Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave Bandpass Filters Containing High-Q Dielectric Resonators

This paper is concerned with dielectric disks used as resonators in microwave bandpass filters. For many years it has been known that modes of resonance occur in isolated dielectric bodies having air boundaries, and that very compact high-Q resonators can be achieved when /spl epsiv/ /sub r/, is high and tan /spl delta/ is low. High-purity TiO/sub 2/ ceramic material, for example, has an /spl epsiv/ /sub r/ about 100, tan /spl delta/ about 0.0001, and Q/sub u/ about 10 000. Practical applications of dielectric resonators have previously been limited by insufficient design information. Formulas are derived for the coupling coefficient between adjacent dielectric-disk resonators within a metal waveguide below cutoff. This metal enclosure is necessary for shielding and to prevent radiation loss. Comparisons between theoretical and experimental coupling coefficient values show very good agreement in each of the three bandpass configurations treated in this paper. Techniques of loop and probe coupling to the end resonators of a multiresonator bandpass filter are discussed and methods of supporting the resonators are suggested. The conclusion is made that microwave dielectric resonators offer important size reductions compared to conventional resonators of similar high Q, but that the center-frequency change of the dielectric resonators as a function of temperature is excessive for many applications. Temperature stabilization is one solution, but preferably a material should be developed having electrical characteristics similar to TiO/sub 2/ ceramic, but with at least an order of magnitude improvement in temperature sensitivity.
Learning To Segment Dominant Object Motion From Watching Videos

Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train. Unsupervised in this context only means that no annotated frames are used during inference. As obtaining ground-truth segmentation masks for real image scenes is a laborious task, we envision a simple framework for dominant moving object segmentation that neither requires annotated data to train nor relies on saliency priors or pre-trained optical flow maps. Inspired by a layered image representation [1], we introduce a technique to group pixel regions according to their affine parametric motion. This enables our network to learn segmentation of the dominant foreground object using only RGB image pairs as input for both training and inference. We establish a baseline for this novel task using a new MovingCars dataset and show competitive performance against recent methods that require annotated masks to train. 1
Controlling the Correctness of Aggregation Operations During Sessions of Interactive Analytic Queries

We present a comprehensive set of conditions and rules to control the correctness of aggregation queries within an interactive data analysis session. The goal is to extend self-service data preparation and Business Intelligence (BI) tools to automatically detect semantically incorrect aggregate queries on analytic tables and views built by using the common analytic operations including filter, project, join, aggregate, union, difference, and pivot. We introduce aggregable properties to describe for any attribute of an analytic table, which aggregation functions correctly aggregate the attribute along which sets of dimension attributes. These properties can also be used to formally identify attributes that are summarizable with respect to some aggregation function along a given set of dimension attributes. This is particularly helpful to detect incorrect aggregations of measures obtained through the use of non-distributive aggregation functions like average and count. We extend the notion of summarizability by introducing a new generalized summarizability condition to control the aggregation of attributes after any analytic operation. Finally, we define propagation rules that transform aggregable properties of the query input tables into new aggregable properties for the result tables, preserving summarizability and generalized summarizability.
Contextual Combinatorial Volatile Bandits with Satisfying via Gaussian Processes

In many real-world applications of combinatorial bandits such as content caching, rewards must be maximized while satisfying minimum service requirements. In addition, base arm availabilities vary over time, and actions need to be adapted to the situation to maximize the rewards. We pro-pose a new bandit model called Contextual Combinatorial Volatile Bandits with Group Thresholds to address these challenges. Our model subsumes combinatorial bandits by considering super arms to be subsets of groups of base arms. We seek to maximize super arm rewards while satisfying thresholds of all base arm groups that constitute a super arm. To this end, we deﬁne a new notion of regret that merges super arm reward maximization with group reward satisfaction. To facilitate learning, we assume that the mean outcomes of base arms are samples from a Gaussian Process indexed by the context set X , and the expected reward is Lipschitz continuous in expected base arm outcomes. We propose an algorithm, called Thresholded Combinatorial Gaussian Process Upper Conﬁdence Bounds (TCGP-UCB), that balances between maximizing cumulative reward and satisfying group reward thresholds and prove that it incurs ˜ O ( K (cid:112) Tγ T ) regret with high probability, where γ T is the maximum information gain associated with the set of base arm contexts that appeared in the ﬁrst T rounds and K is the maximum super arm cardinality of any feasible action over all rounds. We show in experiments that our algorithm accumulates a reward comparable with that of the state-of-the-art combinatorial ban-dit algorithm while picking actions whose groups satisfy their thresholds.
Importance of the nonlocal exchange potential for effective mass calculations in semiconductors: Benchmarking exchange-correlation potentials with the mstar60 dataset

