Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Gaussianity from $X, Y$ gauge bosons in Cosmological Collider Physics

Heavy fields of Hubble scale order present during inflation contribute to the non-Gaussian signature for the three-point function of the inflaton. Taking into account that Hubble scale is around the scale of grand unified theory (GUT), this opens a possibility that the GUT scale signatures, which are very hard to be discovered at collider, might be detectable by using information from the precise observations of cosmic microwave background. We discuss a detactability of the $X, Y$ gauge boson present in any GUT in a framework of cosmological collider physics. Calculating one-loop contributions of $X, Y$ gauge bosons to the inflaton three-point functions, we find a remarkable result that one-loop diagram with interactions originated from the mass terms of $X, Y$ gauge bosons provides an enhancement factor expressed by the ratio between the $X, Y$ gauge boson mass and Hubble scale as $(m_X/H)^4$. In an estimation of the non-Gaussianity, this factor is crucial and its impact on the detactability of $X, Y$ gauge bosons is discussed.
A No-Lose Theorem for Discovering the New Physics of $(g-2)_\mu$ at Muon Colliders

We perform a model-exhaustive analysis of all possible beyond Standard Model (BSM) solutions to the $(g-2)_\mu$ anomaly to study production of the associated new states at future muon colliders, and formulate a no-lose theorem for the discovery of new physics if the anomaly is confirmed and weakly coupled solutions below the GeV scale are excluded. Our goal is to find the highest possible mass scale of new physics subject only to perturbative unitarity, and optionally the requirements of minimum flavour violation (MFV) and/or naturalness. We prove that a 3 TeV muon collider is guaranteed to discover all BSM scenarios in which $\Delta a_\mu$ is generated by SM singlets with masses above $\sim $ GeV; lighter singlets will be discovered by upcoming low-energy experiments. If new states with electroweak quantum numbers contribute to $(g-2)_\mu$, the minimal requirements of perturbative unitarity guarantee new charged states below $\mathcal{O}(100 {\rm TeV})$, but this is strongly disfavoured by stringent constraints on charged lepton flavour violating (CLFV) decays. Reasonable BSM theories that satisfy CLFV bounds by obeying Minimal Flavour Violation (MFV) and avoid generating two new hierarchy problems require the existence of at least one new charged state below $\sim 10 - 20$ TeV. This strongly motivates the construction of high-energy muon colliders, which are guaranteed to discover new physics: either by producing these new charged states directly, or by setting a strong lower bound on their mass, which would empirically prove that the universe is fine-tuned and violates the assumptions of MFV while somehow not generating large CLFVs. The former case is obviously the desired outcome, but the latter scenario would perhaps teach us even more about the universe by profoundly revising our understanding of naturalness, cosmological vacuum selection, and the SM flavour puzzle.
Self-Calibrating Indoor Localization with Crowdsourcing Fingerprints and Transfer Learning

Precise indoor localization is one of the key requirements for fifth Generation (5G) and beyond, concerning various wireless communication systems, whose applications span different vertical sectors. Although many highly accurate methods based on signal fingerprints have been lately proposed for localization, their vast majority faces the problem of degrading performance when deployed in indoor systems, where the propagation environment changes rapidly. In order to address this issue, the crowdsourcing approach has been adopted, according to which the fingerprints are frequently updated in the respective database via user reporting. However, the late crowdsourcing techniques require precise indoor floor plans and fail to provide satisfactory accuracy. In this paper, we propose a low-complexity self-calibrating indoor crowdsourcing localization system that combines historical with frequently updated fingerprints for high precision user positioning. We present a multi-kernel transfer learning approach which exploits the inner relationship between the original and updated channel measurements. Our indoor laboratory experimental results with the proposed approach and using Nexus 5 smartphones at 2.4GHz with 20MHz bandwidth have shown the feasibility of about one meter level accuracy with a reasonable fingerprint update overhead.
Coloring the Black Box: What Synesthesia Tells Us about Character Embeddings

In contrast to their word- or sentence-level counterparts, character embeddings are still poorly understood. We aim at closing this gap with an in-depth study of English character embeddings. For this, we use resources from research on grapheme–color synesthesia – a neuropsychological phenomenon where letters are associated with colors –, which give us insight into which characters are similar for synesthetes and how characters are organized in color space. Comparing 10 different character embeddings, we ask: How similar are character embeddings to a synesthete’s perception of characters? And how similar are character embeddings extracted from different models? We find that LSTMs agree with humans more than transformers. Comparing across tasks, grapheme-to-phoneme conversion results in the most human-like character embeddings. Finally, ELMo embeddings differ from both humans and other models.
Quantum Chemistry Modeling of Homogeneous First-Row Transition Metal Catalysis

