Miracle-dz/UniFAR-FTUs huggingface dataset:
A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers

This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology. The proposed methodology thus comprises all the steps necessary to develop, deploy and evaluate an adaptive traffic signal controller -- from simulation setup to problem formulation and experimental design. We illustrate the proposed methodology in two simple scenarios, highlighting how its different steps address limitations found in the current literature.
A practice-oriented overview of call center workforce planning

We give an overview of the practice and science of call center workforce planning, in which we evaluate the commonly used methods by their quality and the theory by its applicability. As such, this paper is useful for developers and consultants interested in the background and advanced methodology of workforce management and for researchers interested in practically relevant science.
Audio-Visual Biometric Recognition and Presentation Attack Detection: A Comprehensive Survey

Biometric recognition is a trending technology that uses unique characteristics data to identify or verify/authenticate security applications. Amidst the classically used biometrics, voice and face attributes are the most propitious for prevalent applications in day-to-day life because they are easy to obtain through restrained and user-friendly procedures. The pervasiveness of low-cost audio and face capture sensors in smartphones, laptops, and tablets has made the advantage of voice and face biometrics more exceptional when compared to other biometrics. For many years, acoustic information alone has been a great success in automatic speaker verification applications. Meantime, the last decade or two has also witnessed a remarkable ascent in face recognition technologies. Nonetheless, in adverse unconstrained environments, neither of these techniques achieves optimal performance. Since audio-visual information carries correlated and complementary information, integrating them into one recognition system can increase the system’s performance. The vulnerability of biometrics towards presentation attacks and audio-visual data usage for the detection of such attacks is also a hot topic of research. This paper made a comprehensive survey on existing state-of-the-art audio-visual recognition techniques, publicly available databases for benchmarking, and Presentation Attack Detection (PAD) algorithms. Further, a detailed discussion on challenges and open problems is presented in this field of biometrics.
Emergent Communication under Competition

The literature in modern machine learning has only negative results for learning to communicate between competitive agents using standard RL. We introduce a modified sender-receiver game to study the spectrum of partially-competitive scenarios and show communication can indeed emerge in a competitive setting. We empirically demonstrate three key takeaways for future research. First, we show that communication is proportional to cooperation, and it can occur for partially competitive scenarios using standard learning algorithms. Second, we highlight the difference between communication and manipulation and extend previous metrics of communication to the competitive case. Third, we investigate the negotiation game where previous work failed to learn communication between independent agents (Cao et al., 2018). We show that, in this setting, both agents must benefit from communication for it to emerge; and, with a slight modification to the game, we demonstrate successful communication between competitive agents. We hope this work overturns misconceptions and inspires more research in competitive emergent communication.
Use of Kraft Black Liquors From a Pulp Mill for the Production of Soil Conditioners

The possibility of utilizing kraft black liquors generated in a paper mill, using Eucaliptus globulus as raw material, for the production of a nitro-humic soil conditioner was studied. Two processes were considered: (1) oxidation of the kraft black liquors with nitric acid; and (2) precipitation of the lignin contained in kraft black liquors with CO2 and further oxidation of this lignin by nitric acid. The second process was more efficient regarding product yield and safety of operation. Screening tests at different operating conditions of the latter process indicate that high quality nitro-humic materials can be obtained.
Rigidity of topological entropy of boundary maps associated to Fuchsian groups

Given a closed, orientable surface of constant negative curvature and genus $g \ge 2$, we study a family of generalized Bowen-Series boundary maps and prove the following rigidity result: in this family the topological entropy is constant and depends only on the genus of the surface. We give an explicit formula for this entropy and show that the value of the topological entropy also stays constant in the Teichm\"uller space of the surface. The proofs use conjugation to maps of constant slope.
Constraints on the time variation of the speed of light using Pantheon dataset

