Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge Transfer based Radio and Computation Resource Allocation for 5G RAN Slicing

To implement network slicing in 5G, resource allocation is a key function to allocate limited network resources such as radio and computation resources to multiple slices. However, the joint resource allocation also leads to a higher complexity in the network management. In this work, we propose a knowledge transfer based resource allocation (KTRA) method to jointly allocate radio and computation resources for 5G RAN slicing. Compared with existing works, the main difference is that the proposed KTRA method has a knowledge transfer capability. It is designed to use the prior knowledge of similar tasks to improve performance of the target task, e.g., faster convergence speed or higher average reward. The proposed KTRA is compared with Q-learning based resource allocation (QLRA), and KTRA method presents a 18.4% lower URLLC delay and a 30.1% higher eMBB throughput as well as a faster convergence speed.
WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditions

Reconstruction of signals from undersampled and noisy measurements is a topic of considerable interest. Sharpness conditions directly control the recovery performance of restart schemes for first-order methods without the need for restrictive assumptions such as strong convexity. However, they are challenging to apply in the presence of noise or approximate model classes (e.g., approximate sparsity). We provide a first-order method: Weighted, Accelerated and Restarted Primal-dual (WARPd), based on primal-dual iterations and a novel restart-reweight scheme. Under a generic approximate sharpness condition, WARPd achieves stable linear convergence to the desired vector. Many problems of interest fit into this framework. For example, we analyze sparse recovery in compressed sensing, low-rank matrix recovery, matrix completion, TV regularization, minimization of $\|Bx\|_{l^1}$ under constraints ($l^1$-analysis problems for general $B$), and mixed regularization problems. We show how several quantities controlling recovery performance also provide explicit approximate sharpness constants. Numerical experiments show that WARPd compares favorably with specialized state-of-the-art methods and is ideally suited for solving large-scale problems. We also present a noise-blind variant based on the Square-Root LASSO decoder. Finally, we show how to unroll WARPd as neural networks. This approximation theory result provides lower bounds for stable and accurate neural networks for inverse problems and sheds light on architecture choices. Code and a gallery of examples are made available online as a MATLAB package.
Lattice dynamics across the ferroelastic phase transition in Ba2ZnTeO6: A Raman study

Structural phase transitions drive several unconventional phenomena including some illustrious ferroic attributes which are relevant for technological advancements. With this note, we have investigated the structural transition of perovskite-type trigonal Ba2ZnTeO6 using Raman spectroscopy, across Tc ~ 150 K, which is also accompanied by a parato ferroelastic transition. We have observed the presence of central peak (quasi-elastic Rayleigh profile), strong anharmonicity in the soft mode, hysteretic phonon behavior, and signatures of coexistent phases. The existence of central peak in Ba2ZnTeO6 is manifested by a sharp rise in the intensity of the Rayleigh profile in concomitant with the damping of the soft mode near Tc, shedding light on the lattice dynamics during the phase transition. While most of the phonons show splitting below Tc confirming the phase transition, we have observed thermal hysteretic behavior of phonon modes that signifies the first-order nature of the transition and presence of coexisting phases, which are corroborated by our temperature-dependent x-ray diffraction and specific heat measurements. Further, an evidence of the concomitant ferroelastic transition appears in the form of a very strong anharmonicity in the thermal response of the soft phonon mode at ~ 31 cm which is remarkable compared to the hitherto known behavior of soft modes in well-known ferroelectrics.
Bank transactions embeddings help to uncover current macroeconomics

Macroeconomic indexes are of high importance for banks: many risk-control decisions utilize these indexes. A typical workflow of these indexes evaluation is costly and protracted, with a lag between the actual date and available index being a couple of months. Banks predict such indexes now using autoregressive models to make decisions in a rapidly changing environment. However, autoregressive models fail in complex scenarios related to appearances of crises. We propose to use clients’ financial transactions data from a large Russian bank to get such indexes. Financial transactions are long, and a number of clients is huge, so we develop an efficient approach that allows fast and accurate estimation of macroeconomic indexes based on a stream of transactions consisting of millions of transactions. The approach uses a neural networks paradigm and a smart sampling scheme.The results show that our neural network approach outperforms the baseline method on hand-crafted features based on transactions. Calculated embeddings show the correlation between the client’s transaction activity and bank macroeconomic indexes over time.
Deep Neural Networks on EEG Signals to Predict Auditory Attention Score Using Gramian Angular Difference Field

