Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary material to "Catalog of NOx emissions from point sources as derived from the divergence of the NO2 flux for TROPOMI"

Abstract. We present version 1.0 of a global catalog of NOx emissions from point sources, derived from TROPOMI measurements of tropospheric NO2 for 2018–2019. The identification of sources and quantification of emissions are based on the divergence (spatial derivative) of the mean horizontal flux, which is highly sensitive for point sources like power plant exhaust stacks. The catalog lists 451 locations which could be clearly identified as NOx point source by a fully automated algorithm, while ambiguous cases as well as area sources such as Megacities are skipped. 242 of these point sources could be automatically matched to power plants. Other NOx point sources listed in the catalog are metal smelters, cement plants, or industrial areas. The four largest localized NOx emitters are all coal combustion plants in South Africa. About 1/4 of all detected point sources are located in the Indian subcontinent and are mostly associated with power plants. The catalog is incomplete, mainly due to persisting gaps in the TROPOMI NO2 product at some coastlines, inaccurate or complex wind fields in coastal and mountainous regions, and high noise in the divergence maps for high background pollution. The derived emissions are generally too low, lacking a factor of up to 2, mainly due to a low bias of TROPOMI NO2 columns. Still, the catalog has high potential for checking and improving emission inventories, as it provides accurate and independent up-to-date information on the location of sources of NOx, and thus also CO2. The catalog of NOx emissions from point sources is freely available at https://doi.org/10.26050/WDCC/Quant_NOx_TROPOMI (Beirle et al., 2020).
Varieties of contextuality based on complexion, quantum probabilities, and structural non-embeddability

Contextuality can either be synthetically deﬁned in terms of outcome conditionality on the measurement conditions, or in terms of non-classical probability distributions. Another logico-algebraic “strong” form of contextuality characterizes collections of quantum observables that have no faithfully embedding into (extended) Boolean algebras. Any of these forms indicate a classical in-or underdetermination that can be termed “value indeﬁnite”, and formalized by partial functions of theoretical computer sciences.
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner. However, previous approaches do not well exploit the interactive relations among three subtasks and do not pertinently leverage the easily available document-level labeled domain/sentiment knowledge, which restricts their performances. To address these issues, we propose a novel Iterative Multi-Knowledge Transfer Network (IMKTN) for end-to-end ABSA. For one thing, through the interactive correlations between the ABSA subtasks, our IMKTN transfers the task-specific knowledge from any two of the three subtasks to another one at the token level by utilizing a well-designed routing algorithm, that is, any two of the three subtasks will help the third one. For another, our IMKTN pertinently transfers the document-level knowledge, i.e., domain-specific and sentiment-related knowledge, to the aspect-level subtasks to further enhance the corresponding performance. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of our approach.
Retrospective Interference Regeneration Schemes for Relay-Aided K-user MIMO Downlink Networks

To accommodate the explosive growth of the Internet-of-Things (IoT), incorporating interference alignment (IA) into existing multiple access (MA) schemes is under investigation. However, when it is applied in MIMO networks to improve the system compacity, the incoming problem regarding information delay arises which does not meet the requirement of low-latency. Therefore, in this paper, we first propose a new metric, degree of delay (DoD), to quantify the issue of information delay, and characterize DoD for three typical transmission schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA), and retrospective interference alignment (RIA). By analyzing DoD in these schemes, its value mainly depends on three factors, i.e., delay sensitive factor, size of data set, and queueing delay slot. The first two reflect the relationship between quality of service (QoS) and information delay sensitivity, and normalize time cost for each symbol, respectively. These two factors are independent of the transmission schemes, and thus we aim to reduce the queueing delay slot to improve DoD. Herein, three novel joint IA schemes are proposed for MIMO downlink networks with different number of users. That is, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme (HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR). Based on the first scheme, the second scheme extends the application scenarios from $2$-user to $K$-user while causing heavy computational burden. The third scheme relieves such computational burden, though it has certain degree of freedom (DoF) loss due to insufficient utilization of space resources.
Stable Formality Quasi-isomorphisms for Hochschild Cochains