Both the absolute magnitude of type Ia supernovae (SNe Ia) and the luminosity distance of them are modified in the context of the minimally extended varying speed of light (meVSL) model compared to those of general relativity (GR). We have analyzed the likelihood of various dark energy models under meVSL by using the Pantheon SNe Ia data. Both $\omega$CDM and CPL parameterization dark energy models indicate a cosmic variation of the speed of light at the 1-$\sigma$ level. For $\Omega_{\text{m} 0} = 0.30, 0.31$, and 0.32 with $(\omega_0 \,, \omega_a) = (-1 \,, 0)$, 1-$\sigma$ range of $\dot{\tilde{c}}_0/\tilde{c}_0 \, (10^{-13} \, \text{yr}^{-1}) $ are (-8.76 \,, -0.89), (-11.8 \,, 3.93), and (-14.8 \,, -6.98), respectively. Meanwhile, 1-$\sigma$ range of $\dot{\tilde{c}}_0/\tilde{c}_0 (10^{-12} \, \text{yr}^{-1}) $ for the CPL dark energy models with $-1.05 \leq \omega_{0} \leq -0.95$ and $0.28 \leq \Omega_{\text{m} 0} \leq 0.32$, are (-6.31\,, -2.98). The value of $\tilde{c}$ at $z = 3$ can be larger than that of the present by $0.2 \sim 3$ \% for $\omega$CDM models and $5 \sim 13$ \% for CPL models. We also obtain $-25.6 \leq \dot{\tilde{G}}_0/\tilde{G}_0 \, (10^{-12} \, \text{yr}^{-1}) \leq -0.36$ for viable models except for CPL model for $\Omega_{\text{m} 0} = 0.28$. We obtain the increasing rate of the gravitational constant as $1.65 \leq \dot{\tilde{G}}_0/\tilde{G}_0 \, (10^{-12} \, \text{yr}^{-1}) \leq 3.79$ for that model.
Asymmetric Si-Slot Coupler With Nonreciprocal Response Based on Graphene Saturable Absorption

We present the study of a proof-of-concept integrated device that can be used as a nonlinear broadband isolator. The device is based on the asymmetric loading of a highly-confining silicon-slot photonic coupler with graphene layers, whose ultrafast and low-threshold saturable absorption can be exploited for nonreciprocal transmission between the cross-ports of the coupler. The structure is essentially a non-Hermitian system, whose exceptional points are briefly discussed. The nonlinear device is modeled with a coupled Schrödinger equation system whose validity is checked by full-vector finite element-based beam-propagation method simulations in CW. The numerically computed performance reveals a nonreciprocal intensity range (NRIR) in the vicinity of 100 mW peak power with a bandwidth spanning tens of nanometers, from CW down to ps-long pulses. Finally, the combination of saturable absorption and self-phase modulation (Kerr effect) in graphene is studied, indicating the existence of two NRIR with opposite directionality.
The Filtered Gaussian Primitive Diamond Channel

We investigate the special case of diamond relay comprising a Gaussian channel with identical frequency response between the user and the relays and fronthaul links with limited rate from the relays to the destination. We use the oblivious compress and forward (CF) with distributed compression and decode and forward (DF) where each relay decodes the whole message and sends half of its bits to the destination. We derive the achievable rate by using optimal time-sharing between DF and CF, which is advantageous over superposition of CF and DF. The optimal time sharing proportion between DF and CF and power and rate allocations are different at each frequency and are fully determined.
VIO-Aided Structure from Motion Under Challenging Environments

In this paper, we present a robust and efficient Structure from Motion pipeline for accurate 3D reconstruction under challenging environments by leveraging the camera pose information from a visual-inertial odometry. Specifically, we propose a geometric verification method to filter out mismatches by considering the prior geometric configuration of candidate image pairs. Furthermore, we introduce an efficient and scalable reconstruction approach that relies on batched image registration and robust bundle adjustment, both leveraging the reliable local odometry estimation. Extensive experimental results show that our pipeline performs better than the state-of-the-art SfM approaches in terms of reconstruction accuracy and robustness for challenging sequential image collections.
Switching off microcavity polariton condensate near the exceptional point

Gain and loss modulation are ubiquitous in nature. An exceptional point arises when both the eigenvectors and eigenvalues coalesce, which in a physical system can be achieved by engineering the gain and loss coefficients, leading to a wide variety of counter-intuitive phenomena. In this work we demonstrate the existence of an exceptional point in an exciton polariton condensate in a double-well potential. Remarkably, near the exceptional point, the polariton condensate localized in one potential well can be switched off by an additional optical excitation in the other well with very low (far below threshold) laser power which surprisingly induces additional loss into the system. Increasing the power of the additional laser leads to a situation in which gain dominates in both wells again, such that the polaritons re-condense with almost the same density in the two potential wells. Our results offer a simple way to optically manipulate the polariton condensation process in a double-well potential structure. Extending such configuration to complex potential well lattices offers exciting prospects to explore high-order exceptional points and non-Hermitian topological photonics in a non-equilibrium many-body system.
Ja n 20 21 Consistent specification testing under spatial dependence ∗