Auditory attention is a selective type of hearing in which people focus their attention intentionally on a specific source of a sound or spoken words whilst ignoring or inhibiting other auditory stimuli. In some sense, the auditory attention score of an individual shows the focus the person can have in auditory tasks. The recent advancements in deep learning and in the non-invasive technologies recording neural activity beg the question, can deep learning along with technologies such as electroencephalography (EEG) be used to predict the auditory attention score of an individual? In this paper, we focus on this very problem of estimating a person's auditory attention level based on their brain's electrical activity captured using 14-channeled EEG signals. More specifically, we deal with attention estimation as a regression problem. The work has been performed on the publicly available Phyaat dataset. The concept of Gramian Angular Difference Field (GADF) has been used to convert time-series EEG data into an image having 14 channels, enabling us to train various deep learning models such as 2D CNN, 3D CNN, and convolutional autoencoders. Their performances have been compared amongst themselves as well as with the work done previously. Amongst the different models we tried, 2D CNN gave the best performance. It outperformed the existing methods by a decent margin of 0.22 mean absolute error (MAE).
Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments

This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction model is designed by partitioning the environment into multiple belief regions and employed at the high-level navigation planner to estimate the dynamic obstacles' location. This additional location information of dynamic obstacles offered by belief abstraction enables less conservative long-horizon navigation actions beyond guaranteeing immediate collision avoidance. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate nonperiodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates the safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics.
Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models

Pathogen deep‐sequencing is an increasingly routinely used technology in infectious disease surveillance. We present a semi‐parametric Bayesian Poisson model to exploit these emerging data for inferring infectious disease transmission flows and the sources of infection at the population level. The framework is computationally scalable in high‐dimensional flow spaces thanks to Hilbert Space Gaussian process approximations, allows for sampling bias adjustments, and estimation of gender‐ and age‐specific transmission flows at finer resolution than previously possible. We apply the approach to densely sampled, population‐based HIV deep‐sequence data from Rakai, Uganda, and find substantive evidence that adolescent and young women were predominantly infected through age‐disparate relationships in the study period 2009–2015.
An encounter-based approach for restricted diffusion with a gradient drift

We develop an encounter-based approach for describing restricted diffusion with a gradient drift toward a partially reactive boundary. For this purpose, we introduce an extension of the Dirichlet-to-Neumann operator and use its eigenbasis to derive a spectral decomposition for the full propagator, i.e. the joint probability density function for the particle position and its boundary local time. This is the central quantity that determines various characteristics of diffusion-influenced reactions such as conventional propagators, survival probability, first-passage time distribution, boundary local time distribution, and reaction rate. As an illustration, we investigate the impact of a constant drift onto the boundary local time for restricted diffusion on an interval. More generally, this approach accesses how external forces may influence the statistics of encounters of a diffusing particle with the reactive boundary.
Incipient geometric lattice instability of cubic fluoroperovskites

R. M. Dubrovin , ∗ A. C. Garcia-Castro , N. V. Siverin , N. N. Novikova , K. N. Boldyrev , Aldo H. Romero , and R. V. Pisarev 1 Ioffe Institute, Russian Academy of Sciences, 194021 St. Petersburg, Russia School of Physics, Universidad Industrial de Santander, Carrera 27 Calle 9, 680002 Bucaramanga, Colombia Institute of Spectroscopy, Russian Academy of Sciences, 108840 Moscow, Troitsk, Russia Department of Physics and Astronomy, West Virginia University, WV-26506-6315 Morgantown, West Virginia, USA (Dated: October 26, 2021)
Signal to Noise Ratio Loss Function

This work proposes a new loss function targeting classification problems, utilizing a source of information overlooked by cross entropy loss. First, we derive a series of the tightest upper and lower bounds for the probability of a random variable in a given interval. Second, a lower bound is proposed for the probability of a true positive for a parametric classification problem, where the form of probability density function (pdf) of data is given. A closed form for finding the optimal function of unknowns is derived to maximize the probability of true positives. Finally, for the case that the pdf of data is unknown, we apply the proposed boundaries to find the lower bound of the probability of true positives and upper bound of the probability of false positives and optimize them using a loss function which is given by combining the boundaries. We demonstrate that the resultant loss function is a function of the signal to noise ratio both within and across logits. We empirically evaluate our proposals to show their benefit for classification problems.
Dynamic Influence Maximization

