Miracle-dz/UniFAR-FTUs huggingface dataset:
State estimation in a smart distribution system

ABSTRACT State estimation (SE) is a key enabler for any number of “smart grid” applications on the distribution system. Based on the weighted least square (WLS) approach, this paper proposed a state estimator considering different measurements among which are voltage magnitude, voltage phase-angle, branch current magnitude, branch current phase angle, load current magnitude, load current phase angle, real power flow, reactive power flow, real power and reactive power. Based on the analysis of the measurements provided by the advanced metering instrument (AMI) and phasor measurement unit (PMU), the effects of installing AMI and PMU on the estimation accuracy of SE are simulated and analysed. Simulation results indicated that (1) by taking PMU measurements into consideration, the estimation error of both voltage magnitude and angle can be greatly reduced and the optimal measurement portfolio of PMUs is the combination of voltage magnitude, voltage angle, load current magnitude and load current angle; and (2) if AMI has the same measurement accuracy as PMUs, it can reduce the estimation error of voltage magnitude obviously like PMUs but has little effect on the estimation error of voltage angle.
Aksharantar: Towards building open transliteration tools for the next billion users

We introduce Aksharantar, the largest publicly available transliteration dataset for 21 Indic languages containing 26 million transliteration pairs. We build this dataset by mining transliteration pairs from large monolingual and parallel corpora, as well as collecting transliterations from human annotators to ensure diversity of words and representation of low-resource languages. We introduce a new, large, diverse testset for Indic language transliteration containing 103k words pairs spanning 19 languages that enables fine-grained analysis of transliteration models. We train the IndicXlit model on the Aksharantar training set. IndicXlit is a single transformer-based multilingual transliteration model for roman to Indic script conversion supporting 21 Indic languages. It achieves state-of-the art results on the Dakshina testset, and establishes strong base-lines on the Aksharantar testset released along with this work. All the datasets and models are publicly available at https://indicnlp. We hope the availability of these large-scale, open resources will spur innovation for Indic language transliteration and downstream applications.
Multi-confound regression adversarial network for deep learning-based diagnosis on highly heterogenous clinical data

Automated disease detection in medical images using deep learning holds promise to improve the diagnostic ability of radiologists, but routinely collected clinical data frequently contains technical and demographic confounding factors that diﬀer between hospitals, negatively aﬀecting the robustness of diagnostic deep learning models. Thus, there is a critical need for deep learning models that can train on imbalanced datasets without overﬁtting to site-speciﬁc confounding factors. In this work, we developed a novel deep learning architecture, MUCRAN (Multi-Confound Regression Adversarial Network), to train a deep learning model on highly heterogeneous clinical data while regressing demographic and technical confounding factors. We trained MUCRAN using 16,821 clinical T1 Axial brain MRIs collected from Massachusetts General Hospital before 2019 and tested it using post-2019 data to distinguish Alzheimer’s disease (AD) patients, identiﬁed using both prescriptions of AD drugs and ICD codes, from a non-medicated control group. In external validation tests using MRI data from other hospitals, the model showed a robust performance of over 90% accuracy on newly collected data. This work shows the feasibility of deep learning-based diagnosis in real-world clinical data.
A Consistent Theory of Tachyons with Interesting Physics for Neutrinos

Working strictly within the physics theories of Special and General Relativity, I have produced a series of studies developing a consistent mathematical description of tachyons, using both classical and quantum frameworks for particles and fields. The most important choices throughout this work concern the question of which habits from the study of ordinary particles (those that are restricted to velocities less than that of light) should be kept and which should be changed. The first part of this paper notes an alternative set of theories wherein that question is answered differently from the choices of this author; and the results of that are severe in terms of physical symmetry. Following that is a broad summary of what has been accomplished in this work: this starts with the recognition that low energy tachyons will create large gravitational fields through the space-components of the energy-momentum tensor and leads to studying properties of the cosmic neutrino background. Lastly there is a discourse on the various arguments that have been given against the very possibility for tachyons to exist.
Energy Efﬁcient Over-the-Air Computation for Correlated Data in Wireless Sensor Networks

—Over-the-air computation (AirComp) enables efﬁ- cient wireless data aggregation in sensor networks by simultaneous processing of calculation and communication. This paper proposes a novel precoding method for AirComp that incorpo-rates statistical properties of sensing data, spatial correlation and heterogeneous data correlation. The design of the proposed precoding matrix requires no iterative processes so that it can be realized with low computational costs. Moreover, this method provides dimensionality reduction of sensing data to reduce communication costs per sensor. We evaluate performance of the proposed method in terms of various system parameters. The results show the superiority of the proposed method to conventional non-iterative methods in cases where the number of receive antennas at the aggregator is less than that of the total transmit antennas at the sensors.
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations

