Miracle-dz/UniFAR-FTUs huggingface dataset:
Positioning in 5G Networks

The authors describe the recent 3GPP Release 16 specification for positioning in 5G networks. Release 1 6 specifies positioning signals, measurements, procedures, and architecture to meet requirements in regulatory, commercial, and industrial use cases, extending the positioning capability of the 3GPP standard compared to what was possible with LTE. The indicative positioning performance is evaluated in agreed representative 3GPP simulation scenarios, showing a 90 percentile accuracy of a few meters down to a few decimeters depending on scenarios and assumptions.
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving

Balancing safety and efficiency when planning in dense traffic is challenging. Interactive behavior planners incorporate prediction uncertainty and interactivity inherent to these traffic situations. Yet, their use of single-objective optimality impedes interpretability of the resulting safety goal. Safety envelopes which restrict the allowed planning region yield interpretable safety under the presence of behavior uncertainty, yet, they sacrifice efficiency in dense traffic due to conservative driving. Studies show that humans balance safety and efficiency in dense traffic by accepting a probabilistic risk of violating the safety envelope. In this work, we adopt this safety objective for interactive planning. Specifically, we formalize this safety objective, present the Risk-Constrained Robust Stochastic Bayesian Game modeling interactive decisions satisfying a maximum risk of violating a safety envelope under uncertainty of other traffic participants' behavior and solve it using our variant of Multi-Agent Monte Carlo Tree Search. We demonstrate in simulation that our approach outperforms baselines approaches, and by reaching the specified violation risk level over driven simulation time, provides an interpretable and tunable safety objective for interactive planning.
Unified signature cumulants and generalized Magnus expansions

Abstract The signature of a path can be described as its full non-commutative exponential. Following T. Lyons, we regard its expectation, the expected signature, as a path space analogue of the classical moment generating function. The logarithm thereof, taken in the tensor algebra, defines the signature cumulant. We establish a universal functional relation in a general semimartingale context. Our work exhibits the importance of Magnus expansions in the algorithmic problem of computing expected signature cumulants and further offers a far-reaching generalization of recent results on characteristic exponents dubbed diamond and cumulant expansions with motivations ranging from financial mathematics to statistical physics. From an affine semimartingale perspective, the functional relation may be interpreted as a type of generalized Riccati equation.
Building Representative Corpora from Illiterate Communities: A Reviewof Challenges and Mitigation Strategies for Developing Countries

Most well-established data collection methods currently adopted in NLP depend on the as- sumption of speaker literacy. Consequently, the collected corpora largely fail to represent swathes of the global population, which tend to be some of the most vulnerable and marginalised people in society, and often live in rural developing areas. Such underrepresented groups are thus not only ignored when making modeling and system design decisions, but also prevented from benefiting from development outcomes achieved through data-driven NLP. This paper aims to address the under-representation of illiterate communities in NLP corpora: we identify potential biases and ethical issues that might arise when collecting data from rural communities with high illiteracy rates in Low-Income Countries, and propose a set of practical mitigation strategies to help future work.
Deep reinforcement learning for smart calibration of radio telescopes

Modern radio telescopes produce unprecedented amounts of data, which are passed through many processing pipelines before the delivery of scientific results. Hyperparameters of these pipelines need to be tuned by hand to produce optimal results. Because many thousands of observations are taken during a lifetime of a telescope and because each observation will have its unique settings, the fine tuning of pipelines is a tedious task. In order to automate this process of hyperparameter selection in data calibration pipelines, we introduce the use of reinforcement learning. We test two reinforcement learning techniques, twin delayed deep deterministic policy gradient (TD3), and soft actor-critic, to train an autonomous agent to perform this fine tuning. For the sake of generalization, we consider the pipeline to be a black-box system where the summarized state of the performance of the pipeline is used by the autonomous agent. The autonomous agent trained in this manner is able to determine optimal settings for diverse observations and is therefore able to perform smart calibration, minimizing the need for human intervention.
baller2vec: A Multi-Entity Transformer For Multi-Agent Spatiotemporal Modeling

Multi-agent spatiotemporal modeling is a challenging task from both an algorithmic design and computational complexity perspective. Recent work has explored the efficacy of traditional deep sequential models in this domain, but these architectures are slow and cumbersome to train, particularly as model size increases. Further, prior attempts to model interactions between agents across time have limitations, such as imposing an order on the agents, or making assumptions about their relationships. In this paper, we introduce baller2vec, a multi-entity generalization of the standard Transformer that can, with minimal assumptions, simultaneously and efficiently integrate information across entities and time. We test the effectiveness of baller2vec for multi-agent spatiotemporal modeling by training it to perform two different basketball-related tasks: (1) simultaneously modeling the trajectories of all players on the court and (2) modeling the trajectory of the ball. Not only does baller2vec learn to perform these tasks well (outperforming a graph recurrent neural network with a similar number of parameters by a wide margin), it also appears to"understand"the game of basketball, encoding idiosyncratic qualities of players in its embeddings, and performing basketball-relevant functions with its attention heads.
Reducing the Amortization Gap in Variational Autoencoders: A Bayesian Random Function Approach