We initiate a systematic study on $\mathit{dynamic}$ $\mathit{influence}$ $\mathit{maximization}$ (DIM). In the DIM problem, one maintains a seed set $S$ of at most $k$ nodes in a dynamically involving social network, with the goal of maximizing the expected influence spread while minimizing the amortized updating cost. We consider two evolution models. In the $\mathit{incremental}$ model, the social network gets enlarged over time and one only introduces new users and establishes new social links, we design an algorithm that achieves $(1-1/e-\epsilon)$-approximation to the optimal solution and has $k \cdot\mathsf{poly}(\log n, \epsilon^{-1})$ amortized running time, which matches the state-of-art offline algorithm with only poly-logarithmic overhead. In the $\mathit{fully}$ $\mathit{dynamic}$ model, users join in and leave, influence propagation gets strengthened or weakened in real time, we prove that under the Strong Exponential Time Hypothesis (SETH), no algorithm can achieve $2^{-(\log n)^{1-o(1)}}$-approximation unless the amortized running time is $n^{1-o(1)}$. On the technical side, we exploit novel adaptive sampling approaches that reduce DIM to the dynamic MAX-k coverage problem, and design an efficient $(1-1/e-\epsilon)$-approximation algorithm for it. Our lower bound leverages the recent developed distributed PCP framework.
Content Filtering Enriched GNN Framework for News Recommendation

Learning accurate users and news representations is critical for news recommendation. Despite great progress, existing methods seem to have a strong bias towards content representation or just capture collaborative filtering relationship. However, these approaches may suffer from the data sparsity problem (user-news interactive behavior sparsity problem) or maybe affected more by news (or user) with high popularity. In this paper, to address such limitations, we propose content filtering enriched GNN framework for news recommendation, ConFRec in short. It is compatible with existing GNN-based approaches for news recommendation and can capture both collaborative and content filtering information simultaneously. Comprehensive experiments are conducted to demonstrate the effectiveness of ConFRec over the state-of-the-art baseline models for news recommendation on real-world datasets for news recommendation.
Prior Intensified Information Criterion

The widely applicable information criterion (WAIC) has been used as a model selection criterion for Bayesian statistics in recent years. It is an asymptotically unbiased estimator of the Kullback-Leibler divergence between a Bayesian predictive distribution and the true distribution. Not only is the WAIC theoretically more sound than other information criteria, its usefulness in practice has also been reported. On the other hand, the WAIC is intended for settings in which the prior distribution does not have an asymptotic influence, and as we set the class of the prior distribution to be more complex, it never fails to select the most complex one. To alleviate these concerns, this paper proposed the prior intensified information criterion (PIIC). In addition, it customizes this criterion to incorporate sparse estimation and causal inference. Numerical experiments show that the PIIC clearly outperforms the WAIC in terms of prediction performance when the above concerns are manifested. A real data analysis confirms that the results of variable selection and Bayesian estimators of the WAIC and PIIC differ significantly.
Carnegie Supernova Project-II: Near-infrared Spectroscopy of Stripped-envelope Core-collapse Supernovae

We present 75 near-infrared (NIR; 0.8−2.5 μm) spectra of 34 stripped-envelope core-collapse supernovae (SESNe) obtained by the Carnegie Supernova Project-II (CSP-II), encompassing optical spectroscopic Types IIb, Ib, Ic, and Ic-BL. The spectra range in phase from pre-maximum to 80 days past maximum. This unique data set constitutes the largest NIR spectroscopic sample of SESNe to date. NIR spectroscopy provides observables with additional information that is not available in the optical. Specifically, the NIR contains the strong lines of He i and allows a more detailed look at whether Type Ic supernovae are completely stripped of their outer He layer. The NIR spectra of SESNe have broad similarities, but closer examination through statistical means reveals a strong dichotomy between NIR “He-rich” and “He-poor” SNe. These NIR subgroups correspond almost perfectly to the optical IIb/Ib and Ic/Ic-BL types, respectively. The largest difference between the two groups is observed in the 2 μm region, near the He i λ2. 0581 μm line. The division between the two groups is not an arbitrary one along a continuous sequence. Early spectra of He-rich SESNe show much stronger He i λ2. 0581 μm absorption compared to the He-poor group, but with a wide range of profile shapes. The same line also provides evidence for trace amounts of He in half of our SNe in the He-poor group.
Micro Batch Streaming: Allowing the Training of DNN models Using a large batch size on Small Memory Systems