The effective mass is an indicator of the carrier mobility, conductivity and the thermoelectric figure of merit and thus an important parameter in materials design and selection. The accuracy of effective masses predicted by density functional theory depends on the exchange-correlation functional employed, with nonlocal hybrid functionals giving more accurate results than semilocal functionals. In this article, we benchmark the performance of the Perdew–Burke–Ernzerhof, Tran-Blaha modified Becke-Johnson, and the hybrid Heyd-Scuseria-Ernzerhof (HSE) exchangecorrelation functionals and potentials to calculate effective masses with perturbation theory. We introduce the mstar60 dataset which contains 60 effective masses derived from 18 semiconductors. We reveal that the nonlocal exchange in HSE enlarges the optical transition matrix elements leading to the superior accuracy of HSE in the calculation of effective masses. The omission of nonlocal exchange in the transition operator for HSE leads to serious errors. For the semilocal PBE functional, the errors introduced by underestimation of the band gap and of the optical transition matrix elements partially cancel out for the calculation of effective masses. We then compare effective masses of transition metal dichalcogenide bulk and monolayer materials: we show that changes in the matrix elements are important in understanding the layer-dependent effective mass renormalization.
A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition

This work recasts time-dependent optimal control problems governed by partial diﬀerential equations in a Dynamic Mode Decomposition with control framework. Indeed, since the numerical solution of such problems requires a lot of computational eﬀort, we rely on this speciﬁc data-driven technique, using both solution and desired state measurements to extract the underlying system dynamics. Thus, after the Dynamic Mode Decomposition operators construction, we reconstruct and perform future predictions for all the variables of interest at a lower computational cost with respect to the standard space-time discretized models. We test the methodology in terms of relative reconstruction and prediction errors on a boundary control for a Graetz ﬂow and on a distributed control with Stokes constraints.
Exploring Features in the Binary Black Hole Population

Vamana is a mixture model framework that infers the astrophysical distribution of chirp mass, mass ratio, and spin component aligned with the orbital angular momentum for the binary black holes (BBH) population. We extend the mixing components in this framework to also model the redshift evolution of merger rate and report all the major one- and two-dimensional features in the BBH population using the 69 gravitational-wave signals detected with a false alarm rate <1 yr−1 in the third Gravitational-Wave Transient Catalog (GWTC-3). Endorsing our previous report and a recent corroborating report from LIGO Scientific, Virgo, and KAGRA Collaborations, we observe the chirp mass distribution has multiple peaks and a lack of mergers with chirp masses 10–12 M ⊙. In addition, we observe that aligned spins show mass dependence with heavier binaries exhibiting larger spins, the mass ratio shows a dependence on the chirp mass but not on the aligned spin, and the redshift evolution of the merger rate for the peaks in the mass distribution is disparate. These features possibly reflect the astrophysics associated with the BBH formation channels. However, additional observations are needed to improve our limited confidence in them.
A polynomial kernel for vertex deletion into bipartite permutation graphs

A permutation graph can be defined as an intersection graph of segments whose endpoints lie on two parallel lines $\ell_1$ and $\ell_2$, one on each. A bipartite permutation graph is a permutation graph which is bipartite. In the the bipartite permutation vertex deletion problem we ask for a given $n$-vertex graph, whether we can remove at most $k$ vertices to obtain a bipartite permutation graph. This problem is NP-complete but it does admit an FPT algorithm parameterized by $k$. In this paper we study the kernelization of this problem and show that it admits a polynomial kernel with $O(k^{62})$ vertices.
The Klein quartic maximizes the multiplicity of the first positive eigenvalue of the Laplacian

We prove that Klein quartic maximizes the multiplicity of the first positive eigenvalue of the Laplacian among all closed hyperbolic surfaces of genus $3$, with multiplicity equal to $8$. We also obtain partial results in genus $2$, where we find that the maximum multiplicity is between $3$ and $6$. Along the way, we show that for every $g\geq 2$, there exists some $\delta_g>0$ such that the multiplicity of any eigenvalue of the Laplacian on a closed hyperbolic surface of genus $g$ in the interval $[0,1/4+\delta_g]$ is at most $2g-1$ despite the fact that this interval can contain arbitrarily many eigenvalues. This extends a result of Otal to a larger interval but with a weaker bound, which nevertheless improves upon the general upper bound of S\'evennec.
Weakening the Inner Strength: Spotting Core Collusive Users in YouTube Blackmarket Network

