Miracle-dz/UniFAR-FTUs huggingface dataset:
RSS-Based UAV-BS 3-D Mobility Management via Policy Gradient Deep Reinforcement Learning

We address the mobility management of an autonomous UAV-mounted base station (UAV-BS) that provides communication services to a cluster of users on the ground while the geographical characteristics (e.g., location and boundary) of the cluster, the geographical locations of the users, and the characteristics of the radio environment are unknown. UAV-BS solely exploits the received signal strengths (RSS) from the users and accordingly chooses its (continuous) 3-D speed to constructively navigate, Le. , improving the transmitted data rate. To compensate for the lack of a model, we adopt policy gradient deep reinforcement learning. As our approach does not rely on any particular information about the users as well as the radio environment, it is flexible and respects the privacy concerns. Our experiments indicate that despite the minimum available information the UAV-BS is able to distinguish between high-rise (often non-line-of-sight dominant) and sub-urban (mainly line-of-sight dominant) environments such that in the former (resp. latter) it tends to reduce (resp. increase) its height and stays close (resp. far) to the cluster. We further observe that the choice of the reward function affects the speed and the ability of the agent to adhere to the problem constraints without affecting the delivered data rate.
Energy Conditions in $f(P)$ Gravity

f ( P ) gravity is a novel extension of ECG in which the Ricci scalar in the action is replaced by a function of the curvature invariant P which represents the contractions of the Riemann tensor at the cubic order [9]. The present work is concentrated on bounding some f ( P ) gravity models using the concept of energy conditions where the functional forms of f ( P ) are represented as a) f ( P ) = α √ P , and b) f ( P ) = α exp( P ), where α is the sole model parameter. Energy conditions are interesting linear relationships between pressure and density and have been extensively employed to derive interesting results in Einstein’s gravity, and are also an excellent tool to impose constraints on any cosmological model. To place the bounds, we ensured that the energy density must remain positive, the pressure must remain negative, and the EoS parameter must attain a value close to − 1 to make sure that the bounds respect the accelerated expansion of the Universe and are also in harmony with the latest observational data. We report that for both the models, suitable parameter spaces exist which satisfy the aforementioned conditions and therefore posit the f ( P ) theory of gravity to be a promising modiﬁed theory of gravitation.
Security and Privacy of Lightning Network Payments with Uncertain Channel Balances

The Lightning Network (LN) is a prominent payment channel network aimed at addressing Bitcoin's scalability issues. Due to the privacy of channel balances, senders cannot reliably choose sufficiently liquid payment paths and resort to a trial-and-error approach, trying multiple paths until one succeeds. This leaks private information and decreases payment reliability, which harms the user experience. This work focuses on the reliability and privacy of LN payments. We create a probabilistic model of the payment process in the LN, accounting for the uncertainty of the channel balances. This enables us to express payment success probabilities for a given payment amount and a path. Applying negative Bernoulli trials for single- and multi-part payments allows us to compute the expected number of payment attempts for a given amount, sender, and receiver. As a consequence, we analytically derive the optimal number of parts into which one should split a payment to minimize the expected number of attempts. This methodology allows us to define service level objectives and quantify how much private information leaks to the sender as a side effect of payment attempts. We propose an optimized path selection algorithm that does not require a protocol upgrade. Namely, we suggest that nodes prioritize paths that are most likely to succeed while making payment attempts. A simulation based on the real-world LN topology shows that this method reduces the average number of payment attempts by 20% compared to a baseline algorithm similar to the ones used in practice. This improvement will increase to 48% if the LN protocol is upgraded to implement the channel rebalancing proposal described in BOLT14.
EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition

