Miracle-dz/UniFAR-FTUs huggingface dataset:
Dielectric spectroscopy of ferroelectric nematic liquid crystals: Measuring the capacitance of insulating interfacial layers

Numerous measurements of the dielectric constant $\epsilon$ of the recently discovered ferroelectric nematic ($N_F$) liquid crystal (LC) phase report extraordinarily large values of $\epsilon^\prime$ (up to ~30,000). We show that what is in fact being measured in such experiments is the high capacitance of the non-ferroelectric, interfacial, insulating layers of nanoscale thickness that bound the $N_F$ material in typical cells. We analyze a parallel-plate cell filled with $N_F$ material of high-polarization $\mathbf{P}$, oriented parallel to the plates at zero applied voltage. Minimization of the dominant electrostatic energy renders $\mathbf{P}$ spatially uniform and orients it to make the electric field in the $N_F$ as small as possible, a condition under which the voltage applied to the cell appears almost entirely across the high-capacity interfacial layers. This coupling of orientation and charge creates a combined polarization-external capacitance (PCG) Goldstone reorientation mode requiring applied voltages orders of magnitude smaller than that of the $N_F$ layer alone to effectively transport charge across the $N_F$ layer. The $N_F$ layer acts as a low-value resistor and the interfacial capacitors as reversible energy storage reservoirs, lowering the restoring force (mass) of the PCG mode and producing strong reactive dielectric behavior. Analysis of data from several experiments on ferroelectric liquid crystals (chiral smectics C, bent-core smectics, and the $N_F$ phase supports the PCG model, showing that deriving dielectric constants from electrical impedance measurements of high-polarization ferroelectric LCs, without properly accounting for the self-screening effects of polarization charge and the capacitive contributions of interfacial layers, can result in overestimation of the $\epsilon^\prime$ values of the LC by many orders of magnitude.
Provable Adaptivity in Adam

Adaptive Moment Estimation (Adam) optimizer is widely used in deep learning tasks because of its fast convergence properties. However, the convergence of Adam is still not well understood. In particular, the existing analysis of Adam cannot clearly demonstrate the advantage of Adam over SGD. We attribute this theoretical embarrassment to $L$-smooth condition (i.e., assuming the gradient is globally Lipschitz continuous with constant $L$) adopted by literature, which has been pointed out to often fail in practical neural networks. To tackle this embarrassment, we analyze the convergence of Adam under a relaxed condition called $(L_0,L_1)$ smoothness condition, which allows the gradient Lipschitz constant to change with the local gradient norm. $(L_0,L_1)$ is strictly weaker than $L$-smooth condition and it has been empirically verified to hold for practical deep neural networks. Under the $(L_0,L_1)$ smoothness condition, we establish the convergence for Adam with practical hyperparameters. Specifically, we argue that Adam can adapt to the local smoothness condition, justifying the \emph{adaptivity} of Adam. In contrast, SGD can be arbitrarily slow under this condition. Our result might shed light on the benefit of adaptive gradient methods over non-adaptive ones.
Quantum Antireflection Temporal Coatings: Quantum State Frequency Shifting and Inhibited Thermal Noise Amplification

The quantum optical response of antireflection temporal coatings (ATCs), that is, matching temporal layers that suppress the generation of backward waves in temporal boundaries, is investigated. The results reveal that quantum ATCs are characterized for inducing a frequency shift of the quantum state, while preserving all photon statistics intact. Thus, they can find application for fast quantum frequency shifting in photonic quantum networks. The quantum theory also provides additional insight on their classical mode of operation, clarifying which quantities are preserved through the temporal boundary. Last, it is shown that quantum ATCs allow for fast temporal switching without amplification of thermal fields.
Influence of agent’s self-disclosure on human empathy