Collective dynamics can be observed among many animal species, and have given rise in the last decades to an active and interdisciplinary field of study. Such behaviors are often modeled by active matter, in which each individual is self-driven and tends to update its velocity depending on the one of its neighbors. In a classical model introduced by Vicsek and al., as well as in numerous related active matter models, a phase transition between chaotic behavior at high temperature and global order at low temperature can be observed. Even though ample evidence of these phase transitions has been obtained for collective dynamics, from a mathematical standpoint, such active systems are not fully understood yet. Significant progress has been achieved in the recent years under an assumption of mean-field interactions, however to this day, few rigorous results have been obtained for models involving purely local interactions. In this paper, as a first step towards the mathematical understanding of active microscopic dynamics, we describe a lattice active particle system, in which particles interact locally to align their velocities. We obtain rigorously, using the formalism developed for hydrodynamic limits of lattice gases, the scaling limit of this out-of-equilibrium system. This article builds on the multi-type exclusion model introduced by Quastel by detailing his proof and incorporating several generalizations, adding significant technical and phenomenological difficulties.
Spatial Autocorrelation and the Dynamics of the Mean Center of COVID-19 Infections in Lebanon

In this paper we study the spatial spread of the COVID-19 infection in Lebanon. We inspect the spreading of the daily new infections across the 26 administrative districts of the country, and implement the univariate Moran’s I statistics in order to analyze the tempo-spatial clustering of the infection in relation to various variables parameterized by adjacency, proximity, population, population density, poverty rate and poverty density. We find out that except for the poverty rate, the spread of the infection is clustered and associated to those parameters with varying magnitude for the time span between July (geographic adjacency and proximity) or August (population, population density and poverty density) through October. We also determine the temporal dynamics of geographic location of the mean center of new and cumulative infections since late March. The understanding of the spatial, demographic and geographic aspects of the disease spread over time allows for regionally and locally adjusted health policies and measures that would provide higher levels of social and health safety in the fight against the pandemic in Lebanon.
Arms Race in Adversarial Malware Detection: A Survey

Malicious software (malware) is a major cyber threat that has to be tackled with Machine Learning (ML) techniques because millions of new malware examples are injected into cyberspace on a daily basis. However, ML is vulnerable to attacks known as adversarial examples. In this article, we survey and systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified conceptual framework of assumptions, attacks, defenses, and security properties. This not only leads us to map attacks and defenses to partial order structures, but also allows us to clearly describe the attack-defense arms race in the AMD context. We draw a number of insights, including: knowing the defender’s feature set is critical to the success of transfer attacks; the effectiveness of practical evasion attacks largely depends on the attacker’s freedom in conducting manipulations in the problem space; knowing the attacker’s manipulation set is critical to the defender’s success; and the effectiveness of adversarial training depends on the defender’s capability in identifying the most powerful attack. We also discuss a number of future research directions.
Quantum steering as a witness of quantum scrambling

Jhen-Dong Lin,1,2,* Wei-Yu Lin,3,* Huan-Yu Ku ,1,2 Neill Lambert,4 Yueh-Nan Chen ,1,2,† and Franco Nori 4,5,6 1Department of Physics, National Cheng Kung University, 701 Tainan, Taiwan 2Center for Quantum Frontiers of Research & Technology, NCKU, 70101 Tainan, Taiwan 3Department of Electrical Engineering, National Taiwan University, 106 Taipei, Taiwan 4Theoretical Quantum Physics Laboratory, RIKEN Cluster for Pioneering Research, Wako-shi, Saitama 351-0198, Japan 5RIKEN Center for Quantum Computing (RQC), Wakoshi, Saitama 351-0198, Japan 6Department of Physics, University of Michigan, Ann Arbor, Michigan 48109-1040, USA
Optimal dynamic allocation of rental and sales inventory for fashion apparel products

Abstract There is a growing trend towards renting rather than permanent ownership of various product categories such as designer clothes and accessories. In this article, we study an emerging retail business model that simultaneously serves rental and sales markets. Specifically, we consider a retailer that primarily focuses on renting while also selectively meeting incidental sales demand. Once a unit is sold, the firm forgoes potentially recurring rental revenues from that unit during the remaining periods. Therefore, it is critical for a retailer to dynamically decide how much of its inventory to allocate for sales and rentals at each period. We first develop a consumer choice model that determines the fraction of the market that chooses renting over purchasing. We characterize the optimal inventory allocation policy and explore how market characteristics and prices impact inventory allocation. We discuss the value of dynamic allocation and observe that the profit improvement can be substantial. In addition, we propose a simple and efficient heuristic policy. Finally, we extend our analysis to study the optimal allocation policies for (i) a retailer that is primarily a seller that selectively meets rental demand, and (ii) a retailer that does not enforce any prioritization between rental and sales demand.
Excess Capacity, Marginal q and Corporate Investment

