Miracle-dz/UniFAR-FTUs huggingface dataset:
On Energy Allocation and Data Scheduling in Backscatter Networks With Multi-Antenna Readers

In this paper, we study the throughput utility functions in buffer-equipped monostatic backscatter communication networks with multi-antenna Readers. In the considered model, the backscatter nodes (BNs) store the data in their buffers before transmission to the Reader. We investigate three utility functions, namely, the sum, the proportional and the common throughput. We design online admission policies, corresponding to each utility function, to determine how much data can be admitted in the buffers. Moreover, we propose an online data link control policy for jointly controlling the transmit and receive beamforming vectors as well as the reflection coefficients of the BNs. The proposed policies for data admission and data link control jointly optimize the throughput utility, while stabilizing the buffers. We adopt the min-drift-plus-penalty (MDPP) method in designing the control policies. Following the MDPP method, we cast the optimal data link control and the data admission policies as solutions of two independent optimization problems which should be solved in each time slot. The optimization problem corresponding to the data link control is non-convex and does not have a trivial solution. Using Lagrangian dual and quadratic transforms, we find a closed-form iterative solution. Finally, we use the results on the achievable rates of finite blocklength codes to study the system performance in the cases with short packets. As demonstrated, the proposed policies achieve optimal utility and stabilize the data buffers in the BNs.
Square Root Bundle Adjustment for Large-Scale Reconstruction

We propose a new formulation for the bundle adjustment problem which relies on nullspace marginalization of landmark variables by QR decomposition. Our approach, which we call square root bundle adjustment, is algebraically equivalent to the commonly used Schur complement trick, improves the numeric stability of computations, and allows for solving large-scale bundle adjustment problems with single-precision floating-point numbers. We show in real-world experiments with the BAL datasets that even in single precision the proposed solver achieves on average equally accurate solutions compared to Schur complement solvers using double precision. It runs significantly faster, but can require larger amounts of memory on dense problems. The proposed formulation relies on simple linear algebra operations and opens the way for efficient implementations of bundle adjustment on hardware platforms optimized for single-precision linear algebra processing.
Spatial Sampling Design to Improve the Efficiency of the Estimation of the Critical Parameters of the SARS-CoV-2 Epidemic

Abstract Given the urgent informational needs connected with the diffusion of infection with regard to the COVID-19 pandemic, in this article, we propose a sampling design for building a continuous-time surveillance system. Compared with other observational strategies, the proposed method has three important elements of strength and originality: (1) it aims to provide a snapshot of the phenomenon at a single moment in time, and it is designed to be a continuous survey that is repeated in several waves over time, taking different target variables during different stages of the development of the epidemic into account; (2) the statistical optimality properties of the proposed estimators are formally derived and tested with a Monte Carlo experiment; and (3) it is rapidly operational as this property is required by the emergency connected with the diffusion of the virus. The sampling design is thought to be designed with the diffusion of SAR-CoV-2 in Italy during the spring of 2020 in mind. However, it is very general, and we are confident that it can be easily extended to other geographical areas and to possible future epidemic outbreaks. Formal proofs and a Monte Carlo exercise highlight that the estimators are unbiased and have higher efficiency than the simple random sampling scheme.
The Rediscovery Hypothesis: Language Models Need to Meet Linguistics

There is an ongoing debate in the NLP community whether modern language models contain linguistic knowledge, recovered through so-called probes. In this paper, we study whether linguistic knowledge is a necessary condition for the good performance of modern language models, which we call the rediscovery hypothesis. In the first place, we show that language models that are significantly compressed but perform well on their pretraining objectives retain good scores when probed for linguistic structures. This result supports the rediscovery hypothesis and leads to the second contribution of our paper: an information-theoretic framework that relates language modeling objectives with linguistic information. This framework also provides a metric to measure the impact of linguistic information on the word prediction task. We reinforce our analytical results with various experiments, both on synthetic and on real NLP tasks in English.
Deep learning-based synthetic CT generation from MR images: comparison of generative adversarial and residual neural networks