As AI technologies progress, social acceptance of AI agents, including intelligent virtual agents and robots, is becoming even more important for more applications of AI in human society. One way to improve the relationship between humans and anthropomorphic agents is to have humans empathize with the agents. By empathizing, humans act positively and kindly toward agents, which makes it easier for them to accept the agents. In this study, we focus on self-disclosure from agents to humans in order to increase empathy felt by humans toward anthropomorphic agents. We experimentally investigate the possibility that self-disclosure from an agent facilitates human empathy. We formulate hypotheses and experimentally analyze and discuss the conditions in which humans have more empathy toward agents. Experiments were conducted with a three-way mixed plan, and the factors were the agents’ appearance (human, robot), self-disclosure (high-relevance self-disclosure, low-relevance self-disclosure, no self-disclosure), and empathy before/after a video stimulus. An analysis of variance (ANOVA) was performed using data from 918 participants. We found that the appearance factor did not have a main effect, and self-disclosure that was highly relevant to the scenario used facilitated more human empathy with a statistically significant difference. We also found that no self-disclosure suppressed empathy. These results support our hypotheses. This study reveals that self-disclosure represents an important characteristic of anthropomorphic agents which helps humans to accept them.
Observation of a new charmonium state in double charmonium production in e+ e- annihilation at s**(1/2) ~ 10.6-GeV

We report the first observation of a new charmonium state at a mass of $(3.943 \pm 0.006 \pm 0.006) {GeV}/c^2$. This state, called X(3940), is observed in the process $e^+ e^- \to J/\psi X(3940)$ in the spectrum of masses recoiling against a reconstructed $J/\psi$. We also observe the decay of the X(3940) into $D^* \bar{D}$ and measure the intrinsic width of this state to be less than $52 MeV/c^2$ at the 90% C.L. These results are obtained from a $350 fb^{-1}$ data sample collected with the Belle detector near the $\Upsilon(4S)$ resonance, at the KEKB asymmetric energy $e^+ e^-$ collider.
One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction

We propose a universal Graph Neural Network architecture which can be trained as an end-2-end search heuristic for any Constraint Satisfaction Problem (CSP). Our architecture can be trained unsupervised with policy gradient descent to generate problem specific heuristics for any CSP in a purely data driven manner. The approach is based on a novel graph representation for CSPs that is both generic and compact and enables us to process every possible CSP instance with one GNN, regardless of constraint arity, relations or domain size. Unlike previous RL-based methods, we operate on a global search action space and allow our GNN to modify any number of variables in every step of the stochastic search. This enables our method to properly leverage the inherent parallelism of GNNs. We perform a thorough empirical evaluation where we learn heuristics for well known and important CSPs, both decision and optimisation problems, from random data, including graph coloring, MAXCUT, and MAX-k-SAT, and the general RB model. Our approach significantly outperforms prior end-2-end approaches for neural combinatorial optimization. It can compete with conventional heuristics and solvers on test instances that are several orders of magnitude larger and structurally more complex than those seen during training.
Magnetically tuned continuous transition from weak to strong coupling in terahertz magnon polaritons

Depending on the relative rates of coupling and dissipation, a light-matter coupled system is either in the weak- or strong-coupling regime. Here, we present a unique system where the coupling rate continuously increases with an externally applied magnetic field while the dissipation rate remains constant, allowing us to monitor a weak-to-strong coupling transition as a function of magnetic field. We observed a Rabi splitting of a terahertz magnon mode in yttrium orthoferrite above a threshold magnetic field of ~14 T. Based on a microscopic theoretical model, we show that with increasing magnetic field the magnons transition into magnon polaritons through an exceptional point, which will open up new opportunities for in situ control of non-Hermitian systems.
Security Enhancement for Coupled Phase-Shift STAR-RIS Networks