Machine learning models in safety-critical settings like healthcare are often “blackboxes”: they contain a large number of parameters which are not transparent to users. Post-hoc explainability methods where a simple, human-interpretable model imitates the behavior of these blackbox models are often proposed to help users trust model predictions. In this work, we audit the quality of such explanations for different protected subgroups using real data from four settings in finance, healthcare, college admissions, and the US justice system. Across two different blackbox model architectures and four popular explainability methods, we find that the approximation quality of explanation models, also known as the fidelity, differs significantly between subgroups. We also demonstrate that pairing explainability methods with recent advances in robust machine learning can improve explanation fairness in some settings. However, we highlight the importance of communicating details of non-zero fidelity gaps to users, since a single solution might not exist across all settings. Finally, we discuss the implications of unfair explanation models as a challenging and understudied problem facing the machine learning community.
Optimal Control as Variational Inference

In this article we address the stochastic and risk sensitive optimal control problem probabilistically and decompose and solve the probabilistic models using principles from variational inference. We demonstrate how this culminates into two separate probabilistic inference procedures that allow to iteratively infer the deterministic optimal policy. More formally a sequence of belief policies, as a probabilistic proxy for the deterministic optimal policy, is speciﬁed through a ﬁxed point iteration with the equilibrium point coinciding with the deterministic solution. These results re-establish the paradigm of Control as Inference, a concept explored and exploited originally by the Reinforcement Learning community anticipating deep rooted connections between optimal estimation and control. Although the Control as Inference paradigm already resulted in the development of several Reinforcement Learning algorithms, until now the underlying mechanism were only partially understood. For that very reason control as inference has not been well received by the control community. By exposing the underlying mechanism we aim to contribute to its general acceptance as a framework superseding optimal control. In order to exhibit its general relevance we discuss parallels with path integral control and discuss a wide range of possible applications.
A Driver-Vehicle Model for ADS Scenario-based Testing

Scenario-based testing for automated driving systems (ADS) must be able to simulate traffic scenarios that rely on interactions with other vehicles. Although many languages for high-level scenario modelling have been proposed, they lack the features to precisely and reliably control the required micro-simulation, while also supporting behavior reuse and test reproducibility for a wide range of interactive scenarios. To fill this gap between scenario design and execution, we propose the Simulated Driver-Vehicle Model (SDV) to represent and simulate vehicles as dynamic entities with their behavior being constrained by scenario design and goals set by testers. The model combines driver and vehicle as a single entity. It is based on human-like driving and the mechanical limitations of real vehicles for realistic simulation. The layered architecture of the model leverages behavior trees to express high-level behaviors in terms of lower-level maneuvers, affording multiple driving styles and reuse. Further, optimization-based maneuver planner guides the simulated vehicles towards the desired behavior. Our extensive evaluation shows the model's design effectiveness using NHTSA pre-crash scenarios, its motion realism in comparison to naturalistic urban traffic, and its scalability with traffic density. Finally, we show the applicability of SDV model to test a real ADS and to identify crash scenarios, which are impractical to represent using predefined vehicle trajectories. The SDV model instances can be injected into existing simulation environments via co-simulation.
The GPGCD Algorithm with the Bézout Matrix for Multiple Univariate Polynomials

We propose a modification of the GPGCD algorithm, which has been presented in our previous research, for calculating approximate greatest common divisor (GCD) of more than 2 univariate polynomials with real coefficients and a given degree. In transferring the approximate GCD problem to a constrained minimization problem, different from the original GPGCD algorithm for multiple polynomials which uses the Sylvester subresultant matrix, the proposed algorithm uses the B\'ezout matrix. Experiments show that the proposed algorithm is more efficient than the original GPGCD algorithm for multiple polynomials with maintaining almost the same accuracy for most of the cases.
High-order Moment Portfolio Optimization via An Accelerated Difference-of-Convex Programming Approach and Sums-of-Squares