In this manuscript, the topic of multi-corpus Speech Emotion Recognition (SER) is approached from a deep transfer learning perspective. A large corpus of emotional speech data, <bold><sc>EmoSet</sc></bold>, is assembled from a number of existing Speech Emotion Recognition (SER) corpora. In total, <sc>EmoSet</sc> contains <bold>84 181 audio recordings</bold> from <bold>26 SER corpora</bold> with a total duration of over <bold>65 hours</bold>. The corpus is then utilised to create a novel framework for multi-corpus SER and general audio recognition, namely <bold><sc>EmoNet</sc></bold>. A combination of a deep ResNet architecture and residual adapters is transferred from the field of multi-domain visual recognition to multi-corpus SER on <sc>EmoSet</sc>. The introduced residual adapter approach enables parameter efficient training of a multi-domain SER model on all 26 corpora. A shared model with only 3.5 times the number of parameters of a model trained on a single database leads to increased performance for 21 of the 26 corpora in <sc>EmoSet</sc>. Using repeated training runs and Almost Stochastic Order with significance level of <inline-formula><tex-math notation="LaTeX">$\alpha = 0.05$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>α</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>. </mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="gerczuk-ieq1-3135152.gif"/></alternatives></inline-formula>, these improvements are further significant for 15 datasets while there are just three corpora that see only significant decreases across the residual adapter transfer experiments. Finally, we make our <sc>EmoNet</sc> framework publicly available for users and developers at <monospace><uri>https://github.com/EIHW/EmoNet</uri></monospace>.
Designing Phononic Band Gaps With Sticky Potentials

Spectral gaps in the vibrational modes of disordered solids are key design elements in the synthesis and control of phononic meta-materials that exhibit a plethora of novel elastic and mechanical properties. However, reliably producing these gaps often require a high degree of network specificity through complex control optimization procedures. In this work, we present as an additional tool to the existing repertoire, a numerical scheme that rapidly generates sizeable spectral gaps in absence of any fine tuning of the network structure or elastic parameters. These gaps occur even in disordered polydisperse systems consisting of relatively few particles (N ~ 102 − 103). Our proposed procedure exploits sticky potentials that have recently been shown to suppress the formation of soft modes, thus effectively recovering the linear elastic regime where band structures appear, at much shorter length scales than in conventional models of disordered solids. Our approach is relevant to design and realization of gapped spectra in a variety of physical setups ranging from colloidal suspensions to 3D-printed elastic networks.
Causal Markov Boundaries

Feature selection is an important problem in machine learning, which aims to select variables that lead to an optimal predictive model. In this paper, we focus on feature selection for post-intervention outcome prediction from pre-intervention variables. We are motivated by healthcare settings, where the goal is often to select the treatment that will maximize a specific patient's outcome; however, we often do not have sufficient randomized control trial data to identify well the conditional treatment effect. We show how we can use observational data to improve feature selection and effect estimation in two cases: (a) using observational data when we know the causal graph, and (b) when we do not know the causal graph but have observational and limited experimental data. Our paper extends the notion of Markov boundary to treatment-outcome pairs. We provide theoretical guarantees for the methods we introduce. In simulated data, we show that combining observational and experimental data improves feature selection and effect estimation.
High Performance Optical Filters Using Three Waveguide Coupled Sagnac Loop Reflectors

We theoretically investigate advanced multi-functional integrated photonic filters formed by three waveguide coupled Sagnac loop reflectors (3WC-SLRs). By tailoring the coherent mode interference, the spectral response of the 3WC-SLR resonators is engineered to achieve diverse filtering functions with high performance. These include optical analogues of Fano resonances that yield ultrahigh spectral extinction ratios (ERs) and slope rates, resonance mode splitting with high ERs and low free spectral ranges, and classical Butterworth, Bessel, Chebyshev, and elliptic filters. A detailed analysis of the impact of the structural parameters and fabrication tolerances is provided to facilitate device design and optimization. The requirements for practical applications are also considered. These results theoretically verify the effectiveness of using 3WCSLR resonators as multi-functional integrated photonic filters for flexible spectral engineering in diverse applications. Keywords—Integrated optics, resonators, Fano resonance, mode splitting, classical filters.
Path integrals for stochastic hybrid reaction-diffusion processes