The secure transmission of the simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided communication system is investigated. Considering the coupled phase shifts of STAR-RISs and the fair secrecy requirement of users, a new secure beamforming design is proposed for addressing the unique full-space mutual eavesdropping in STAR-RIS aided communications. In particular, a penalty-based secrecy beamforming algorithm is proposed to solve the resulting non-convex optimization problem, where closed-form solutions of the coupled phase-shift coefficients are obtained in each iteration. Numerical results demonstrate that 1) the proposed scheme achieves higher secrecy capacity than conventional RIS; 2) 4-bit discrete phase shifters are sufficient for secrecy guarantee.
Gender-grade-gap zeroed out under a specific intro-physics assessment regime

Unhappiness with issues involved in grading, coupled with a desire to offer classes where everyone succeeded, led us to offer several classes of a large-enrollment introductory physics course that took a small step toward mastery learning. These attempts to improve instruction in these courses involved three different instructors. The classes were offered about 5 years ago but I (David Webb) retired almost immediately after those course offerings and my colleague Wendell Potter died less than a year later so I have only recently returned to these data. This report is written from my perspective. In the last decade or so, when I have done test runs of significant systemic changes in introductory physics courses I have checked whether the particular changes we made discriminate, in a way that is measurable by grades, against any large demographic groups in the course. The concern is mainly that people who already face discrimination are not further hurt by our changes in these courses. The American Physical Society (APS) has identified several groups of people who are represented in the field of physics in the United States at smaller percentages than they are in the larger US population. The APS groups these people in terms of their gender (female physicists1) and/or their racial/ethnic ancestry (physicists with african, native american, hispanic, or pacific island heritage). There are usually a large enough group of female students to allow statistical studies. However, we must usually gather students with these racial/ethnic ancestries together into a single group to give us the statistical power to
Spectral Decomposition Representation for Reinforcement Learning

Representation learning often plays a critical role in reinforcement learning by managing the curse of dimensionality. A representative class of algorithms exploits a spectral decomposition of the stochastic transition dynamics to construct representations that enjoy strong theoretical properties in an idealized setting. However, current spectral methods suffer from limited applicability because they are constructed for state-only aggregation and derived from a policy-dependent transition kernel, without considering the issue of exploration. To address these issues, we propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy, while also balancing the exploration-versus-exploitation trade-off during learning. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. In addition, an experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several benchmarks.
Discrete dynamics in cluster integrable systems from geometric \(R\)-matrix transformations

Cluster integrable systems are a broad class of integrable systems modelled on bipartite dimer models on the torus. Many discrete integrable dynamics arise by applying sequences of local transformations, which form the cluster modular group of the cluster integrable system. This cluster modular group was recently characterized by the first author and Inchiostro. There exist some discrete integrable dynamics that make use of non-local transformations associated with geometric $R$-matrices. In this article we characterize the generalized cluster modular group -- which includes both local and non-local transformations -- in terms of extended affine symmetric groups. We also describe the action of the generalized cluster modular group on the spectral data associated with cluster integrable systems.
DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection

The prosperity of deep learning contributes to the rapid progress in scene text detection. Among all the methods with convolutional networks, segmentation-based ones have drawn extensive attention due to their superiority in detecting text instances of arbitrary shapes and extreme aspect ratios. However, the bottom-up methods are limited to the performance of their segmentation models. In this paper, we propose DPTNet (Dual-Path Transformer Network), a simple yet effective architecture to model the global and local information for the scene text detection task. We further propose a parallel design that integrates the convolutional network with a powerful self-attention mechanism to provide complementary clues between the attention path and convolutional path. Moreover, a bi-directional interaction module across the two paths is developed to provide complementary clues in the channel and spatial dimensions. We also upgrade the concentration operation by adding an extra multi-head attention layer to it. Our DPTNet achieves state-of-the-art results on the MSRA-TD500 dataset, and provides competitive results on other standard benchmarks in terms of both detection accuracy and speed.
Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies

We introduce a new type of test, called a Turing Experiment (TE), for evaluating to what extent a given language model, such as GPT models, can simulate different aspects of human behavior. A TE can also reveal consistent distortions in a language model's simulation of a specific human behavior. Unlike the Turing Test, which involves simulating a single arbitrary individual, a TE requires simulating a representative sample of participants in human subject research. We carry out TEs that attempt to replicate well-established findings from prior studies. We design a methodology for simulating TEs and illustrate its use to compare how well different language models are able to reproduce classic economic, psycholinguistic, and social psychology experiments: Ultimatum Game, Garden Path Sentences, Milgram Shock Experiment, and Wisdom of Crowds. In the first three TEs, the existing findings were replicated using recent models, while the last TE reveals a"hyper-accuracy distortion"present in some language models (including ChatGPT and GPT-4), which could affect downstream applications in education and the arts.
A Web Application for Experimenting and Validating Remote Measurement of Vital Signs

With a surge in online medical advising remote monitoring of patient vitals is required. This can be facilitated with the Remote Photoplethysmography (rPPG) techniques that compute vital signs from facial videos. It involves processing video frames to obtain skin pixels, extracting the cardiac data from it and applying signal processing ﬁlters to extract the Blood Volume Pulse (BVP) signal. Diﬀerent algorithms are applied to the BVP signal to estimate the various vital signs. We implemented a web application framework to measure a person’s Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO 2 ), Respiration Rate (RR), Blood Pressure (BP), and stress from the face video. The rPPG technique is highly sensitive to illumination and motion variation. The web application guides the users to reduce the noise due to these variations and thereby yield a cleaner BVP signal. The accuracy and robustness of the framework was validated with the help of volunteers.
A Survey of Distributed Ledger Technology for IoT Verticals

The Internet of Things (IoT) and Distributed ledger technology (DLT) have significantly changed our daily lives. Due to their distributed operational environment and naturally decentralized applications, the convergence of these two technologies indicates a more lavish arrangement for the future. This article develops a comprehensive survey to investigate and illustrate state-of-the-art DLT for various IoT use cases, from smart homes to autonomous vehicles and smart cities. We develop a novel framework for conducting a systematic and comprehensive review of DLT over IoT by extending the knowledge graph approach. With relevant insights from this review, we extract innovative and pragmatic techniques to DLT design that enable high-performance, sustainable, and highly scalable IoT systems. Our findings support designing an end-to-end IoT-native DLT architecture for the future that fully coordinates network-assisted functionalities.
High Mass Flow Rate in a BAL Outflow of Quasar SDSS J1130+0411

We present the analysis of the absorption troughs of six outflows observed in quasar SDSS J1130+0411 (z ≈ 3.98) with radial velocities ranging from −2400 to −15, 400 km s−1. These spectra were taken with the Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph over the rest frame wavelength range of 1135 − 1890 Å. In the main outflow system (v ≈ −3200 km s−1), we identify Fe ii and several Fe ii* absorption troughs as well as Si ii and Si ii* troughs, which we use to determine the electron number density $\log n_e = 2. 6_{-0.7}^{+0.8}$ cm−3. Using the column densities of these and other ions, we determine a photoionization solution with hydrogen column density $\log N_H = 21. 44_{-0.33}^{+0.24}$ cm−2 and ionization parameter $\log U_H = -1. 75_{-0.45}^{+0.28}$. From these values we derive the distance $R = 16_{-11}^{+23}$ kpc, the average mass flow rate $\dot{M} = 4100_{-2400}^{+6600}$M⊙ yr−1, and the kinetic luminosity $\log \dot{E}_k = 46. 13_{-0.37}^{+0.41}$ erg s−1. This $\dot{E}_k$ is $1. 4_{-0.8}^{+2.2}$% of the quasar’s Eddington luminosity, and therefore contributes significantly to AGN feedback.
Diamagnetic Composites for High‐Q Levitating Resonators