Variational autoencoder (VAE) is a very successful generative model whose key element is the so called amortized inference network, which can perform test time inference using a single feed forward pass. Unfortunately, this comes at the cost of degraded accuracy in posterior approximation, often underperforming the instance-wise variational optimization. Although the latest semi-amortized approaches mitigate the issue by performing a few variational optimization updates starting from the VAE's amortized inference output, they inherently suffer from computational overhead for inference at test time. In this paper, we address the problem in a completely different way by considering a random inference model, where we model the mean and variance functions of the variational posterior as random Gaussian processes (GP). The motivation is that the deviation of the VAE's amortized posterior distribution from the true posterior can be regarded as random noise, which allows us to take into account the uncertainty in posterior approximation in a principled manner. In particular, our model can quantify the difficulty in posterior approximation by a Gaussian variational density. Inference in our GP model is done by a single feed forward pass through the network, significantly faster than semi-amortized methods. We show that our approach attains higher test data likelihood than the state-of-the-arts on several benchmark datasets.
Metric Embedding Sub-discrimination Study

Deep metric learning is a technique used in a variety of discriminative tasks to achieve zero-shot, one-shot or few-shot learning. When applied, the sys-tem learns an embedding space where a non-parametric approach, such as K-nearest neighbor (KNN), can be used to discriminate features during test time. This work focuses on investigating to what extent feature information contained within this embedding space can be used to carry out sub-discrimination in the feature space. The study shows that within a discrimination embedding, the information on the salient attributes needed to solve the problem of sub-discrimination is saved within the embedding and that this inherent information can be used to carry out sub-discriminative tasks. To demonstrate this, an embedding designed initially to discriminate faces is used to diﬀerentiate several attributes such as gender, age and skin tone, without any additional training. The study is split into two study cases: intra class discrimination where all the embeddings took into consideration are from the same identity; and extra class discrimination where the embeddings represent diﬀerent identities. After the study, it is shown that it is possible to infer common attributes to diﬀer-ent identities. The system can also perform extra class sub-discrimination with
Radiative Scale Height and Shadows in Protoplanetary Disks

Planets form in young circumstellar disks called protoplanetary disks. However, it is still difficult to catch planet formation in situ. Nevertheless, from recent ALMA/SPHERE data, encouraging evidence of the direct and indirect presence of embedded planets has been identified in disks around young stars: co-moving point sources, gravitational perturbations, rings, cavities, and emission dips or shadows cast on disks. The interpretation of these observations needs a robust physical framework to deduce the complex disk geometry. In particular, protoplanetary disk models usually assume the gas pressure scale height given by the ratio of the sound speed over the azimuthal velocity H/r = c s /v k. By doing so, radiative pressure fields are often ignored, which could lead to a misinterpretation of the real vertical structure of such disks. We follow the evolution of a gaseous disk with an embedded Jupiter-mass planet through hydrodynamical simulations, computing the disk scale height including radiative pressure, which was derived from a generalization of the stellar atmosphere theory. We focus on the vertical impact of the radiative pressure in the vicinity of circumplanetary disks, where temperatures can reach ≳1000 K for an accreting planet and radiative forces can overcome gravitational forces from the planet. The radiation pressure effects create a vertical, optically thick column of gas and dust at the protoplanet location, casting a shadow in scattered light. This mechanism could explain the peculiar illumination patterns observed in some disks around young stars such as HD 169142 where a moving shadow has been detected or the extremely high aspect ratio H/r ∼ 0.2 observed in systems like AB Aur and CT Cha.
Automated brainstem parcellation using multi-atlas segmentation and deep neural network

