Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of High Penetration of Inverter-based Generation on Electromechanical Wave Propagation in Power Grids

Abstract— A power system electromechanical wave propagates from the disturbance location to the rest of system, influencing various types of protections. In addition, since more power-electronics-interfaced generation and energy storage devices are being integrated into power systems, electromechanical wave propagation speeds in the future power systems are likely to change accordingly. In this paper, GPSsynchronized measurement data from a wide-area synchrophasor measurement system FNET/GridEye are used to analyze the characteristics of electromechanical wave propagation in the U.S. Eastern Interconnection (EI) system. Afterwards, high levels of photovoltaic (PV) penetration are modeled in the EI to investigate the influences of a typical power-electronics--interfaced resource on the electromechanical wave propagation speed. The result shows a direct correlation between the local penetration level of inverter-based generation and the electromechanical wave propagation speed.
Thermodynamics with pressure and volume of black holes based on two assumptions under scalar field scattering

Recently, a new assumption was proposed in [Phys. Rev. D 100, no.10, 104022 (2019)]. This assumption considers that the energy of the particle changes the enthalpy of the black hole after throwing the particle into the black hole. Using the energy-momentum relation, the results show that the second law of thermodynamics of the black hole is valid in extended phase space. In this paper, we discuss the validity of the laws of thermodynamics and the stability of the horizon of the charged AdS black hole by scalar field scattering under two assumptions, i.e., the energy flux of the scalar field $dE$ changes the internal energy of the black hole $dU$ and the energy flux of the scalar field $dE$ changes the enthalpy of the black hole $dM$.
Current and Emergent Economic Impacts of Covid-19 and Brexit on UK Fresh Produce and Horticultural Businesses

This paper describes a study designed to investigate the current and emergent impacts of Covid-19 and Brexit on UK horticultural businesses. Various characteristics of UK horticultural production, notably labour reliance and import dependence, make it an important sector for policymakers concerned to understand the effects of these disruptive events as we move from 2020 into 2021. The study design prioritised timeliness, using a rapid survey to gather information from a relatively small (n = 19) but indicative group of producers. The main novelty of the results is to suggest that a very substantial majority of producers either plan to scale back production in 2021 (47%) or have been unable to make plans for 2021 because of uncertainty (37%). The results also add to broader evidence that the sector has experienced profound labour supply challenges, with implications for labour cost and quality. The study discusses the implications of these insights from producers in terms of productivity and automation, as well as in terms of broader economic implications. Although automation is generally recognised as the long-term future for the industry (89%), it appeared in the study as the second most referred short-term option (32%) only after changes to labour schemes and policies (58%). Currently, automation plays a limited role in contributing to the UK's horticultural workforce shortage due to economic and socio-political uncertainties. The conclusion highlights policy recommendations and future investigative intentions, as well as suggesting methodological and other discussion points for the research community.
Medical Segment Coloring of Clinical Notes

This paper proposes a deep learning-based method to identify the segments of a clinical note corresponding to ICD-9 broad categories which are further color-coded with respect to 17 ICD-9 categories. The proposed Medical Segment Colorer (MSC) architecture is a pipeline framework that works in three stages: (1) word categorization, (2) phrase allocation, and (3) document classification. MSC uses gated recurrent unit neural networks (GRUs) to map from an input document to word multi-labels to phrase allocations, and uses statistical median to map phrase allocation to document multi-label. We compute variable length segment coloring from overlapping phrase allocation probabilities. These cross-level bidirectional contextual links identify adaptive context and then produce segment coloring. We train and evaluate MSC using the document labeled MIMIC-III clinical notes. Training is conducted solely using document multi-labels without any information on phrases, segments, or words. In addition to coloring a clinical note, MSC generates as byproducts document multi-labeling and word tagging -- creation of ICD9 category keyword lists based on segment coloring. Performance comparison of MSC byproduct document multi-labels versus methods whose purpose is to produce justifiable document multi-labels is 64% vs 52.4% micro-average F1-score against the CAML (CNN attention multi label) method. For evaluation of MSC segment coloring results, medical practitioners independently assigned the colors to broad ICD9 categories given a sample of 40 colored notes and a sample of 50 words related to each category based on the word tags. Binary scoring of this evaluation has a median value of 83.3% and mean of 63.7%.
Formation Processes of Zinc Excimer Thin Films Due to Ion-Recombination Processes