We propose a series-based nonparametric specification test for a regression function when data are spatially dependent, the ‘space’ being of a general economic or social nature. Dependence can be parametric, parametric with increasing dimension, semiparametric or any combination thereof, thus covering a vast variety of settings. These include spatial error models of varying types and levels of complexity. Under a new smooth spatial dependence condition, our test statistic is asymptotically standard normal. To prove the latter property, we establish a central limit theorem for quadratic forms in linear processes in an increasing dimension setting. Finite sample performance is investigated in a simulation study and empirical examples illustrate the test with real-world data.
Automatic Monitoring Social Dynamics During Big Incidences: A Case Study of COVID-19 in Bangladesh

Newspapers are trustworthy media where people get the most reliable and credible information compared with other sources. On the other hand, social media often spread rumors and misleading news to get more traffic and attention. Careful characterization, evaluation, and interpretation of newspaper data can provide insight into intrigue and passionate social issues to monitor any big social incidence. This study analyzed a large set of spatio-temporal Bangladeshi newspaper data related to the COVID-19 pandemic. The methodology included volume analysis, topic analysis, automated classification, and sentiment analysis of news articles to get insight into the COVID-19 pandemic in different sectors and regions in Bangladesh over a period of time. This analysis will help the government and other organizations to figure out the challenges that have arisen in society due to this pandemic, what steps should be taken immediately and in the post-pandemic period, how the government and its allies can come together to address the crisis in the future, keeping these problems in mind.
Nested sampling methods

Nested sampling (NS) computes parameter posterior distributions and makes Bayesian model comparison computationally feasible. Its strengths are the unsupervised navigation of complex, potentially multi-modal posteriors until a well-defined termination point. A systematic literature review of nested sampling algorithms and variants is presented. We focus on complete algorithms, including solutions to likelihood-restricted prior sampling, parallelisation, termination and diagnostics. The relation between number of live points, dimensionality and computational cost is studied for two complete algorithms. A new formulation of NS is presented, which casts the parameter space exploration as a search on a tree data structure. Previously published ways of obtaining robust error estimates and dynamic variations of the number of live points are presented as special cases of this formulation. A new online diagnostic test is presented based on previous insertion rank order work. The survey of nested sampling methods concludes with outlooks for future research.
Piezoelectric Energy Harvesting: A Systematic Review of Reviews

In the last decade, an enormous amount of attention has been paid to piezoelectric harvesters due to their flexibility in design and the increasing need for small-scale energy generation. As a result, various energy review papers have been presented by many researchers to cover different aspects of piezoelectric-based energy harvesting, including piezo-materials, modeling approaches, and design points for various applications. Most of these papers have tried to shed light on recent progress in related interdisciplinary fields, and to pave the road for future prospects in the development of these technologies. However, there are some missing parts, overlaps, and even some contradictions in these review papers. In the present review of these review articles, recommendations for future research directions suggested by the review papers have been systematically summed up under one umbrella. In the final section, topics for missing review papers, concluding remarks on outlooks and possible research topics, as well as potentially misleading strategies, have been presented. The review papers have been evaluated based on their merits and subcategories and the authors’ choice papers have been presented for each section based on clear classification criteria.
Ultrasonic Lesions in Mammalian Central Nervous System

The ultrasonic dosages given in W. J. Fry et al., "Ultrasonic lesions in the mammalian central nervous system" [Science 122, 517 (1955)] should be corrected as follows: For the lesion illusrated in Fig. 1, the dosage was 40 atm acoustic pressure amplitude and 3.9(102) cm/sec acoustic particle velocity amplitude. For the lesions illustrated in Figs. 2 and 3, the dosage was 41 atm acoustic pressure amplitude and 4.0(102) cm/sec acoustic particle velocity amplitude.
SpanEmo: Casting Multi-label Emotion Classification as Span-prediction

Emotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and security. Current approaches to ER, mainly classify emotions independently without considering that emotions can co-exist. Such approaches overlook potential ambiguities, in which multiple emotions overlap. We propose a new model “SpanEmo” casting multi-label emotion classification as span-prediction, which can aid ER models to learn associations between labels and words in a sentence. Furthermore, we introduce a loss function focused on modelling multiple co-existing emotions in the input sentence. Experiments performed on the SemEval2018 multi-label emotion data over three language sets (i.e., English, Arabic and Spanish) demonstrate our method’s effectiveness. Finally, we present different analyses that illustrate the benefits of our method in terms of improving the model performance and learning meaningful associations between emotion classes and words in the sentence.
A modified Kačanov iteration scheme with application to quasilinear diffusion models