Background : Currently, MRI - only radiotherapy (RT) eliminates some of the concerns about using CT images in RT chains such as the registration of MR images to a separate CT, extra dose delivery, and the additional cost of repeated imaging. However, one remaining challenge is that the signal intensities of MRI are not related to the attenuation coefficient of the biological tissue. This work compares the performance of two state - of - the - art deep learning models; a generative adversarial network (GAN) and a residual network (ResNet) for synthetic CTs (sCT) generation from MR images. Materials and Methods: The brain MR and CT images of 86 participants were analyzed. GAN and ResNet models were implemented for the generation of synthetic CTs from the 3D T1 - weighted MR images using a six - fold cross - validation scheme. The resulting sCTs were compared, considering the CT images as a reference using standard metrics such as the mean absolute error (MAE), peak signal - to - noise - ratio (PSNR) and the structural similarity index (SSIM). Results: Overall, the ResNet model exhibited higher accuracy in relation to the delineation of brain tissues. The ResNet model estimated the CT values for the entire head region with an MAE of 114.1±27.5 HU compared to MAE= - 10.9±147.0 HU obtained from the GAN model. Moreover, both models offered comparable SSIM and PSNR values, although the ResNet method exhibited a slightly superior performance over the GAN method. Conclusion: We compared two state - of - the - art deep learning models for the task of MR - based sCT generation. The ResNet model exhibited superior results, thus demonstrating its potential to be used for the challenge of synthetic CT generation in PET/MR AC and MR - only RT planning.
Neural Code Summarization

Code summarization is the task of generating readable summaries that are semantically meaningful and can accurately describe the presumed task of a software. Program comprehension has become one of the most tedious tasks for knowledge transfer. As the codebase evolves over time, the description needs to be manually updated each time with the changes made. An automatic approach is proposed to infer such captions based on benchmarked and custom datasets with comparison between the original and generated results.
Spin-half bosonic classification

In this paper, we define a new spinor classification that encompasses the recently proposed spin-half bosons with mass dimension three-half (Ahluwalia D. V., EPL, 131 (2020) 41001). As will be shown, these particles, which are governed by a first-order equation and consequently provide a local theory, belong to a specific subclass of class-2 spinors within the bosonic classification, playing a similar role to that of Dirac spinors within the Lounesto classification. Such bosonic classification is shown to be closely connected with the usual fermionic Lounesto classification, and thus, evincing a symmetry between Dirac fermions and spin-half bosons.
Towards 6G with Connected Sky: UAVs and Beyond

The large-scale and ever-growing use of unmanned aerial vehicles (UAVs) in a wide range of applications is foreseen to be a major part of beyond 5G and 6G wireless networks in the next decade. The effective support of such massive deployment of UAVs requires offering reliable, secure, and cost-effective wireless connectivity. In this regard, cellular networks play essential roles in serving UAVs acting as flying user equipments. While the cellular networks provide promising connectivity solutions for UAVs, enabling robust UAV operations faces several challenges. In this paper, an overview of key barriers and design considerations of widespread commercial use of flying UAVs are presented along with their potential solutions. In addition, we discuss how cellular networks can support UAVs by relying on their advanced features, network intelligence, key enabling technologies for beyond 5G and 6G, and exploiting new tools from machine learning. Finally, we shed light on offering wireless services to high altitudes and the integration of non-terrestrial networks with terrestrial networks towards limitless connectivity in 6G.
LEAF: Simulating Large Energy-Aware Fog Computing Environments

Despite constant improvements in efficiency, today’s data centers and networks consume enormous amounts of energy and this demand is expected to rise even further. An important research question is whether and how fog computing can curb this trend. As real-life deployments of fog infrastructure are still rare, a significant part of research relies on simulations. However, existing power models usually only target particular components such as compute nodes or battery-constrained edge devices.Combining analytical and discrete-event modeling, we develop a holistic but granular energy consumption model that can determine the power usage of compute nodes as well as network traffic and applications over time. Simulations can incorporate thousands of devices that execute complex application graphs on a distributed, heterogeneous, and resource-constrained infrastructure. We evaluated our publicly available prototype LEAF within a smart city traffic scenario, demonstrating that it enables research on energy-conserving fog computing architectures and can be used to assess dynamic task placement strategies and other energy-saving mechanisms.
Does Interaction Improve Bayesian Reasoning with Visualization?

Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed for evaluating whether allowing users to interact with a static visualization can improve their reasoning. Through two crowdsourced studies, we show that adding interaction to a static Bayesian reasoning visualization does not improve participants’ accuracy on a Bayesian reasoning task. In some cases, it can significantly detract from it. Moreover, we demonstrate that underlying visualization design modulates performance and that people with high versus low spatial ability respond differently to different interaction techniques and underlying base visualizations. Our work suggests that interaction is not as unambiguously good as we often believe; a well designed static visualization can be as, if not more, effective than an interactive one.
Electrical Tuning of Tin-Vacancy Centers in Diamond