Social reputation (e.g., likes, comments, shares, etc.) on YouTube is the primary tenet to popularize channels/videos. However, the organic way to improve social reputation is tedious, which often provokes content creators to seek services of online blackmarkets for rapidly inflating content reputation. Such blackmarkets act underneath a thriving collusive ecosystem comprising core users and compromised accounts (together known as collusive users). Core users form the backbone of blackmarkets; thus, spotting and suspending them may help in destabilizing the entire collusive network. Although a few studies focused on collusive user detection on Twitter, Facebook, and YouTube, none of them differentiate between core users and compromised accounts. We are the first to present a rigorous analysis of core users in YouTube blackmarkets. To this end, we collect a new dataset of collusive YouTube users. We study the core-periphery structure of the underlying collusive commenting network (CCN). We examine the topology of CCN to explore the behavioral dynamics of core and compromised users. We then introduce KORSE, a novel graph-based method to automatically detect core users based only on the topological structure of CCN. KORSE performs a weighted k-core decomposition using our proposed metric, called Weighted Internal Core Collusive Index (WICCI). However, KORSE is infeasible to adopt in practice as it requires complete interactions among collusive users to construct CCN. We, therefore, propose NURSE, a deep fusion framework that only leverages user timelines (without considering the underlying CCN) to detect core blackmarket users. Experimental results show that NURSE is quite close to KORSE in detecting core users and outperforms nine baselines.
Modified Shuffled Frog-Leaping Algorithm on Optimal Planning for a Stand-Alone Photovoltaic System

Shuffled frog leaping algorithm (SFLA) is a new meta-heuristic evolutionary algorithm with simple algorithm and effective calculation speed. It performs stochastic searching process that mimics natural biological evolution and the social behavior of species. SFLA conducts its formulation from two main methods, the local searching validated by particle swarm optimization and the competitiveness mixing of information implemented by shuffled complex algorithm. A modified shuffled frog leaping algorithm (MSFLA) is investigated that improves the leaping rule by properly extending the leaping step size and adding a leaping inertia component to account for social behavior. In this paper, a MSFLA is proposed to solve combinatorial optimization problem for a stand-alone photovoltaic (SPV) generation planning. Several practical installed and operated costs for a SPV system were used to trade off nonlinear system reliability in the three specified locations of Taiwan. Different degrees of loss of load hours and load profiles were investigated to achieve the long-term planning requirement.
Waveform Optimization for Wireless Power Transfer with Power Amplifier and Energy Harvester Non-linearities

Waveform optimization has recently been shown to be a key technique to boost the efficiency and range of far-field wireless power transfer (WPT). Current research has optimized transmit waveform adaptive to channel state information (CSI) and accounting for energy harvester (EH)’s non-linearity but under the assumption of linear high power amplifiers (HPA) at the transmitter. This paper proposes a channel-adaptive waveform design strategy that optimizes the transmitter’s input waveform considering both HPA and EH non-linearities. Simulations demonstrate that HPA’s non-linearity degrades the energy harvesting efficiency of WPT significantly, while the performance loss can be compensated by using the proposed optimal input waveform.
Specularity Removal for Single Image Based on Inpainting and Blending with Parameter Estimation by Neural Networks over Multiple Feature Spaces

Specularity removal is useful for image related applications that need consistent object surface appearance. For a single image it can be more challenging problem due to presence of different shapes, sizes and colors of specular regions, which may have some parts with totally missing data. The problem can become more difficult if the specular regions having partial information grow bigger, because the exact boundaries are difficult to mark. Any region filling method can provide unusual results because the appropriate boundaries selection is important for these methods. In this work, we address this problem and propose a scheme which can handle specular regions by segmenting both types of sub-regions of specularity. Our segmentation algorithm is multistage which uses Luminance as well as principal components for the identification of specular regions. For specularity removal, we proposed a three step scheme which includes balancing illumination, inpainting and blending. Finally feed-forward neural network is proposed to estimate the tunning parameters, which not only automate the whole process but also simplifies the difficult task of choosing parameters like size of specular regions or preprocessing selection. The results demonstrates the effectiveness of the proposed method for a variety of images having natural specular reflection.
Antiferromagnetism and large magnetoresistance in GdBi single crystal