In materials science, the number of d-electrons of transition metals is an essentially important factor controlling characteristics of alloys and compounds. In this paper, we show an example to control the number of d-electrons (holes) by using inner-core electron excitation of zinc atoms. An important feature of our research is that we can make a long lifetime excited electronic state of zinc (3d8), and the life-time of excited zinc is more than 307 days. At first, the experimental apparatus and boundary conditions of the ion-recombination processes were explained. From results of XPS, excited zinc films showed satellites peaks what caused by the final state of 3d8 and the charge transfer final state of 3d10L2. Excited states of zinc were formatted at the surface of substrate caused by ion-recombination process between Zn+ and Zn-. The excited zinc diffused from substrate surface to the surface of the excited zinc thin film. Intensity of excited zinc is proportional to the intensity of electron on the substrate.
Distributed Learning over Markovian Fading Channels for Stable Spectrum Access

We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user chooses a single channel for transmission at each time slot. The state of each channel is modeled by a restless unknown Markovian process. Previous studies have analyzed a special case of this setting, in which each channel yields the same expected rate for all users. By contrast, we consider a more general and practical model, where each channel yields a different expected rate for each user. This model adds a significant challenge of how to efficiently learn a channel allocation in a distributed manner to yield a global system-wide objective. We adopt the stable matching rate as the system objective, which is known to yield strong performance in multichannel wireless networks, and develop a novel Distributed Stable Strategy Learning (DSSL) algorithm to achieve the objective. We prove theoretically that DSSL converges to the stable matching allocation, and the regret, defined as the loss in total rate with respect to the stable matching solution, has a logarithmic order with time. Simulation results demonstrate the strong performance of the DSSL algorithm. We show that DSSL has significant advantages over the state-of-the-art methods in terms of implementation complexity, convergence speed, and network churn conditions.
Efficient Video Summarization Framework using EEG and Eye-tracking Signals

This paper proposes an efficient video summarization framework that will give a gist of the entire video in a few key-frames or video skims. Existing video summarization frameworks are based on algorithms that utilize computer vision low-level feature extraction or high-level domain level extraction. However, being the ultimate user of the summarized video, humans remain the most neglected aspect. Therefore, the proposed paper considers human's role in summarization and introduces human visual attention-based summarization techniques. To understand human attention behavior, we have designed and performed experiments with human participants using electroencephalogram (EEG) and eye-tracking technology. The EEG and eye-tracking data obtained from the experimentation are processed simultaneously and used to segment frames containing useful information from a considerable video volume. Thus, the frame segmentation primarily relies on the cognitive judgments of human beings. Using our approach, a video is summarized by 96.5% while maintaining higher precision and high recall factors. The comparison with the state-of-the-art techniques demonstrates that the proposed approach yields ceiling-level performance with reduced computational cost in summarising the videos.
Anti-Aliasing Add-On For Deep Prior Seismic Data Interpolation

Data interpolation is a fundamental step in any seismic processing workflow. Among machine learning techniques recently proposed to solve data interpolation as an inverse problem, Deep Prior paradigm aims at employing a convolutional neural network to capture priors on the data in order to regularize the inversion. However, this technique lacks of reconstruction precision when interpolating highly decimated data due to the presence of aliasing. In this work, we propose to improve Deep Prior inversion by adding a directional Laplacian as regularization term to the problem. This regularizer drives the optimization towards solutions that honor the slopes estimated from the interpolated data low frequencies. We provide some numerical examples to showcase the methodology devised in this manuscript, showing that our results are less prone to aliasing also in presence of noisy and corrupted data.
A Strengthened Cutset Upper Bound on the Capacity of the Relay Channel and Applications

We develop a new upper bound on the capacity of the relay channel that is tighter than previously known upper bounds. This upper bound is proved using traditional weak converse techniques involving mutual information inequalities and Gallager-type explicit identification of auxiliary random variables. We show that the new upper bound is strictly tighter than all previous bounds for the Gaussian relay channel with non-zero channel gains. When specialized to the relay channel with orthogonal receiver components, the bound resolves a conjecture by Kim on a class of deterministic relay channels. When further specialized to the class of product-form relay channels with orthogonal receiver components, the bound resolves a generalized version of Cover’s relay channel problem, recovers the recent upper bound for the Gaussian case by Wu et al., and improves upon the recent bounds for the binary symmetric case by Wu et al. and Barnes et al., which were obtained using non-traditional geometric proof techniques. For the special class of a relay channel with orthogonal receiver components, we develop another upper bound on the capacity which utilizes an auxiliary receiver and show that it is strictly tighter than the bound by Tandon and Ulukus. Finally, we show through the Gaussian relay channel with i.i. d. relay output sequence that the bound with the auxiliary receiver can be strictly tighter than our main bound.
Iterated and mixed discriminants

We consider systems of Laurent polynomials with support on a fixed point configuration. In the non-defective case, the closure of the locus of coefficients giving a non-degenerate multiple root of the system is defined by a polynomial called the mixed discriminant. We define a related polynomial called the multivariate iterated discriminant, generalizing the classical Sch\"afli method for hyperdeterminants. This iterated discriminant is easier to compute and we prove that it is always divisible by the mixed discriminant. We show that tangent intersections can be computed via iteration if and only if the singular locus of a corresponding dual variety has sufficiently high codimension. We also study when point configurations corresponding to Segre-Veronese varieties and to the lattice points of planar smooth polygons, have their iterated discriminant equal to their mixed discriminant.
The $(l,r)$-Stirling numbers: a combinatorial approach