Catalysts accelerate rates of chemical reactions, and there is great interest in identifying catalysts that are effective, stable, and economical. This thesis reports two homogeneous chemical reaction mechanism studies using first principles quantum chemistry. The goal of this work is to understand the fundamental reaction pathways that reactions occur, so that one might eventually make improved catalysts. A cobalt-based catalyst system was recently reported for CO2 hydrogenation to form formate.1 It was hypothesized that a cooperative bifunctional catalyst involving an N-heterocyclic carbene (NHC) and this cobalt-based catalyst might further improve this chemistry. We investigated the likelihood that the NHC could function either as a Bronsted base to deprotonate a metal hydride or dihydrogen intermediate, or as a Lewis base to activate carbon dioxide towards nucleophilic attack. We also investigated fundamental catalysis involving heterobimetallic catalysts. We elucidated a complete mechanistic pathway for C−H boralytion with Cu−Fe catalysts to explain experimental observations and suggest improvements to the catalyst. This thesis aims to provide useful insight into canonical organometallic reaction mechanisms involved in bimetallic catalysts.
Finite-Sample Analysis of Two-Time-Scale Natural Actor–Critic Algorithm

Actor–critic style two-time-scale algorithms are one of the most popular methods in reinforcement learning, and have seen great empirical success. However, their performance is not completely understood theoretically. In this article, we characterize the <italic>global</italic> convergence of an online natural actor–critic algorithm in the tabular setting using a single trajectory of samples. Our analysis applies to very general settings, as we only assume ergodicity of the underlying Markov decision process. In order to ensure enough exploration, we employ an <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-greedy sampling of the trajectory. For a fixed and small enough exploration parameter <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>, we show that the two-time-scale natural actor–critic algorithm has a rate of convergence of <inline-formula><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/T^{1/4})$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> is the number of samples, and this leads to a sample complexity of <inline-formula><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/\delta ^{8})$</tex-math></inline-formula> samples to find a policy that is within an error of <inline-formula><tex-math notation="LaTeX">$\delta$</tex-math></inline-formula> from the global optimum. Moreover, by carefully decreasing the exploration parameter <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> as the iterations proceed, we present an improved sample complexity of <inline-formula><tex-math notation="LaTeX">$\tilde{\mathcal {O}}(1/\delta ^{6})$</tex-math></inline-formula> for convergence to the global optimum.
RTGPU: Real-Time GPU Scheduling of Hard Deadline Parallel Tasks With Fine-Grain Utilization

Many emerging cyber-physical systems, such as autonomous vehicles and robots, rely heavily on artificial intelligence and machine learning algorithms to perform important system operations. Since these highly parallel applications are computationally intensive, they need to be accelerated by graphics processing units (GPUs) to meet stringent timing constraints. However, despite the wide adoption of GPUs, efficiently scheduling multiple GPU applications while providing rigorous real-time guarantees remains challenging. Each GPU application has multiple CPU execution and memory copy segments, with GPU kernels running on different hardware resources. Because of the complicated interactions between heterogeneous segments of parallel tasks, high schedulability is hard to achieve with conventional approaches. This paper proposes RTGPU, which combines fine-grain GPU partitioning on the system-side with a novel scheduling algorithm on the theory-side. We start by building a model for CPU and memory copy segments. Leveraging persistent threads, we then implement fine-grained GPU partitioning with improved performance through interleaved execution. To reap the benefits of fine-grained GPU partitioning and schedule multiple parallel GPU applications, we propose a novel real-time scheduling algorithm based on federated scheduling and grid search with uniprocessor fixed-priority scheduling. Our approach provides real-time guarantees to meet hard deadlines and achieves over 11% improvement in system throughput and up to 57% schedulability improvement compared with previous work. We validate and evaluate RTGPU on NVIDIA GPU systems. Our system-side techniques can be applied on mainstream GPUs, and the proposed scheduling theory can be used in general heterogeneous computing platforms which have a similar task execution pattern.
Shear-induced orientational ordering in an active glass former