The Mean-Variance-Skewness-Kurtosis (MVSK) portfolio optimization model is a quartic nonconvex polynomial minimization problem over a polytope, which can be formulated as a Difference-of-Convex (DC) program. In this manuscript, we investigate four DC programming approaches for solving the MVSK model. First, two DC formulations based on the projective DC decomposition and the Difference-of-Convex-Sums-of-Squares (DC-SOS) decomposition are established, where the second one is novel. Then, DCA is applied to solve these DC formulations. The convergence analysis of DCA for the MVSK model is established. Second, we propose an accelerated DCA (Boosted-DCA) for solving a general convex constrained DC program involving both smooth and nonsmooth functions. The acceleration is realized by an inexact line search of the Armijo-type along the DC descent direction generated by two consecutive iterates of DCA. The convergence analysis of the Boosted-DCA is established. Numerical simulations of the proposed four DC algorithms on both synthetic and real portfolio datasets are reported. Comparisons with KNITRO, FILTERSD, IPOPT and MATLAB fmincon optimization solvers demonstrate good performance of our methods. Particularly, two DC algorithms with DC-SOS decomposition require less number of iterations, which demonstrates that DC-SOS decomposition can provide better convex over-approximations for polynomials. Moreover, the accelerated versions indeed reduce the number of iterations and achieve the best numerical results.
Can a Hurricane Be Managed

Based on realistic estimates of geophysical conditions it is demonstrated that by practical means; (1) a hurricane’s intensity can be diminished before making landfall; (2) and other circumstances, potential hurricanes might be quenched before achieving critical strength. Under the first strategy, cold deep ocean water cools the hurricane track to weaken the hurricane. Analyses are facilitated by derivation of a novel exact solution that models a tropical depression, a mesoscale eddy, and the eye of a hurricane. Under the second strategy, it is shown. the threat of a tropical depression can be diminished, in a timely manner. Estimates of the power needed to perform timely ocean mixing show that this can be accomplished by high-performance submarines, and an exceptional coefficient of performance , O (10 4 ). The destructive power of a hurricane is functionally related to a hurricane’s maximal wind speed, V m . It is shown that a 20% reduction in maximal wind speed produces a year 50% reduction in destructive costs. Complementary deliberations show the potential for producing rainfall in relief of drought. Shown also is the opportunity to significantly modify vessel architecture to maximize the desired goals. It is the contention of this paper that a practical framework exists to pursue means by which to reduce the tragedy and devastation caused by hurricanes.
Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration

Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an eﬀective way to improve the robustness. Extending purely data-driven generative models, recent specialized models satisfy additional controllable requirements such as embedding a traﬃc sign in a driving scene by manipulating patterns implicitly in the neuron level. In this paper, we introduce a method to incorporate domain knowledge explicitly in the generation process to achieve the Semantically Adversarial Generation (SAG) . To be consistent with the composition of driving scenes, we ﬁrst categorize the knowledge into two types, the property of objects and the relationship among objects. We then propose a tree-structured variational auto-encoder (T-VAE) to learn hierarchical scene representation. By imposing semantic rules on the properties of nodes and edges in the tree structure, explicit knowledge integration enables controllable generation. We construct a synthetic example to illustrate the controllability and explainability of our method in a succinct setting. We further extend to realistic environments for autonomous vehicles: our method eﬃciently identiﬁes adversarial driving scenes against diﬀerent state-of-the-art 3D point cloud segmentation models and satisﬁes the traﬃc rules speciﬁed as the explicit knowledge.
A deep complex multi-frame filtering network for stereophonic acoustic echo cancellation

In hands-free communication system, the coupling between loudspeaker and microphone generates echo signal, which can severely influence the quality of communication. Meanwhile, various types of noise in communication environments further reduce speech quality and intelligibility. It is difficult to extract the near-end signal from the microphone signal within one step, especially in low signal-to-noise ratio scenarios. In this paper, we propose a deep complex network approach to address this issue. Specially, we decompose the stereophonic acoustic echo cancellation into two stages, including linear stereophonic acoustic echo cancellation module and residual echo suppression module, where both modules are based on deep learning architectures. A multi-frame filtering strategy is introduced to benefit the estimation of linear echo by capturing more inter-frame information. Moreover, we decouple the complex spectral mapping into magnitude estimation and complex spectrum refinement. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions.
Thermal preconditioning in a reef-building coral alleviates oxidative damage through a BI-1 mediated antioxidant response

Global coral reef decline is largely driven by the breakdown of the coral-algal symbiosis during temperature stress. Corals can acclimatize to higher temperatures, but the cellular processes underlying this ability are poorly understood. We show that preconditioning-based improvements in thermal tolerance in Pocillopora acuta are accompanied by increases in host glutathione reductase (GR) activity and expression, which prevents DNA damage. A strong correlation between GR and BI-1 expression in heat-stressed preconditioned corals and the presence of an antioxidant response element (ARE) in the GR promoter suggest BI-1 could regulate GR expression through Nrf2/ARE pathway. To fortify this link, we developed an siRNA- mediated gene knockdown protocol and targeted the coral BI-1 gene. BI-1 knock-down specifically decreased GR expression and activity and increased oxidative DNA damage in heat- stressed preconditioned corals, showing that BI-1-mediated enhanced antioxidant response during acute heat stress is a key mechanism that prevents oxidative DNA damage after preconditioning. Teaser Preconditioning improves redox homeostasis and prevents oxidative stress in a thermally stressed reef-building coral
Auxin Plays a Role in the Adaptation of Rice to Anaerobic Germination and Seedling Establishment