This work deals with a new generalization of r-Stirling numbers using l-tuple of permutations and partitions called (l, r)-Stirling numbers of both kinds. We study various properties of these numbers using combinatorial interpretations and symmetric functions. Also, we give a limit representation of the multiple zeta function using (l, r)Stirling of the first kind.
Piecewise nonlinearity and capacitance in the joint density functional theory of extended interfaces

The ab initio simulation of charged interfaces in the framework of density functional theory (DFT) is heavily employed for the study of electrochemical energy conversion processes. The capacitance is the primary descriptor for the response of the electrochemical interface. It is essentially equal to the inverse of the energy curvature as a function of electron number, and as such there appears a conﬂict with the fundamental principle of piecewise linearity in DFT that requires the energy curvature to be zero at fractional electron numbers, i.e. almost everywhere. To resolve this conﬂict, we derive an exact expression between the energy curvature and the Kohn-Sham density of states, the local density of states, and the Fukui potential. We ﬁnd that the piecewise linearity requirement does not hold for the volume- or area-speciﬁc energy of extended systems and surfaces. Applied to the joint density functional theory of an electrode–electrolyte interface, including the ionic and dielectric response of the electrolyte, the same expression represents a rigorous basis for the partitioning of the total interfacial capacitance into contributions of the quantum capacitance, space-charge capacitance, and electrochemical double-layer capacitance. It provides insight into the inﬂuence of the electrode material, thickness, and temperature on the charging characteristics, as demonstrated by results for a bulk gold electrode, a single-layer gold electrode, and a single-layer graphene electrode.
Towards low loss non-volatile phase change materials in mid index waveguides

Photonic integrated circuits currently use platform intrinsic thermo-optic and electro-optic effects to implement dynamic functions such as switching, modulation and other processing. Currently, there is a drive to implement field programmable photonic circuits, a need which is only magnified by new neuromorphic and quantum computing applications. The most promising non-volatile photonic components employ phase change materials such as GST and GSST, which had their origin in electronic memory. However, in the optical domain, these compounds introduce significant losses potentially preventing a large number of applications. Here, we evaluate the use of two newly introduced low loss phase change materials, Sb2S3 and Sb2Se3, on a silicon nitride photonic platform for future implementation in neuromorphic computing. We focus the study on Mach–Zehnder interferometers that operate at the O and C bands to demonstrate the performance of the system. Our measurements show an insertion loss below 0.04 dB μm−1 for Sb2S3 and lower than 0.09 dB μm−1 for Sb2Se3 cladded devices for both amorphous and crystalline phases. The effective refractive index contrast for Sb2S3 on SiNx was measured to be 0.05 at 1310 nm and 0.02 at 1550 nm, whereas for Sb2Se3, it was 0.03 at 1310 nm and 0.05 at 1550 nm highlighting the performance of the integrated device.
Electric-field-tunable intervalley excitons and phonon replicas in bilayer WSe$_2$

We report the direct observation of intervalley exciton between the Q conduction valley and Γ valence valley in bilayer WSe2 by photoluminescence. Τhe QΓ exciton lies at ~18 meV below the QK exciton and dominates the luminescence of bilayer WSe2. By measuring the exciton spectra at gate-tunable electric field, we reveal different interlayer electric dipole moments and Stark shifts between QΓ and QK excitons. Notably, we can use the electric field to switch the energy order and dominant luminescence between QΓ and QK excitons. Both QΓ and QK excitons exhibit pronounced phonon replicas, in which two-phonon replicas outshine the one-phonon replicas due to the existence of (nearly) resonant exciton-phonon scatterings and numerous twophonon scattering paths. We can simulate the replica spectra by comprehensive theoretical modeling and calculations. The good agreement between theory and experiment for the Stark shifts and phonon replicas strongly supports our assignment of QΓ and QK excitons.
Safe Multi-Agent Reinforcement Learning via Shielding

Multi-agent reinforcement learning (MARL) has been increasingly used in a wide range of safety-critical applications, which require guaranteed safety (e.g., no unsafe states are ever visited) during the learning process.Unfortunately, current MARL methods do not have safety guarantees. Therefore, we present two shielding approaches for safe MARL. In centralized shielding, we synthesize a single shield to monitor all agents' joint actions and correct any unsafe action if necessary. In factored shielding, we synthesize multiple shields based on a factorization of the joint state space observed by all agents; the set of shields monitors agents concurrently and each shield is only responsible for a subset of agents at each step.Experimental results show that both approaches can guarantee the safety of agents during learning without compromising the quality of learned policies; moreover, factored shielding is more scalable in the number of agents than centralized shielding.
Optimal Oracles for Point-to-Set Principles