Levitation offers extreme isolation of mechanical systems from their environment, while enabling unconstrained high‐precision translation and rotation of objects. Diamagnetic levitation is one of the most attractive levitation schemes because it allows stable levitation at room temperature without the need for a continuous power supply. However, dissipation by eddy currents in conventional diamagnetic materials significantly limits the application potential of diamagnetically levitating systems. Here, a route toward high‐Q macroscopic levitating resonators by substantially reducing eddy current damping using graphite particle based diamagnetic composites is presented. Resonators that feature quality factors Q above 450 000 and vibration lifetimes beyond one hour are demonstrated, while levitating above permanent magnets in high vacuum at room temperature. The composite resonators have a Q that is >400 times higher than that of diamagnetic graphite plates. By tuning the composite particle size and density, the dissipation reduction mechanism is investigated, and the Q of the levitating resonators is enhanced. Since their estimated acceleration noise is as low as some of the best superconducting levitating accelerometers at cryogenic temperatures, the high Q and large mass of the presented composite resonators positions them as one of the most promising technologies for next generation ultra‐sensitive room temperature accelerometers.
Mass estimation, dynamics and feedback in galaxy groups

Accurate mass estimates for galaxy groups and clusters are important for understanding the evolution of matter within the universe. In this thesis we first discuss methods of mass estimation currently used within the literature, and introduce a set of scaling relations for mass estimation in cases where literature methods are not applicable. We find that methods based on group richness provide the best (i.e. lowest scatter) mass estimator. Secondly, we study the impact of feedback on hot group gas for a sample of optically selected groups. We refine the group selection using their dynamical state, identified through substructure in the distribution of member galaxies. We find this sample to be underluminous compared to an X-ray selected sample. Furthermore, with two groups showing high 2σ lower limits on entropy, the population of high entropy groups predicted by hydrodynamical simulations may have been detected. Finally, we combine measures of dynamical state and mass estimation scaling relations to understand how the presence of substructure can impact upon the ability to reliably estimate group and cluster masses. We find that substructure introduced through poor group identification has the largest effect on the quality of the final mass estimates.
Mod $p$ homology of unordered configuration spaces of surfaces

We provide a short proof that the dimensions of the mod $p$ homology groups of the unordered configuration space $B_k(T)$ of $k$ points in a torus are the same as its Betti numbers for $p>2$ and $k\leq p$. Hence the integral homology has no $p$-power torsion. The same argument works for the punctured genus $g$ surface with $g>0$, thereby recovering a result of Brantner-Hahn-Knudsen via Lubin-Tate theory.
The Physical Properties of Massive Green Valley Galaxies as a Function of Environments at 0.5 < z < 2.5 in 3D-HST/Candels Fields

To investigate the effects of environment in the quenching phase, we study the empirical relations for green valley (GV) galaxies between overdensity and other physical properties (i.e., effective radius r e , Sérsic indices n, and specific star formation rate (sSFR)). Based on five 3D-HST/CANDELS fields, we construct a large sample of 2126 massive (M ⋆ > 1010 M ☉) GV galaxies at 0.5 < z < 2.5 and split it into the higher overdensity quarter and the lower overdensity quarter. The results shows that GV galaxies in denser environments have higher n values and lower sSFR at 0.5 < z < 1, while there is no discernible distinction at 1 < z < 2.5. No significant enlarging or shrinking is found for GV galaxies in different environments within the same redshift bin. This suggests that a dense environment would promote the growth of bulges and suppress star formation activity of GV galaxies at 0.5 < z < 1.5 but would not affect the galaxy size. We also study the dependence of the fraction of three populations (blue cloud, GV, and red sequence) on both environments and M ⋆. At a given M ⋆, blue cloud fraction goes down with increasing environment density, while red sequence fraction is opposite. For the most massive GV galaxies, a sharp drop appears in the denser environment. Coupled with the mass dependence of three fractions in different redshift bins, our result implies that stellar mass and environments jointly promote the quenching process. Such a dual effect is also confirmed by recalculating the new effective GV fraction as the number of GV galaxies over the number of nonquiescent galaxies.