We construct path integrals for stochastic hybrid reaction-diffusion (RD) processes, in which the reaction terms depend on the discrete state of a randomly switching environment. We proceed by spatially discretizing a given RD system and using a spinor representation of the environmental states to derive a path integral for the lattice model. In the case of large molecular numbers, the corresponding continuum path integral action is expressed in terms of an effective Hamiltonian, which involves a concentration field $u(\x,t)$, $\x\in \R^d$, a conjugate field $v(\x,t)$, and $M$ auxiliary conjugate pairs $(c_m(t),\phi_m(t))$, where $M$ is the number of discrete environmental states. The variable $c_m(t)$ determines the effective probability that a sample path is exposed to the $m$-th environmental state at time $t$, with $\sum_{m=1}^Mc_m(t)=1$. We then consider the semi-classical (adiabatic) limit $\epsilon \rightarrow 0$, where $\epsilon^{-1}$ determines the rate of switching between the environmental states. We show how the auxiliary variables can be eliminated to yield an action functional for the fields $u$ and $v$ alone. The associated Hamiltonian is the sum of a diffusion term and the Perron or principal eigenvalue of a functional linear operator involving the reaction terms and the matrix generator of the switching process. The reduced path integral is then used to derive a functional Hamilton-Jacobi equation for least action paths and to obtain a Gaussian noise approximation of the stochastic hybrid RD system in the adiabatic limit. Finally, the path integral in the case of low molecular numbers is constructed by considering a corresponding RD master equation. It is now necessary to take into account two sources of noise, one due to the switching environment and the other due to fluctuations in molecular numbers.
Multi-Agent Reinforcement Learning based Joint Cooperative Spectrum Sensing and Channel Access for Cognitive UAV Networks

—Designing clustered unmanned aerial vehicle (UAV) communication networks based on cognitive radio (CR) and reinforcement learning can signiﬁcantly improve the intelligence level of clustered UAV communication networks and the robustness of the system in a time-varying environment. Among them, designing smarter systems for spectrum sensing and access is a key research issue in CR. Therefore, we focus on the dynamic cooperative spectrum sensing and channel access in clustered cognitive UAV (CUAV) communication networks. Due to the lack of prior statistical information on the primary user (PU) channel occupancy state, we propose to use multi-agent reinforcement learning (MARL) to model CUAV spectrum competition and cooperative decision-making problem in this dynamic scenario, and a return function based on the weighted compound of sensing-transmission cost and utility is introduced to characterize the real-time rewards of multi-agent game. On this basis, a time slot multi-round revisit exhaustive search algorithm based on virtual controller (VC-EXH), a Q-learning algorithm based on independent learner (IL-Q) and a deep Q-learning algorithm based on independent learner (IL-DQN) are respectively proposed. Further, the information exchange overhead, execution complexity and convergence of the three algorithms are brieﬂy analyzed. Through the numerical simulation analysis, all three algorithms can converge quickly, signiﬁcantly improve system performance and increase the utilization of idle spectrum resources.
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions

Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization performance across variety of different driving scenarios is still largely an open problem. In particular, getting good performance on complex road and weather conditions require exhaustive tuning and computation time. Curriculum RL, which focuses on solving simpler automation tasks in order to transfer knowledge to complex tasks, is attracting attention in RL community. The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications. For this purpose, we setup several different driving scenarios in a realistic driving simulator, with varying road complexity and weather conditions. Next, we train and evaluate performance of RL agents on different sequences of task combinations and curricula. Results show that curriculum RL can yield significant gains in complex driving tasks, both in terms of driving performance and sample complexity. Results also demonstrate that different curricula might enable different benefits, which hints future research directions for automated curriculum training.
Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning

The recent global outbreak of covid-19 is affecting many countries around the world. Due to the growing number of newly infected individuals and the health-care system bottlenecks, it will be useful to predict the upcoming number of patients. This study aims to efficiently forecast the is used to estimate new cases, number of deaths, and number of recovered patients in Iran for 180 days, using the official dataset of the Iranian Ministry of Health and Medical Education and the impact of control measures on the spread of COVID-19. Four different types of forecasting techniques, time series, and machine learning algorithms, are developed and the best performing method for the given case study is determined. Under the time series, we consider the four algorithms including Prophet, Long short-term memory, Autoregressive, Autoregressive Integrated Moving Average models. On comparing the different techniques, we found that deep learning methods yield better results than time series forecasting algorithms. More specifically, the least value of the error measures is observed in seasonal ANN and LSTM models. Our findings showed that if precautionary measures are taken seriously, the number of new cases and deaths will decrease, and the number of deaths in September 2021 will reach zero.
Chebyshev-Fibonacci polynomial relations using generating functions

The main object of the paper is to reveal connections between Chebyshev polynomials of the ﬁrst and second kinds and Fibonacci polynomials introduced by Catalan. This is achieved by relating the respective (ordinary and exponential) generating functions to each other. As a consequence, we also establish new combinatorial identities for balancing polynomials and Fibonacci (Lucas) numbers.
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation

Effective management of urban traffic is important for any smart city initiative. Therefore, the quality of the sensory traffic data is of paramount importance. However, like any sensory data, urban traffic data are prone to imperfections leading to missing measurements. In this paper, we focus on inter-region traffic data completion. We model the inter-region traffic as a spatiotemporal tensor that suffers from missing measurements. To recover the missing data, we propose an enhanced CANDECOMP/PARAFAC (CP) completion approach that considers the urban and temporal aspects of the traffic. To derive the urban characteristics, we divide the area of study into regions. Then, for each region, we compute urban feature vectors inspired from biodiversity which are used to compute the urban similarity matrix. To mine the temporal aspect, we first conduct an entropy analysis to determine the most regular time-series. Then, we conduct a joint Fourier and correlation analysis to compute its periodicity and construct the temporal matrix. Both urban and temporal matrices are fed into a modified CP-completion objective function. To solve this objective, we propose an alternating least square approach that operates on the vectorized version of the inputs. We conduct comprehensive comparative study with two evaluation scenarios. In the first one, we simulate random missing values. In the second scenario, we simulate missing values at a given area and time duration. Our results demonstrate that our approach provides effective recovering performance reaching 26% improvement compared to state-of-art CP approaches and 35% compared to state-of-art generative model-based approaches.
The Public Life of Data: Investigating Reactions to Visualizations on Reddit

This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people’s reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
Lasry-Lions Envelopes and Nonconvex Optimization: A Homotopy Approach

In large-scale optimization, the presence of nonsmooth and nonconvex terms in a given problem typically makes it hard to solve. A popular approach to address nonsmooth terms in convex optimization is to approximate them with their respective Moreau envelopes. In this work, we study the use of Lasry–Lions double envelopes to approximate nonsmooth terms that are also not convex. These envelopes are an extension of the Moreau ones but exhibit an additional smoothness property that makes them amenable to fast optimization algorithms. Lasry–Lions envelopes can also be seen as an “intermediate” between a given function and its convex envelope, and we make use of this property to develop a method that builds a sequence of approximate subproblems that are easier to solve than the original problem. We discuss convergence properties of this method when used to address composite minimization problems; additionally, based on a number of experiments, we discuss settings where it may be more useful than classical alternatives in two domains: signal decoding and spectral unmixing.
LARNet: Lie Algebra Residual Network for Profile Face Recognition

Due to large variations between proﬁle and frontal faces, proﬁle-based face recognition remains as a tremendous challenge in many practical vision scenarios. Traditional techniques address this challenge either by synthesizing frontal faces or by pose-invariants learning. In this paper, we pro-pose a novel method with Lie algebra theory to explore how face rotation in the 3D space affects the deep feature generation process of convolutional neural networks (CNNs). We prove that face rotation in the image space is equivalent to an additive residual component in the feature space of CNNs, which is determined solely by the rotation. Based on this theoretical ﬁnding, we further design a Lie algebraic residual network (LARNet) for tackling proﬁle-based face recognition. Our LARNet consists of a residual subnet for decoding rotation information from input face images, and a gating subnet to learn rotation magnitude for controlling the number of residual components contributing to the feature learning process. Comprehensive experimental evaluations on frontal-proﬁle face datasets and general face recognition datasets demonstrate that our method consistently outperforms the state-of-the-arts.
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

In this paper, we present a novel unpaired point cloud completion network, named Cycle4Completion, to infer the complete geometries from a partial 3D object. Previous unpaired completion methods merely focus on the learning of geometric correspondence from incomplete shapes to complete shapes, and ignore the learning in the reverse direction, which makes them suffer from low completion accuracy due to the limited 3D shape understanding ability. To address this problem, we propose two simultaneous cycle transformations between the latent spaces of complete shapes and incomplete ones. Specifically, the first cycle transforms shapes from incomplete domain to complete domain, and then projects them back to the incomplete domain. This process learns the geometric characteristic of complete shapes, and maintains the shape consistency between the complete prediction and the incomplete input. Similarly, the inverse cycle transformation starts from complete domain to incomplete domain, and goes back to complete domain to learn the characteristic of incomplete shapes. We experimentally show that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods. Code will be available at https://github.com/diviswen/Cycle4Completion.