Significance Dense active matter systems are ubiquitous in nature, with examples ranging from the cytoplasm to tissues of motile cells to vehicular traffic jams. Many recent studies have explored the dynamical and transport aspects of such active glasses. However, an understanding of the rheological behavior of these fascinating out-of-equilibrium systems (i.e., their response to mechanical deformation) remains elusive. Using extensive molecular dynamics simulation of a model active glassy system under steady shear, we establish the existence of different dynamical states: disordered, propulsion-induced ordered, and shear-induced ordered (SIO). Combining simulation results with an analytical theory, we rationalize the qualitative features of the SIO state, which is facilitated by the applied shear and appears without any alignment interactions or particle shape anisotropies. Dense assemblies of self-propelled particles that can form solid-like states also known as active or living glasses are abundant around us, covering a broad range of length scales and timescales: from the cytoplasm to tissues, from bacterial biofilms to vehicular traffic jams, and from Janus colloids to animal herds. Being structurally disordered as well as strongly out of equilibrium, these systems show fascinating dynamical and mechanical properties. Using extensive molecular dynamics simulation and a number of distinct dynamical and mechanical order parameters, we differentiate three dynamical steady states in a sheared model active glassy system: 1) a disordered state, 2) a propulsion-induced ordered state, and 3) a shear-induced ordered state. We supplement these observations with an analytical theory based on an effective single-particle Fokker–Planck description to rationalize the existence of the shear-induced orientational ordering behavior in an active glassy system without explicit aligning interactions of, for example, Vicsek type. This ordering phenomenon occurs in the large persistence time limit and is made possible only by the applied steady shear. Using a Fokker–Planck description with parameters that can be measured independently, we make testable predictions for the joint distribution of single-particle position and orientation. These predictions match well with the joint distribution measured from direct numerical simulation. Our results are of relevance for experiments exploring the rheological response of dense active colloids and jammed active granular matter systems.
T-Quadratic Forms and Spectral Analysis of T-Symmetric Tensors

An n× n× p tensor is called a T-square tensor. It arises from many applications, such as the image feature extraction problem and the multi-view clustering problem. We may symmetrize a T-square tensor to a T-symmetric tensor. For each T-square tensor, we define a T-quadratic form, whose variable is an n × p matrix, and whose value is a p-dimensional vector. We define eigentuples and eigenmatrices for T-square tensors. We show that a T-symmetric tensor has unique largest and smallest eigentuples, and a T-quadratic form is positive semidefinite (definite) if and only if its smallest eigentuple is nonnegative (positive). The relation between the eigen-decomposition of T-symmetric tensors, and the TSVD of general third order tensors are also studied.
The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games

What if emotion could be captured in a general and subject-agnostic fashion? Is it possible, for instance, to design general-purpose representations that detect affect solely from the pixels and audio of a human-computer interaction video? In this article we address the above questions by evaluating the capacity of deep learned representations to predict affect by relying only on audiovisual information of videos. We assume that the pixels and audio of an interactive session embed the necessary information required to detect affect. We test our hypothesis in the domain of digital games and evaluate the degree to which deep classifiers and deep preference learning algorithms can learn to predict the arousal of players based only on the video footage of their gameplay. Our results from four dissimilar games suggest that general-purpose representations can be built across games as the arousal models obtain average accuracies as high as 85 percent using the challenging leave-one-video-out cross-validation scheme. The dissimilar audiovisual characteristics of the tested games showcase the strengths and limitations of the proposed method.
Switch Peptides: pH‐Induced α‐Helix to β‐Sheet Transitions of Bis‐amphiphilic Oligopeptides

The interconversion of two secondary structure types (alpha-helix reversible beta-sheet) induced by external conditions such as pH, temperature, concentration, and solvent could be proved in a new class of amphiphilic peptides (switch peptides) in aqueous solution. The pH-dependent conformational changes were studied by CD spectroscopy. Special attention was given to the investigations of four newly synthesized peptides with similar sequences.
El Volumen Louder Por Favor: Code-switching in Task-oriented Semantic Parsing

Being able to parse code-switched (CS) utterances, such as Spanish+English or Hindi+English, is essential to democratize task-oriented semantic parsing systems for certain locales. In this work, we focus on Spanglish (Spanish+English) and release a dataset, CSTOP, containing 5800 CS utterances alongside their semantic parses. We examine the CS generalizability of various Cross-lingual (XL) models and exhibit the advantage of pre-trained XL language models when data for only one language is present. As such, we focus on improving the pre-trained models for the case when only English corpus alongside either zero or a few CS training instances are available. We propose two data augmentation methods for the zero-shot and the few-shot settings: fine-tune using translate-and-align and augment using a generation model followed by match-and-filter. Combining the few-shot setting with the above improvements decreases the initial 30-point accuracy gap between the zero-shot and the full-data settings by two thirds.
A Missing Data Imputation Method for 3D Object Reconstruction using Multi-modal Variational Autoencoder