About 5-8% of individuals over the age of 60 have dementia. With our ever-aging population this number is likely to increase, making dementia one of the most important threats to public health in the 21st century. Given the phenotypic overlap of individual dementias the diagnosis of dementia is a major clinical challenge, even with current gold standard diagnostic approaches. However, it has been shown that certain dementias show specific structural characteristics in the brain. Progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) are prototypical examples of this phenomenon, as they often present with characteristic brainstem atrophy. More detailed characterization of brain atrophy due to individual diseases is urgently required to select biomarkers and therapeutic targets that are meaningful to each disease. Here we present a joint multi-atlas-segmentation and deep-learning-based segmentation method for fast and robust parcellation of the brainstem into its four substructures, i.e., the midbrain, pons, medulla, and superior cerebellar peduncles (SCP), that in turn can provide detailed volumetric information on the brainstem sub-structures affected in PSP and MSA. The method may also benefit other neurodegenerative diseases, such as Parkinson’s disease; a condition which is often considered in the differential diagnosis of PSP and MSA. Comparison with state-of-the-art labeling techniques evaluated on ground truth manual segmentations demonstrate that our method is significantly faster than prior methods as well as showing improvement in labeling the brainstem indicating that this strategy may be a viable option to provide a better characterization of the brainstem atrophy seen in PSP and MSA.
Spell Correction for Azerbaijani Language using Deep Neural Networks

Spell correction is used to detect and correct orthographic mistakes in texts. Most of the time, traditional dictionary lookup with string similarity methods is suitable for the languages that have a less complex structure such as the English language. However, the Azerbaijani language has a more complex structure and due to its morphological structure, the derivation of words is plenty that several words are derived from adding suffices, affixes to the words. Therefore, in this paper sequence to sequence model with an attention mechanism is used to develop spelling correction for Azerbaijani. Total 12000 wrong and correct sentence pairs used for training, and the model is tested on 1000 real-world misspelled words and F1-score results are 75% for distance 0, 90% for distance 1, and 96% for distance 2.
On multichromatic numbers of widely colorable graphs

A coloring is called s ‐wide if no walk of length 2s−1 connects vertices of the same color. A graph is s ‐widely colorable with t colors if and only if it admits a homomorphism into a universal graph W(s,t) . Tardif observed that the value of the rth multichromatic number χr(W(s,t)) of these graphs is at least t+2(r−1) and equality holds for r=s=2 . He asked whether there is equality also for r=s=3 . We show that χs(W(s,t))=t+2(s−1) for all s thereby answering Tardif's question. We observe that for large r (with respect to s and t fixed) we cannot have equality and that for s fixed and t going to infinity the fractional chromatic number of W(s,t) also tends to infinity. The latter is a simple consequence of another result of Tardif on the fractional chromatic number of generalized Mycielski graphs.
Security assessment and impact analysis of cyberattacks in integrated T&D power systems

In this paper, we examine the impact of cyberattacks in an integrated transmission and distribution (T&D) power grid model with distributed energy resource (DER) integration. We adopt the OCTAVE Allegro methodology to identify critical system assets, enumerate potential threats, analyze, and prioritize risks for threat scenarios. Based on the analysis, attack strategies and exploitation scenarios are identified which could lead to system compromise. Specifically, we investigate the impact of data integrity attacks in inverted-based solar PV controllers, control signal blocking attacks in protective switches and breakers, and coordinated monitoring and switching time-delay attacks.
Multifractality and self-averaging at the many-body localization transition

Finite-size effects have been a major and justifiable source of concern for studies of many-body localization, and several works have been dedicated to the subject. In this paper, however, we discuss yet another crucial problem that has received much less attention, that of the lack of self-averaging and the consequent danger of reducing the number of random realizations as the system size increases. By taking this into account and considering ensembles with a large number of samples for all system sizes analyzed, we find that the generalized dimensions of the eigenstates of the disordered Heisenberg spin-1/2 chain close to the transition point to localization are described remarkably well by an exact analytical expression derived for the non-interacting Fibonacci lattice, thus providing an additional tool for studies of many-body localization.
Effect of Cobalt(II) and Manganese(II) on the Chemiluminescent Reaction of Peroxymonosulfate with Humic Acid and Some Organic Compounds. Analytical Applications

Abstract The intensity of the radiation emitted by humic acid (HA) in the presence of SO5 2− in basic medium was used to determine HA in the range up to 20.0 mg l−1. The detection limit was 0.24 mg l−1. The addition to the sample of 50 mg l−1 of Co(II) or Mn(II), as EDTA complexes or chloride salts, enhanced the radiation emission as a result of the formation of strong oxidant radicals such as SO5 •−, SO4 •−, and HO•. In the presence of these metal ions, the oxidation of HA and the catalytic decomposition of SO5 2− occur simultaneously. Low concentration of HA in natural waters can be detected. HA was replaced by some model organic compounds. The marked chemiluminescent (CL) signals followed the order: phloroglucinol>fulvic acid>humic acid>resorcinol>pyrogallol>cathecol>hydroquinone.
LoRD-Net: Unfolded Deep Detection Network With Low-Resolution Receivers