Theory predicts that when managers expect the firm to operate below full capacity, marginal q will be less than average q as the prospect of underutilizing new capital reduces the marginal benefit of investing. To control for this potential bias in average q, we augment the q model with a proxy for capacity utilization. This adjustment substantially improves the explanatory power of investment regressions relative to traditional models. Moreover, our findings indicate that investment is remarkably sensitive to capacity utilization. These results, combined with the erosion in capacity utilization over time, seem to explain the puzzling secular decline in the rate of corporate investment despite a material increase in average q. These empirical findings hold across all G7 countries and are robust across a variety of dimensions.
Testing DBMS Performance with Mutations

Because database systems are the critical component of modern data-intensive applications, it is important to ensure that they operate correctly. To this end, developers extensively test these systems to eliminate bugs that negatively affect functionality. In addition to functional bugs, however, there is another important class of bugs: performance bugs. These bugs negatively affect the response time of a database system and can therefore affect the overall performance of the system. Despite their impact on end-user experience, performance bugs have received considerably less attention than functional bugs. In this paper, we present AMOEBA, a system for automatically detecting performance bugs in database systems. The core idea behind AMOEBA is to construct query pairs that are semantically equivalent to each other and then compare their response time on the same database system. If the queries exhibit a significant difference in their runtime performance, then the root cause is likely a performance bug in the system. We propose a novel set of structure and predicate mutation rules for constructing query pairs that are likely to uncover performance bugs. We introduce feedback mechanisms for improving the efficacy and computational efficiency of the tool. We evaluate AMOEBA on two widely-used DBMSs, namely PostgreSQL and CockroachDB. AMOEBA has discovered 20 previously-unknown performance bugs, among which developers have already confirmed 14 and fixed 4.
Parametric Tracking of Electrical Current Components Using Gradient Descent Algorithm

In the last few years, Motor Current Signature Analysis (MCSA) has proven to be an effective method for electrical machines condition monitoring. Indeed, compared to vibration and temperature analysis, current measurement proves to be a convenient and non-invasive alternative. Moreover, it has proven to be a reliable method since many mechanical and electrical faults manifest as side-band spectral components generated around the fundamental frequency component of the motor’s current. These components are called interharmonics and they are a major focus of fault detection using MCSA. However, the main drawback of this approach is that the interference of other more prevalent components such as the fundamental and noise components can obstruct the effect of interharmonics in the spectrum and may therefore affect fault detection accuracy. Thus, we propose in this paper an alternative approach that aims to decompose the different current components using a model based on a Vandermonde matrix, in order to monitor each component independently. Then, the tracking of each distinct component in time and spectral domains is implemented. This is achieved by estimating their respective relevant parameters using the Gradient Descent algorithm. This method has been favorably compared to an existing estimation algorithm (MUSIC) and its efficiency has been validated. The results of this work prove to be promising and establish the parametric tracking of the electrical current components using the Gradient Descent algorithm as a reliable monitoring approach.
Novel elementary excitations in spin-1/2 antiferromagnets on the triangular lattice

We discuss spin-$\frac12$ Heisenberg antiferromagnet on the triangular lattice using the recently proposed bond-operator technique (BOT). We use the variant of the BOT which takes into account all spin degrees of freedom in the magnetic unit cell containing three spins. Apart from conventional magnons known from the spin-wave theory (SWT), there are novel high-energy collective excitations in the BOT which are built from high-energy excitations of the magnetic unit cell. We obtain also another novel high-energy quasiparticle which has no counterpart not only in the SWT but also in the harmonic approximation of the BOT. All observed elementary excitations produce visible anomalies in dynamical spin correlators. We show that quantum fluctuations considerably change properties of conventional magnons predicted by the SWT. The effect of a small easy-plane anisotropy is discussed. The anomalous spin dynamics with multiple peaks in the dynamical structure factor is explained that was observed recently experimentally in $\rm Ba_3CoSb_2O_9$ and which the SWT could not describe even qualitatively.
Comparative Evaluation for Effectiveness Analysis of Policy Based Deep Reinforcement Learning Approaches