—The size of the deep learning models has greatly increased over the past decade. Such models are difﬁcult to train using a large batch size, because commodity machines do not have enough memory to accommodate both the model and a large data size. The batch size is one of the hyper-parameters used in the training model, and it is dependent on and is limited by the target machine memory capacity and it is dependent on the remaining memory after the model is uploaded. A smaller batch size usually results in performance degradation. This paper proposes a framework called Micro-Batch Streaming (MBS) to address this problem. This method helps deep learning models to train by providing a batch streaming algorithm that splits a batch into the appropriate size for the remaining memory size and streams them sequentially to the target machine. A loss normalization algorithm based on the gradient accumulation is used to maintain the performance. The purpose of our method is to allow deep learning models to train using mathematically determined optimal batch sizes that cannot ﬁt into the memory of a target system.
Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

Social influence pervades our everyday lives and lays the foundation for complex social phenomena. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect appears between psychology approaches, which are generally performed and tested in controlled lab experiments, and the quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues (such as an ongoing pandemic);the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work bridges this gap and presents three contributions towards modeling and empirically quantifying online influence. The first contribution is a data-driven Generalized Influence Model that incorporates two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the social capital distribution. The second contribution is a framework to empirically rank users' social influence using a human-in-the-loop active learning method combined with crowdsourced pairwise influence comparisons. We build a human-labeled ground truth, calibrate our generalized influence model and perform a large-scale evaluation of influence. We find that our generalized model outperforms the current state-of-the-art approaches and corrects the inherent biases introduced by the widely used follower count. As the third contribution, we apply the influence model to discussions around COVID-19. We quantify users' influence, and we tabulate it against their professions. We find that the executives, media, and military are more influential than pandemic-related experts such as life scientists and healthcare professionals.
HWTool: Fully Automatic Mapping of an Extensible C++ Image Processing Language to Hardware

Implementing image processing algorithms using FPGAs or ASICs can improve energy efficiency by orders of magnitude over optimized CPU, DSP, or GPU code. These efficiency improvements are crucial for enabling new applications on mobile power-constrained devices, such as cell phones or AR/VR headsets. Unfortunately, custom hardware is commonly implemented using a waterfall process with time-intensive manual mapping and optimization phases. Thus, it can take years for a new algorithm to make it all the way from an algorithm design to shipping silicon. Recent improvements in hardware design tools, such as C-to-gates High-Level Synthesis (HLS), can reduce design time, but still require manual tuning from hardware experts. In this paper, we present HWTool, a novel system for automatically mapping image processing and computer vision algorithms to hardware. Our system maps between two domains: HWImg, an extensible C++ image processing library containing common image processing and parallel computing operators, and Rigel2, a library of optimized hardware implementations of HWImg's operators and backend Verilog compiler. We show how to automatically compile HWImg to Rigel2, by solving for interfaces, hardware sizing, and FIFO buffer allocation. Finally, we map full-scale image processing applications like convolution, optical flow, depth from stereo, and feature descriptors to FPGA using our system. On these examples, HWTool requires on average only 11% more FPGA area than hand-optimized designs (with manual FIFO allocation), and 33% more FPGA area than hand-optimized designs with automatic FIFO allocation, and performs similarly to HLS.
Classification of the core-collapse supernova explosion mechanism with learned dictionaries

Core-collapse supernovae (CCSN) are a prime source of gravitational waves. Estimations of their typical frequenciesmake themperfect targets for the current network of advanced, groundbased detectors. A successful detection could potentially reveal the underlying explosion mechanism through the analysis of the waveform. This has been illustrated using the Supernova Model Evidence Extractor (SMEE; Logue et al. (2012)), an algorithm based on principal component analysis andBayesianmodel selection.Here,we present a complementary approach to SMEE based on (supervised) dictionary-learning and show that it is able to reconstruct and classify CCSN signals according to their morphology. Our waveform signals are obtained from (a) two publicly available catalogs built from numerical simulations of neutrino-driven (Mur) and magneto-rotational (Dim) CCSN explosions and (b) from a third ‘mock’ catalog of simulated sine-Gaussian (SG) waveforms. Those signals are injected into coloured Gaussian noise to simulate the background noise of Advanced LIGO in its broadband configuration and scaled to a freely-specifiable signal-to-noise ratio (SNR). We show that our approach correctly classifies signals from all three dictionaries. In particular, for SNR=15-20, we obtain perfect matches for both Dim and SG signals and about 85% true classifications for Mur signals. These results are comparable to those reported by SMEE for the same CCSN signals when those are injected in only one LIGO detector. We discuss the main limitations of our approach as well as possible improvements.