Auxin is well known to stimulate coleoptile elongation and rapid seedling growth in the air. However, its role in regulating rice germination and seedling establishment under submergence is largely unknown. Previous studies have shown that excessive levels of IAA frequently cause the inhibition of plant growth and development. In the present study, the high-level accumulation of endogenous IAA is observed under submergence in the dark, stimulating rice coleoptile elongation but limiting the root and primary leaf growth during anaerobic germination (AG). We found that oxygen and light can reduce IAA levels, promote the seedling establishment and enhance rice AG tolerance. miRNA microarray profiling and RNA gel blot analysis results show that the expression of miR167 is negatively regulated by submergence; it subsequently modulates the accumulation of free IAA through the miR167-ARF-GH3 pathway. The OsGH3-8 encodes an IAA-amido synthetase that functions to prevent free IAA accumulation. Reduced miR167 levels or overexpressing OsGH3-8 increase auxin metabolism, reduce endogenous levels of free IAA and enhance rice AG tolerance. The present study reveals that poor seed germination and seedling growth inhibition resulting from excessive IAA accumulation would cause intolerance to submergence in rice, suggesting that a certain threshold level of auxin is essential for rice AG tolerance.
Senescent preosteoclast secretome promotes metabolic syndrome associated osteoarthritis through cyclooxygenase 2

Background Metabolic syndrome–associated osteoarthritis (MetS-OA) is a distinct osteoarthritis phenotype defined by the coexistence of MetS or its individual components. Despite the high prevalence of MetS-OA, its pathogenic mechanisms are unclear. The aim of this study was to determine the role of cellular senescence in the development of MetS-OA. Methods Analysis of the human osteoarthritis initiative (OAI) dataset was conducted to investigate the MRI subchondral bone features of MetS-human OA participants. Joint phenotype and senescent cells were evaluated in two MetS-OA mouse models: high-fat diet (HFD)-challenged mice and STR/Ort mice. In addition, the molecular mechanisms by which preosteoclasts become senescent as well as how the senescent preosteoclasts impair subchondral bone microenvironment were characterized using in vitro preosteoclast culture system. Results Humans and mice with MetS are more likely to develop osteoarthritis-related subchondral bone alterations than those without MetS. MetS-OA mice exhibited a rapid increase in joint subchondral bone plate and trabecular thickness before articular cartilage degeneration. Subchondral preosteoclasts undergo senescence at the pre- or early-osteoarthritis stage and acquire a unique secretome to stimulate osteoblast differentiation and inhibit osteoclast differentiation. Antagonizing preosteoclast senescence markedly mitigates pathological subchondral alterations and osteoarthritis progression in MetS-OA mice. At the molecular level, preosteoclast secretome activates COX2-PGE2, resulting in stimulated differentiation of osteoblast progenitors for subchondral bone formation. Administration of a selective COX2 inhibitor attenuated subchondral bone alteration and osteoarthritis progression in MetS-OA mice. Longitudinal analyses of the human Osteoarthritis Initiative (OAI) cohort dataset also revealed that COX2 inhibitor use, relative to non-selective nonsteroidal anti-inflammatory drug use, is associated with less progression of osteoarthritis and subchondral bone marrow lesion worsening in participants with MetS-OA. Conclusions Our findings suggest a central role of a senescent preosteoclast secretome-COX2/PGE2 axis in the pathogenesis of MetS-OA, in which selective COX2 inhibitors may have disease-modifying potential. Funding This work was supported by the National Institutes of Health grant R01AG068226 and R01AG072090 to M.W. , R01AR079620 to S.D. , and P01AG066603 to X.C.
STEREOLOGICAL EVALUATION OF TISSUE PRESERVATION AFTER NEUROPROTECTIVE TREATMENTS FOR TRAUMATIC SPINAL CORD INJURY

Spinal cord injury (SCI) is a major cause of permanent disability and its causes and pathophysiological effects are very variables between patients. The assessment of tissue damage extent and neurodegeneration degree correlated with the functional evaluation are the most accepted tools to diagnose and prognose the trauma severity. Animal models of SCI have been used for treatment development and in the present work we evaluate the potency of stereological tools to estimate damage degree for a diagnostic of neural degeneration and locomotor and sensorial disability after SCI and the efficacy of different types of therapeutic strategies.