The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on the extreme case of one-bit quantizers, and propose a deep detector entitled LoRD-Net for recovering information symbols from one-bit measurements. Our method is a model-aware data-driven architecture based on deep unfolding of first-order optimization iterations. LoRD-Net has a task-based architecture dedicated to recovering the underlying signal of interest from the one-bit noisy measurements without requiring prior knowledge of the channel matrix through which the one-bit measurements are obtained. The proposed deep detector has much fewer parameters compared to black-box deep networks due to the incorporation of domain-knowledge in the design of its architecture, allowing it to operate in a data-driven fashion while benefiting from the flexibility, versatility, and reliability of model-based optimization methods. LoRD-Net operates in a blind fashion, which requires addressing both the non-linear nature of the data-acquisition system as well as identifying a proper optimization objective for signal recovery. Accordingly, we propose a two-stage training method for LoRD-Net, in which the first stage is dedicated to identifying the proper form of the optimization process to unfold, while the latter trains the resulting model in an end-to-end manner. We numerically evaluate the proposed receiver architecture for one-bit signal recovery in wireless communications and demonstrate that the proposed hybrid methodology outperforms both data-driven and model-based state-of-the-art methods, while utilizing small datasets, on the order of merely $\sim 500$ samples, for training.
Star-shaped hexakis(9,9-dihexyl-9H-fluoren-2-yl)benzene end-capped with carbazole and diphenylamine units: solution-processable, high Tg hole-transporting materials for organic light-emitting devices

Two new hole-transporting materials, namely HFB-Cz and HFB-Dpa, were designed and synthesized by attaching carbazole and diphenylamine units to the hexakis(9,9-dihexyl-9H-fluoren-2-yl)benzene (HFB) core via Buchwald–Hartwig coupling reaction. The long alkyl chain and core rigidity endow these compounds with good solution processability and high thermal stability. HFB-Cz and HFB-Dpa exhibit significantly high glass transition temperatures (225 and 154 °C) relative to widely used hole-transporting materials, such as N,N′-bis(3-methylphenyl)-1,1′-biphenyl-4, 4′-diamine (TPD, 65 °C) and 1,4-bis((1-naphthylphenyl)amino)biphenyl (NPB, 96 °C). Solution-processed green OLED devices using HFB-Cz and HFB-Dpa as hole-transporting materials exhibit very high efficiencies with a maximum current efficiency up to 6.2 cd A−1. These efficiencies are substantially higher than the NPB-based control device, and are among the highest for the hole-transporting materials in similar device configuration.
Perspective: dusty plasma experiments—a learning tool for physics graduate students

Plasma is an ionized gas that responses collectively to any external (or internal) perturbation. Introducing micron-sized solid dust particles into plasma makes it more interesting. The dust particles acquire large negative charges on their surface in low-temperature laboratory plasma and exhibit collective behavior similar to the ambient plasma medium. Some remarkable features of the charged dust grain medium (dusty plasma) allow us to use it as a model system to understand a number of physics phenomena at a microscopic level. In this perspective paper, the author highlights the role of dusty plasma experiments as a learning tool for undergraduate and post-graduate physics students at higher academic institutions. Students could have great opportunities to understand basic physical phenomena as well as learn about many advanced data analysis tools and techniques by performing dusty plasma (plasma) experiments. A few simple experiments in a single dusty plasma device that connect the basic physics phenomena are discussed.
An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios

Intelligent robots provide a new insight into efficiency improvement in industrial and service scenarios to replace human labor. However, these scenarios include dense and dynamic obstacles that make motion planning of robots challenging. Traditional algorithms like A* can plan collision-free trajectories in static environment, but their performance degrades and computational cost increases steeply in dense and dynamic scenarios. Optimal-value reinforcement learning algorithms (RL) can address these problems but suffer slow speed and instability in network convergence. Network of policy gradient RL converge fast in Atari games where action is discrete and finite, but few works have been done to address problems where continuous actions and large action space are required. In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning, therefore optimal speeds and directions of robot are generated. Experimental results demonstrate that our algorithm converges faster and stable than optimal-value RL. It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
How Information Diffuse in a Nomination Network?

During the special period of the COVID-19 outbreak, this project investigated the driving factors in different information diffusion modes (i.e. broadcasting mode, contagion mode) based on the nomination relations in a charitable social relay campaign on Sina Weibo. Specifically, we mapped a nomination social network and tracked the core communicators in both modes. Besides, we also observed the network from perspectives such as relationships between core communicators and modularity of the whole network. We extracted homophily factors and tested them on representative communities within the largest component of the network. We found that some core communicators distributed in a co-dependent way. At last, we presented several explanations to the phenomenon which can be explored in further research.