relationship between the emission properties of Seyfert galaxies and blazar subclasses of flatspectrum radio quasars (FSRQs) and BL Lac subclasses (BL Lacs). The results show from the average values that these objects follow a sequence that is indicative of probable Seyfert galaxies BL Lacs FSRQs unification. We discovered that Seyfert galaxies, BL Lacs and FSRQs share similar emission characteristics in X-ray luminosity (LX-ray), suggestive of fundamental connection while the γ-ray and radio luminosities (Lγ-ray and Lradio) of Seyfert galaxies are the least powerful, signifying an evolving structure. From two-dimensional Kolmogorov-Smirnov test (K-S test), we found that Seyfert galaxies differ from the blazar subclasses in Lradio while there is no significant difference between them in LXray and Lγ-ray which implies that high energy emissions in Seyfert galaxies and blazar subclasses may come from the same emission mechanism. Significant positive correlations exist between the high energy luminosities (X-ray and γ-ray bands) and the low energy component (radio) within the whole sample of blazars and Seyfert galaxies implying a form of connection between them. These results are not only consistent with the prediction of the unified scheme for blazars but also show that Seyfert galaxies have evolutionary link with blazar subclasses.
Proto: A Neural Cocktail for Generating Appealing Conversations

In this paper, we present our Alexa Prize Grand Challenge 4 socialbot: Proto. Leveraging diverse sources of world knowledge, and powered by a suite of neural and rule-based natural language understanding modules, state-of-the-art neural generators, novel state-based deterministic generators, an ensemble of neural re-rankers, a robust post-processing algorithm, and an efficient overall conversation strategy, Proto strives to be able to converse coherently about a diverse range of topics of interest to humans, and provide a memorable experience to the user. In this paper we dissect and analyze the different components and conversation strategies implemented by our socialbot, which enables us to generate colloquial, empathetic, engaging, self-rectifying, factually correct, and on-topic response, which has helped us achieve consistent scores throughout the competition.
ViSTA: a Framework for Virtual Scenario-based Testing of Autonomous Vehicles

In this paper, we present ViSTA, a framework for Virtual Scenario-based Testing of Autonomous Vehicles (AV), developed as part of the 2021 IEEE Autonomous Test Driving AI Test Challenge. Scenario-based virtual testing aims to construct specific challenges posed for the AV to overcome, albeit in virtual test environments that may not necessarily resemble the real world. This approach is aimed at identifying specific issues that arise safety concerns before an actual deployment of the AV on the road. In this paper, we describe a comprehensive test case generation approach that facilitates the design of special-purpose scenarios with meaningful parameters to form test cases, both in automated and manual ways, leveraging the strength and weaknesses of either. Furthermore, we describe how to automate the execution of test cases, and analyze the performance of the AV under these test cases.
Terahertz conductivity of the highly mismatched amorphous alloy, GaNBi

We report terahertz optical conductivity measurements of the highly mismatched alloy, GaNBi. We find that in these amorphous GaNBi epilayers grown using plasma assisted molecular beam epitaxy, the optical conductivity is enhanced in the samples grown at higher gallium beam equivalent pressure (BEP). The optical conductivity spectra in these pseudo-amorphous epilayers follow a Drude–Smith behaviour due to charge confinement effects. The direct current conductivity in the epilayers grown at the highest Ga BEP (3.1 × 10−7 Torr) show an increase of three orders of magnitude compared to the one grown at the lowest Ga BEP (2.0 × 10−7 Torr). Our measurements suggests a percolative transition from an insulating nature in the GaNBi epilayers grown at low Ga BEP to a highly conducting phase in the epilayers grown at high Ga BEP.
Enhancing Language Models with Plug-and-Play Large-Scale Commonsense

We study how to enhance language models (LMs) with textual commonsense knowledge. Previous work (e.g., Know-BERT) has focused on the integrating entity knowledge from knowledge graphs. In order to introduce the external entity embeddings, they learn to jointly represent the original sentences and external knowledge by pre-training on a large scale corpus. However, when switching to textual commonsense, unlike the “light” entity embeddings, the encoding of commonsense descriptions is “heavy”. Therefore, the pre-training for learning to jointly represent the target sentence and exter-nal commonsense descriptions is unaffordable. On the other hand, since pre-trained LMs for representing the target sentences alone are readily available, is it feasible to introduce commonsense knowledge in downstream tasks by ﬁne-tuning them only? In this paper, we propose a plug-and-play method for large-scale commonsense integration without pre-training. Our method is inspired by the observation that in the regular ﬁne-tuning for downstream tasks where no external knowledge was introduced, the variation in the parameters of the language model was minor . Our method starts from a pre-trained LM that represents the target sentences only (e.g., BERT). We think that the pre-training for joint representation learning can be avoided, if the joint representation reduces the impact of parameters on the starting LM. Previous methods such as KnowBERT proposed complex modiﬁcations to the vanilla LM to introduce external knowledge. Our model (Cook-Transformer, commonsense knowledge-enhanced Transformer), on the other hand, hardly changes the vanilla LM except adding a knowledge to-ken in each Transformer layer. In a variety of experiments, COOK-Transformer-based BERT/RoBERTa improve their effect without any pre-training.
Sparse-MLP: A Fully-MLP Architecture with Conditional Computation