Deep Reinforcement Learning (DRL) has proven to be a very strong technique with results in various applications in recent years. Especially the achievements in the studies in the field of robotics show that much more progress will be made in this field. Undoubtedly, policy choices and parameter settings play an active role in the success of DRL. In this study, an analysis has been made on the policies used by examining the DRL studies conducted in recent years. Policies used in the literature are grouped under three different headings: value-based, policy-based and actor-critic. However, the problem of moving a common target using Newton's law of motion of collaborative agents is presented. Trainings are carried out in a frictionless environment with two agents and one object using four different policies. Agents try to force an object in the environment by colliding it and try to move it out of the area it is in. Two-dimensional surface is used during the training phase. As a result of the training, each policy is reported separately and its success is observed. Test results are discussed in section 5. Thus, policies are tested together with an application by providing information about the policies used in deep reinforcement learning approaches. Keywords-Deep Reinforcement Learning; Deep Learning; Multi Agent
Dynamics of isoflurane-induced vasodilation and blood flow of cerebral vasculature revealed by multi-exposure speckle imaging

Background Anesthetized animal models are used extensively during neurophysiological and behavioral studies despite systemic effects from anesthesia that undermine both accurate interpretation and translation to awake human physiology. The majority of work examining the impact of anesthesia on cerebral blood flow (CBF) has been restricted to before and after measurements with limited spatial resolution. New Method We used multi-exposure speckle imaging (MESI), an advanced form of laser speckle contrast imaging (LSCI), to characterize the dynamics of isoflurane anesthesia induction on cerebral vasculature and blood flow in the mouse brain. Results The large anatomical changes caused by isoflurane are depicted with wide-field imagery and video highlighting the induction of general anesthesia. Within minutes of exposure, both vessel diameter and blood flow increased drastically compared to the awake state and remained elevated for the duration of imaging. An examination of the dynamics of anesthesia induction reveals that blood flow increased faster in arteries than in veins or parenchyma regions. Comparison with Existing Methods MESI offers robust hemodynamic measurements across large fields-of-view and high temporal resolutions sufficient for continuous visualization of cerebrovascular events featuring major changes in blood flow. Conclusion The large alterations caused by isoflurane anesthesia to the cortical vasculature and CBF are readily characterized using MESI. These changes are unrepresentative of normal physiology and provide further evidence that neuroscience experiments would benefit from transitioning to un-anesthetized awake animal models.
Uncorrected Proof ) Title : Brain Wave Patterns in Patients With Chronic Low Back Pain : A Case-Control Study Running Title : Brain wave patterns and chronic LBP

Introduction: Research evidence indicates that maladaptive reorganization of the brain plays a critical role in amplifying pain experiences and pain chronification; there is, however, no clear evidence of change in brain wave activity among patients with chronic low back pain (CLBP). The objective of this study was to assess brain wave activity in patients with CLBP, compared to healthy controls. Methods: Twenty-five patients with CLBP and twenty-four healthy controls participated in the study. A quantitative Electroencephalography device was used to assess brain wave activity in eyes-open and eyes-closed conditions (EO and EC). The regional absolute and relative power of brain waves were compared between the groups. Results: Our results showed significant increases in the absolute power of theta (F=5.905, p=0.019), alpha (F=5.404, p=0.024) waves in patients with CLBP compared to healthy subjects in both EC and EO condition. Patients with CLBP showed a reduced delta absolute power in the frontal region (F=5.852, p=0.019) and augmented delta absolute power in the central region (F=5.597, p=0.022) in the EO condition. An increased delta absolute power was observed in the frontal (F=7.563 p=0.008), central (F=10.430, p=0.002) and parietal (F=4.596, p=0.037) regions in patients with CLBP compared to the healthy subjects in the EC condition. In the EC condition, significant increases in theta relative power (F=4.680, p=0.036) in the parietal region was also found in patients with CLBP. Conclusion: Increased absolute power of brain waves in people with CLBP may indicate cortical overactivity and changes in pain processing mechanism in these patients.
Design and optimization of soft gripper for material handling using robotic arm

Various numbers of the grippers are used in different aspects of our life which use especially in industrial robotic. This article studies the real design of the grippers and different types of soft robotics and their beneficial uses for our life. Every gripper consists of different module it depend on the need of this grippers. The study focuses on the development of robotic and its interactions and how it becoming so familiar and actually needed in our life due to the technology revolution. Moreover, the article explains the intelligent response and motion characteristics of organisms of nature that have the real sources of the creation of scientists. Key Word: Soft gripper, Flexible gripper, Actuators, grasp, and sensors.