The point-to-set principle \cite{LutLut17} characterizes the Hausdorff dimension of a subset $E\subseteq\R^n$ by the \textit{effective} (or algorithmic) dimension of its individual points. This characterization has been used to prove several results in classical, i.e., without any computability requirements, analysis. Recent work has shown that algorithmic techniques can be fruitfully applied to Marstrand's projection theorem, a fundamental result in fractal geometry. In this paper, we introduce an extension of point-to-set principle - the notion of \textit{optimal oracles} for subsets $E\subseteq\R^n$. One of the primary motivations of this definition is that, if $E$ has optimal oracles, then the conclusion of Marstrand's projection theorem holds for $E$. We show that every analytic set has optimal oracles. We also prove that if the Hausdorff and packing dimensions of $E$ agree, then $E$ has optimal oracles. Moreover, we show that the existence of sufficiently nice outer measures on $E$ implies the existence of optimal Hausdorff oracles. In particular, the existence of exact gauge functions for a set $E$ is sufficient for the existence of optimal Hausdorff oracles, and is therefore sufficient for Marstrand's theorem. Thus, the existence of optimal oracles extends the currently known sufficient conditions for Marstrand's theorem to hold. Under certain assumptions, every set has optimal oracles. However, assuming the axiom of choice and the continuum hypothesis, we construct sets which do not have optimal oracles. This construction naturally leads to a generalization of Davies theorem on projections.
Multi-Hypothesis Pose Networks: Rethinking Top-Down Pose Estimation

A key assumption of top-down human pose estimation approaches is their expectation of having a single person present in the input bounding box. This often leads to failures in crowded scenes with occlusions. We propose a novel solution to overcome the limitations of this fundamental assumption. Our Multi-Hypothesis Pose Network (MHPNet) allows for predicting multiple 2D poses within a given bounding box. We introduce a Multi-Hypothesis Attention Block (MHAB) that can adaptively modulate channel-wise feature responses for each hypothesis and is parameter efﬁcient. We demonstrate the efﬁcacy of our approach by evaluating on COCO, CrowdPose and OCHuman datasets. Speciﬁcally, we achieve 70 . 0 AP on CrowdPose and 42 . 5 AP on OCHuman test sets, a signiﬁcant improvement of 2 . 4 AP and 6 . 5 AP over prior art, respectively. When using ground truth bounding boxes for inference, MHPNet achieves an improvement of 0 . 7 AP on COCO, 0 . 9 AP on CrowdPose, and 9 . 1 AP on OCHuman validation sets compared to HRNet. Interest-ingly, when fewer, high conﬁdence bounding boxes are used, HRNet’s performance degrades (by 5 AP) on OCHuman, whereas MHPNet maintains a relatively stable performance (drop of 1 AP) for the same inputs.
Revisiting Contrastive Learning for Few-Shot Classification

Instance discrimination based contrastive learning has emerged as a leading approach for self-supervised learning of visual representations. Yet, its generalization to novel tasks remains elusive when compared to representations learned with supervision, especially in the few-shot setting. We demonstrate how one can incorporate supervision in the instance discrimination based contrastive self-supervised learning framework to learn representations that generalize better to novel tasks. We call our approach CIDS ( C ontrastive I nstance D iscrimination with S upervision). CIDS performs favorably compared to existing algorithms on popular few-shot benchmarks like Mini-ImageNet or Tiered-ImageNet. We also propose a novel model selection algo-rithm that can be used in conjunction with a universal embedding trained using CIDS to outperform state-of-the-art algorithms on the challenging Meta-Dataset benchmark.
Dielectric slot-coupled half-Maxwell fisheye lens as octave-bandwidth beam expander for terahertz-range applications

We present a paradigm for integrated photonic devices based upon broadband slab-conﬁned collimated beams that are launched with half-Maxwell ﬁsheye lenses. Although it is challenging to match to the low-index focus of the lens whilst maintaining adequate ﬁeld conﬁnement for a well-deﬁned point source, integrated dielectric slot waveguides prove highly suitable, yielding collimators of 90% eﬃciency and over one octave bandwidth. Terahertz technology will beneﬁt from such broadband slab-conﬁned beams to replace free-space optics, toward compact, mass-producible systems that do not require manual optical alignment. We present two prototype systems to demonstrate the ver-satility of this concept, namely a diagonally-set distributed Bragg reﬂector as frequency-division diplexer for terahertz communications, and an attenuated total reﬂection-based liquid sensor. Both are enabled by oblique in-slab reﬂections that are collected at a location other than the originating lens, which is not attainable using ordinary single-mode channel waveguides.