Group-IV color centers in diamond have attracted significant attention as solid-state spin qubits because of their excellent optical and spin properties. Among these color centers, the tin-vacancy (SnV$^{\,\textrm{-}}$) center is of particular interest because its large ground-state splitting enables long spin coherence times at temperatures above 1$\,$K. However, color centers typically suffer from inhomogeneous broadening, which can be exacerbated by nanofabrication-induced strain, hindering the implementation of quantum nodes emitting indistinguishable photons. Although strain and Raman tuning have been investigated as promising techniques to overcome the spectral mismatch between distinct group-IV color centers, other approaches need to be explored to find methods that can offer more localized control without sacrificing emission intensity. Here, we study electrical tuning of SnV$^{\,\textrm{-}}$ centers in diamond via the direct-current Stark effect. We demonstrate a tuning range beyond 1.7$\,$GHz. We observe both quadratic and linear dependence on the applied electric field. We also confirm that the tuning effect we observe is a result of the applied electric field and is distinct from thermal tuning due to Joule heating. Stark tuning is a promising avenue toward overcoming detunings between emitters and enabling the realization of multiple identical quantum nodes.
Audio-Visual Speech Separation Using Cross-Modal Correspondence Loss

We present an audio-visual speech separation learning method that considers the correspondence between the separated signals and the visual signals to reflect the speech characteristics during training. Audio-visual speech separation is a technique to estimate the individual speech signals from a mixture using the visual signals of the speakers. Conventional studies on audio-visual speech separation mainly train the separation model on the audio-only loss, which reflects the distance between the source signals and the separated signals. However, conventional losses do not reflect the characteristics of the speech signals, including the speaker’s characteristics and phonetic information, which leads to distortion or remaining noise. To address this problem, we propose the cross-modal correspondence (CMC) loss, which is based on the cooccurrence of the speech signal and the visual signal. Since the visual signal is not affected by background noise and contains speaker and phonetic information, using the CMC loss enables the audio-visual speech separation model to remove noise while preserving the speech characteristics. Experimental results demonstrate that the proposed method learns the cooccurrence on the basis of CMC loss, which improves separation performance.
Universal-Prototype Augmentation for Few-Shot Object Detection

Few-shot object detection (FSOD) aims to strengthen the performance of novel object detection with few labeled samples. To alleviate the constraint of few samples, enhancing the generalization ability of learned features for novel objects plays a key role. Thus, the feature learning process of FSOD should focus more on intrinsical object characteristics, which are invariant under different visual changes and therefore are helpful for feature generalization. Unlike previous attempts of the meta-learning paradigm, in this paper, we explore how to smooth object features with intrinsi-cal characteristics that are universal across different object categories. We propose a new prototype, namely universal prototype, that is learned from all object categories. Besides the advantage of characterizing invariant characteristics, the universal prototypes alleviate the impact of unbalanced object categories. After augmenting object features with the universal prototypes, we impose a consistency loss to maximize the agreement between the augmented features and the original one, which is beneﬁcial for learning invariant object characteristics. Thus, we develop a new framework of few-shot object detection with universal prototypes ( FSOD up ) that owns the merit of feature generalization towards novel objects. Experimental results on PASCAL VOC and MS COCO demonstrate the effectiveness of FSOD up . Particularly, for the 1-shot case of VOC Split2, FSOD up outperforms the baseline by 6.8% in terms of mAP. More-over, we further verify FSOD up on a long-tail detection dataset, i.e., LVIS. And employing FSOD up outperforms
OmniNet: Omnidirectional Representations from Transformers

This paper proposes Omnidirectional Representations from Transformers (OmniNet). In OmniNet, instead of maintaining a strictly horizontal receptive field, each token is allowed to attend to all tokens in the entire network. This process can also be interpreted as a form of extreme or intensive attention mechanism that has the receptive field of the entire width and depth of the network. To this end, the omnidirectional attention is learned via a meta-learner, which is essentially another self-attention based model. In order to mitigate the computationally expensive costs of full receptive field attention, we leverage efficient self-attention models such as kernel-based (Choromanski et al.), low-rank attention (Wang et al.) and/or Big Bird (Zaheer et al.) as the meta-learner. Extensive experiments are conducted on autoregressive language modeling (LM1B, C4), Machine Translation, Long Range Arena (LRA), and Image Recognition. The experiments show that OmniNet achieves considerable improvements across these tasks, including achieving state-of-the-art performance on LM1B, WMT'14 En-De/En-Fr, and Long Range Arena. Moreover, using omnidirectional representation in Vision Transformers leads to significant improvements on image recognition tasks on both few-shot learning and fine-tuning setups.
Optimization of Growth Conditions for Test Fungus Cultures used in Direct Bioautographic TLC Detection. 3. Test Fungus: Candida albicans