Single crystal of the binary equi-atomic compound GdBi crystallizing in the rock salt type cubic crystal structure with the space group Fm3̄m has been grown by flux method. The electrical and magnetic measurements have been performed on well oriented single crystals. The antiferromagnetic ordering of the Gd moments is confirmed at TN = 27.5 K. The magnetization measurement performed at 2 K along the principal crystallographic direction [100] did not show any metamagnetic transition and no sign of saturation up to 7 T. Zero field electrical resistivity reveals a sharp drop at 27.5 K suggesting a reduction in the spin disorder scattering due to the antiferromagnetic alignment of the Gd moments. The residual resistivity at 2 K is 390 nΩcm suggesting a good quality of the grown crystal. The magneto resistance attains a value of 1.0 × 10% with no sign of saturation, in a field of 14 T, at T = 2 K. Shubnikov de Hass (SdH) oscillations have been observed in the high field range of the magnetoresistance with five different frequencies corresponding to the extremal areas of the Fermi surface. Analysis of the Hall data revealed a near compensation of the charge carriers accounting for the extremely large magnetoresistance.
A Multi-state Markov Model to Infer the Latent Deterioration Process From the Maintenance Effect on Reliability Engineering of Ships

Maintenance optimization of naval ship equipment is crucial in terms of national defense. However, the mixed effect of the maintenance and the pure deterioration processes in the observed data hinders an exact comparison between candidate maintenance policies. That is, the observed data-annual failure counts of naval ships reflect counteracting actions between the maintenance and deterioration. The inference of the latent deteriorating process is needed in advance for choosing an optimal maintenance policy to be carried out. This study proposes a new framework for the separation of the true deterioration effect by predicting it from the current maintenance effect through the multi-state Markov model. Using an annual engine failure count of 99 ships in the Korean navy, we construct the framework consisting of imputation, transition matrix design, optimization, and validation. The hierarchical Gaussian process model is used for the imputation and the three-state Markov model is applied for the estimation of parameters in the deterioration and maintenance effect. To consider the natural (deterioration) and artificial (maintenance) effect respectively, the Bayesian HMM model with a categorical distribution is employed. Computational experiments under multiple settings showed the robustness of the estimated parameters, as well as an accurate recovery of the observed data, thereby confirming the credibility of our model. The framework could further be employed to establish a reliable maintenance system and to reduce an overall maintenance cost.
Position-sensitive photodetector using interdigital electrodes on Pb2CrO5 thin films

A one-dimensional position sensitive photodetector composed of a pair of Au interdigital electrodes on a Pb2CrO5 thin film is described. The device uses the photovoltaic effect of the Au/Pb2CrO5 Schottky contact to sense an incident light beam. The device structure is simple without means of compensation for the change of irradiation intensity. A position detection error of less than ±1 % was obtained in an effective range of 3.4 mm.
HDR-NeRF: High Dynamic Range Neural Radiance Fields

We present High Dynamic Range Neural Radiance Fields (HDR-NeRF) to recover an HDR radiance field from a set of low dynamic range (LDR) views with different exposures. Using the HDR-NeRF, we are able to generate both novel HDR views and novel LDR views under different exposures. The key to our method is to model the simplified physical imaging process, which dictates that the radiance of a scene point transforms to a pixel value in the LDR image with two implicit functions: a radiance field and a tone mapper. The radiance field encodes the scene radiance (values vary from 0 to $+\infty$), which outputs the density and radiance of a ray by giving corresponding ray origin and ray direction. The tone mapper models the mapping process that a ray hitting on the camera sensor becomes a pixel value. The color of the ray is predicted by feeding the radiance and the corresponding exposure time into the tone mapper. We use the classic volume rendering technique to project the output radiance, colors and densities into HDR and LDR images, while only the input LDR images are used as the supervision. We collect a new forward-facing HDR dataset to evaluate the proposed method. Experimental results on synthetic and real-world scenes validate that our method can not only accurately control the exposures of synthesized views but also render views with a high dynamic range.
Resource Allocation for IRS-Enabled Secure Multiuser Multi-Carrier Downlink URLLC Systems

Secure ultra-reliable low-latency communication (URLLC) has been recently investigated with the fundamental limits of finite block length (FBL) regime in mind. Analysis has revealed that when eavesdroppers outnumber BS antennas or enjoy a more favorable channel condition compared to the legitimate users, base station (BS) transmit power should increase exorbitantly to meet quality of service (QoS) constraints. Channel-induced impairments such as shadowing and/or blockage pose a similar challenge. These practical considerations can drastically limit secure URLLC performance in FBL regime. Deployment of an intelligent reflecting surface (IRS) can endow such systems with much-needed resiliency and robustness to satisfy stringent latency, availability, and reliability requirements. We address this problem and propose to minimize the total BS transmit power by simultaneously designing the beamformers and artificial noise at the BS and phase-shifts at the IRS, while guaranteeing the required number of securely transmitted bits with the desired packet error probability, information leakage, and maximum affordable delay. The proposed optimization problem is non-convex and we apply block coordinate descent and successive convex approximation to iteratively solve a series of convex sub-problems instead. The proposed algorithm converges to a sub-optimal solution in a few iterations and attains substantial power saving and robustness compared to baseline schemes.