The classical Kacanov scheme for the solution of nonlinear variational problems can be interpreted as a fixed point iteration method that updates a given approximation by solving a linear problem in each step. Based on this observation, we introduce a modified Kacanov method, which allows for (adaptive) damping, and, thereby, to derive a new convergence analysis under more general assumptions and for a wider range of applications. For instance, in the specific context of quasilinear diffusion models, our new approach does no longer require a standard monotonicity condition on the nonlinear diffusion coefficient to hold. Moreover, we propose two different adaptive strategies for the practical selection of the damping parameters involved.
Explanation as a Defense of Recommendation

Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended.
3-D Underactuated Bipedal Walking via H-LIP Based Gait Synthesis and Stepping Stabilization

In this article, we holistically present a hybrid-linear inverted pendulum (H-LIP) based approach for synthesizing and stabilizing 3-D foot-underactuated bipedal walking, with an emphasis on thorough hardware realization. The H-LIP is proposed to capture the essential components of the underactuated and actuated part of the robotic walking. The robot walking gait is then directly synthesized based on the H-LIP. We comprehensively characterize the periodic orbits of the H-LIP and provably derive the stepping stabilization via its step-to-step (S2S) dynamics, which is then utilized to approximate the S2S dynamics of the horizontal state of the center of mass of the robotic walking. The approximation facilities a H-LIP based stepping controller to provide desired step sizes to stabilize the robotic walking. By realizing the desired step sizes, the robot achieves dynamic and stable walking. The approach is fully evaluated in both simulation and experiment on the 3-D underactuated bipedal robot Cassie, which demonstrates dynamic walking behaviors with both high versatility and robustness.
Securing Full-Duplex Amplify-and-Forward Relay-Aided Transmissions Through Processing-Time Optimization

We investigate physical-layer security of the full-duplex (FD) amplify-and-forward (AF) relay channel. We provide a new perspective on the problem and show that the processing time (delay) at the relay can be exploited to improve the system's security. We show that the FD AF relay channel can be seen as an intersymbol-interference (ISI) channel, hence, the discrete-Fourier transform (DFT) can be used for data modulation and demodulation to convert the frequency-selective channel into flat-fading channel per sub-channel/sub-carrier. By exploiting the fact that the channel memory needs to be cleared by inserting the cyclic-prefix, Alice injects an artificial-noise (AN) signal that hurts the eavesdropping nodes only. The strength of this AN signal and its interference rank are controlled by the relay's processing time.
Using the redshift evolution of the Lyman-α effective opacity as a probe of dark matter models

Lyman-α forest data are known to be a good probe of the small scale matter power. In this paper, we explore the redshift evolution of the observable effective optical depth τeff (z) from the Lyman-α data as a discriminator between dark matter models that differ from the ΛCDM model on small scales. We consider the thermal warm dark matter (WDM) and the ultra-light axion (ULA) models for the following set of parameters: the mass of ULA, ma ≃ 10-24–5 × 10-22 eV and WDM mass, m_ wdm = 0.1 – 4.6 keV. We simulate the line-of-sight HI density and velocity fields using semi-analytic methods. The simulated effective optical depth for the alternative dark matter models diverges from the ΛCDM model for z ≳ 3, which provides a meaningful probe of the matter power at small scales. Using likelihood analysis, we compare the simulated data with the high-resolution Lyman-α forest data in the redshift range 2 < z < 4.2. The analysis yields the following 1σ bounds on dark matter masses: m_ wdm > 0.7 keV and m_ a > 2 × 10-23 eV. To further test the efficacy of our proposed method, we simulate synthetic data sets compatible with the ΛCDM model in the redshift range 2 ≤ z ≤ 6.5 and compare with theory. The 1σ bounds obtained are significantly tighter: m_ wdm > 1.5 keV and m_ a > 7 × 10-23 eV. Although our method provides an alternative way of constraining dark matter models, we note that these bounds are weaker than those obtained by high-resolution hydrodynamical simulations.
Machine Learning for Strategic Inference

We introduce a framework to study the interaction between a strategic players and a market whose behavior is guided by an algorithm designer. The algorithm is limited in the set of decision rules that can be fit to the data in order to prescribe actions. Even if the as-if rational reply is one of these decision rules, as-if rational play need not emerge if the algorithm is constrained. On the other hand, we show it is possible to guide behavior across a rich set of possible environments using limited details. This conclusion relies upon the strategic player (endogenously) seeking to maximize her payoff. More precisely, we show how Adaptive Boosting algorithms can be specified to induce behavior that is (approximately) as-if rational using only observed data, and in particular without needing to infer the optimal response from Bayes rule. The key property ensuring our algorithms can succeed is weak learnability, which we show holds as long as it is possible to find a best-fitting single-threshold classifier. We describe how our analysis provides a statistical perspective to the study of endogenous model misspecification.