Optimum conditions have been established for culture of the fungus Candida albicans (ATCC 90028) for microbial detection of zones in direct bioautographic TLC. Bioluminescent ATP assay is a highly sensitive method for optimizing the viability of Candida albicans test fungus in bioautographic TLC detection. A suspension of microbes (OD600nm = 0.5-0.7, in Mueller-Hinton broth with 5% glucose) in the log phase of growth can be used for dipping TLC plates. On the basis of our results with Candida albicans, we can differentiate between microbiostatic (bacteriostatic or fungistatic) and microbio-cidal (bactericidal or fungicidal) effects on TLC plates. Our micrographs clearly show the borders of inhibition zones in bioauto-grams. This technique leads to new possibilities in studies of the interactions between microbes and antimicrobial compounds on bioautographic silica gel TLC plates by using scanning electron microscopy. In this paper we describe an optimization procedure for bioautographic TLC detection.
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution. We provide an in-depth mathematical analysis of differences between the theoretical setup and the reality of training Wasserstein GANs. In this work, we gather both theoretical and empirical evidence that the WGAN loss is not a meaningful approximation of the Wasserstein distance. Moreover, we argue that the Wasserstein distance is not even a desirable loss function for deep generative models, and conclude that the success of Wasserstein GANs can in truth be attributed to a failure to approximate the Wasserstein distance.
SWP: Microsecond Network SLOs Without Priorities

The increasing use of cloud computing for latency-sensitive applications has sparked renewed interest in providing tight bounds on network tail latency. Achieving this in practice at reasonable network utilization has proved elusive, due to a combination of highly bursty application demand, faster link speeds, and heavy-tailed message sizes. While priority scheduling can be used to reduce tail latency for some traffic, this comes at a cost of much worse delay behavior for all other traffic on the network. Most operators choose to run their networks at very low average utilization, despite the added cost, and yet still suffer poor tail behavior. This paper takes a different approach. We build a system, swp, to help operators (and network designers) to understand and control tail latency without relying on priority scheduling. As network workload changes, swp is designed to give real-time advice on the network switch configurations needed to maintain tail latency objectives for each traffic class. The core of swp is an efficient model for simulating the combined effect of traffic characteristics, end-to-end congestion control, and switch scheduling on service-level objectives (SLOs), along with an optimizer that adjusts switch-level scheduling weights assigned to each class. Using simulation across a diverse set of workloads with different SLOs, we show that to meet the same SLOs as swp provides, FIFO would require 65% greater link capacity, and 79% more for scenarios with tight SLOs on bursty traffic classes.
Predicting Video with VQVAE

In recent years, the task of video prediction-forecasting future video given past video frames-has attracted attention in the research community. In this paper we propose a novel approach to this problem with Vector Quantized Variational AutoEncoders (VQ-VAE). With VQ-VAE we compress high-resolution videos into a hierarchical set of multi-scale discrete latent variables. Compared to pixels, this compressed latent space has dramatically reduced dimensionality, allowing us to apply scalable autoregressive generative models to predict video. In contrast to previous work that has largely emphasized highly constrained datasets, we focus on very diverse, large-scale datasets such as Kinetics-600. We predict video at a higher resolution on unconstrained videos, 256x256, than any other previous method to our knowledge. We further validate our approach against prior work via a crowdsourced human evaluation.
Solar wind-driven day-to-day effects on the Martian thermosphere/exosphere composition

Since the first in-situ measurements of the altitude profile of upper atmospheric density and composition carried out by the Viking lander missions during 1976, similar data were continuously gathered by MAVEN and MOM spacecraft orbiting Mars since September 2014 with a mass spectrometer and other related payloads. Using near-simultaneous observations by the two orbiters, we show that both data sets confirm significant day-to-day variations of Argon (Ar) density profiles in the Martian thermosphere/exosphere during 1-15 June 2018 when the solar EUV radiation did not show any appreciable change. We extend this study to include the parent atmospheric constituents (CO2, Ar, He, N2) and the photochemical products (O, CO) to examine the effect of solar wind plasma (e/H+) velocities and fluxes during the above time interval. Density profiles of these constituents show significant effects due to the additional electron impact dissociation and ionisation during the first week of June 2018, which subside in the next week returning to normal conditions. These first-time results are interpreted based on a number of relevant neutral and ion chemical reactions. This result provides a vital input to future modelling efforts of Martian thermosphere/exosphere composition studies and the solar EUV related variations due to the Schwabe cycle.