Mixture-of-Experts (MoE) with sparse conditional computation has been proved an effective architecture for scaling attention-based models to more parameters with comparable computation cost. In this paper, we propose Sparse-MLP, scaling the recent MLP-Mixer model with sparse MoE layers, to achieve a more computation-efﬁcient architecture. We replace a subset of dense MLP blocks in the MLP-Mixer model with Sparse blocks. In each Sparse block, we apply two stages of MoE layers: one with MLP experts mixing information within channels along image patch dimension, one with MLP experts mixing information within patches along the channel dimension. Besides, to reduce computational cost in routing and improve expert capacity, we design Re-represent layers in each Sparse block. These layers are to re-scale image representations by two simple but effective linear transformations. When pre-training on ImageNet-1k with MoCo v3 algorithm, our models can outperform dense MLP models by 2.5% on ImageNet Top-1 accuracy with fewer parameters and computational cost. On small-scale downstream image classiﬁcation tasks, i.e., Cifar10 and Cifar100, our Sparse-MLP can still achieve better performance than baselines.
Parsing Table Structures in the Wild

This paper tackles the problem of table structure parsing (TSP) from images in the wild. In contrast to existing studies that mainly focus on parsing well-aligned tabular images with simple layouts from scanned PDF documents, we aim to establish a practical table structure parsing system for real-world scenarios where tabular input images are taken or scanned with severe deformation, bending or occlusions. For designing such a system, we propose an approach named Cycle-CenterNet on the top of CenterNet with a novel cycle-pairing module to simultaneously detect and group tabular cells into structured tables. In the cycle-pairing module, a new pairing loss function is proposed for the network training. Alongside with our Cycle-CenterNet, we also present a large-scale dataset, named Wired Table in the Wild (WTW), which includes well-annotated structure parsing of multiple style tables in several scenes like photo, scanning files, web pages, etc. In experiments, we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy of table structure parsing on the new WTW dataset by 24.6% absolute improvement evaluated by the TEDS metric. A more comprehensive experimental analysis also validates the advantages of our proposed methods for the TSP task.
Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition

While deep learning has contributed to the advancement of sensor-based Human Activity Recognition (HAR), it is usually a costly and challenging supervised task with the needs of a large amount of labeled data. To alleviate this issue, contrastive learning has been applied for sensor-based HAR. Data augmentation is an essential part of contrastive learning and has a significant impact on the performance of downstream tasks. However, current popular augmentation methods do not achieve competitive performance in contrastive learning for sensor-based HAR. Motivated by this issue, we propose a new sensor data augmentation method by resampling, which simulates more realistic activity data by varying the sampling frequency to maximize the coverage of the sampling space. In addition, we extend MoCo, a popular contrastive learning framework, to MoCoHAR for HAR. The resampling augmentation method will be evaluated on two contrastive learning frameworks, SimCLRHAR and MoCoHAR, using UCI-HAR, MotionSensor, and USC-HAD datasets. The experiment results show that the resampling augmentation method outperforms all state-of-the-art methods under a small amount of labeled data, on SimCLRHAR and MoCoHAR, with mean F1-score as the evaluation metric. The results also demonstrate that not all data augmentation methods have positive effects in the contrastive learning framework.
Learning with Holographic Reduced Representations

Holographic Reduced Representations (HRR) are a method for performing symbolic AI on top of real-valued vectors by associating each vector with an abstract concept, and providing mathematical operations to manipulate vectors as if they were classic symbolic objects. This method has seen little use outside of older symbolic AI work and cognitive science. Our goal is to revisit this approach to understand if it is viable for enabling a hybrid neural-symbolic approach to learning as a differentiable component of a deep learning architecture. HRRs today are not effective in a differentiable solution due to numerical instability, a problem we solve by introducing a projection step that forces the vectors to exist in a well behaved point in space. In doing so we improve the concept retrieval efficacy of HRRs by over $100\times$. Using multi-label classification we demonstrate how to leverage the symbolic HRR properties to develop an output layer and loss function that is able to learn effectively, and allows us to investigate some of the pros and cons of an HRR neuro-symbolic learning approach. Our code can be found at https://github.com/NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations
Optimized protocol for DNA extraction from ancient skeletal human remains using Chelex-100

PCR-based analysis of skeletonized human remains is a common aspect in both forensic human identification as well as Ancient DNA research. In this, both areas not merely utilize very similar methodology, but also share the same problems regarding quantity and quality of recovered DNA and presence of inhibitory substances in samples from excavated remains. To enable amplification based analysis of the remains, development of optimized DNA extraction procedures is thus a critical factor in both areas.The study here presents an optimized protocol for DNA extraction from ancient skeletonized remains using Chelex-100, which proved to be effective in yielding amplifiable extracts from sample material excavated after centuries in a soil environment, which consequently have high inhibitor content and overall limited DNA preservation. Success of the optimization strategies utilized is shown in significantly improved amplification outcomes compared to the predecessor method
Timbre Transfer with Variational Auto Encoding and Cycle-Consistent Adversarial Networks

This work investigates the application of deep learning to timbre transfer. The adopted approach combines Variational Autoencoders with Generative Adversarial Networks to construct meaningful representations of the source audio and produce realistic generations of the target audio and is applied to the Flickr 8k Audio dataset for transferring the vocal timbre between speakers and the URMP dataset for transferring the musical timbre between instruments. Variations of the adopted approach were trained, and performance was compared using the metrics SSIM (Structural Similarity Index) and FAD (Frechét Audio Distance). It was found that a many-to-many approach supersedes a one-to-one approach in terms of reconstructive capabilities, while one-to-one showed better results in terms of adversarial translation. The adoption of a basic over a bottleneck residual block design is more suitable for enriching content information about a latent space, and the decision on whether cyclic loss takes on a variational autoencoder or vanilla auto encoder approach does not have a significant impact on reconstructive and adversarial translation aspects of the model.