For effective human-robot teaming, it is importantfor the robots to be able to share their visual perceptionwith the human operators. In a harsh remote collaborationsetting, however, it is especially challenging to transfer a largeamount of sensory data over a low-bandwidth network in real-time, e.g., for the task of 3D shape reconstruction given 2Dcamera images. To reduce the burden of data transferring, datacompression techniques such as autoencoder can be utilized toobtain and transmit the data in terms of latent variables in acompact form. However, due to the low-bandwidth limitation orcommunication delay, some of the dimensions of latent variablescan be lost in transit, degenerating the reconstruction results.Moreover, in order to achieve faster transmission, an intentionalover compression can be used where only partial elements ofthe latent variables are used. To handle these incomplete datacases, we propose a method for imputation of latent variableswhose elements are partially lost or manually excluded. Toperform imputation with only some dimensions of variables,exploiting prior information of the category- or instance-levelis essential. In general, a prior distribution used in variationalautoencoders is achieved from all of the training datapointsregardless of their labels. This type of flattened prior makes itdifficult to perform imputation from the category- or instance-level distributions.
Rods coiling about a rigid constraint: helices and perversions

Mechanical instabilities can be exploited to design innovative structures, able to change their shape in the presence of external stimuli. In this work, we derive a mathematical model of an elastic beam subjected to an axial force and constrained to smoothly slide along a rigid support, where the distance between the rod midline and the constraint is fixed and finite. Using both theoretical and computational techniques, we characterize the bifurcations of such a mechanical system, in which the axial force and the natural curvature of the beam are used as control parameters. We show that, in the presence of a straight support, the rod can deform into shapes exhibiting helices and perversions, namely transition zones connecting together two helices with opposite chirality. The mathematical predictions of the proposed model are also compared with some experiments, showing a good quantitative agreement. In particular, we find that the buckled configurations may exhibit multiple perversions and we propose a possible explanation for this phenomenon based on the energy landscape of the mechanical system.
Deep View Synthesis via Self-Consistent Generative Network

View synthesis aims to produce unseen views from a set of views captured by two or more cameras at different positions. This task is non-trivial since it is hard to conduct pixel-level matching among different views. To address this issue, most existing methods seek to exploit the geometric information to match pixels. However, when the distinct cameras have a large baseline (i. e., far away from each other), severe geometry distortion issues would occur and the geometric information may fail to provide useful guidance, resulting in very blurry synthesized images. To address the above issues, in this paper, we propose a novel deep generative model, called Self-Consistent Generative Network (SCGN), which synthesizes novel views from the given input views without explicitly exploiting the geometric information. The proposed SCGN model consists of two main components, i. e. , a View Synthesis Network (VSN) and a View Decomposition Network (VDN), both employing an Encoder-Decoder structure. Here, the VDN seeks to reconstruct input views from the synthesized novel view to preserve the consistency of view synthesis. Thanks to VDN, SCGN is able to synthesize novel views without using any geometric rectification before encoding, making it easier for both training and applications. Finally, adversarial loss is introduced to improve the photo-realism of novel views. Both qualitative and quantitative comparisons against several state-of-the-art methods on two benchmark tasks demonstrated the superiority of our approach.
A Local Baseline of the Black Hole Mass Scaling Relations for Active Galaxies. IV. Correlations Between M BH and Host Galaxy σ, Stellar Mass, and Luminosity

The tight correlations between the mass of supermassive black holes (M BH) and their host-galaxy properties have been of great interest to the astrophysical community, but a clear understanding of their origin and fundamental drivers still eludes us. The local relations for active galaxies are interesting in their own right and form the foundation for any evolutionary study over cosmic time. We present Hubble Space Telescope optical imaging of a sample of 66 local active galactic nuclei (AGNs); for 14 objects, we also obtained Gemini near-infrared images. We use state-of-the-art methods to perform surface photometry of the AGN host galaxies, decomposing them into spheroid, disk, and bar (when present), and inferring the luminosity and stellar mass of the components. We combine this information with spatially resolved kinematics obtained at the Keck Telescopes to study the correlations between M BH (determined from single-epoch virial estimators) and host galaxy properties. The correlations are uniformly tight for our AGN sample, with intrinsic scatter 0.2–0.4 dex, smaller than or equal to that of quiescent galaxies. We find no difference between pseudo and classical bulges or barred and nonbarred galaxies. We show that all the tight correlations can be simultaneously satisfied by AGN hosts in the 107–109 M ⊙ regime, with data of sufficient quality. The M BH–σ relation is also in agreement with that of AGN with M BH obtained from reverberation mapping, providing an indirect validation of single-epoch virial estimators of M BH.
Isolated skyrmions in the 
CP2
 nonlinear sigma model with a Dzyaloshinskii-Moriya type interaction

We study two dimensional soliton solutions in the $CP^2$ nonlinear $\sigma$-model with a Dzyaloshinskii-Moriya type interaction. First, we derive such a model as a continuous limit of the $SU(3)$ tilted ferromagnetic Heisenberg model on a square lattice. Then, introducing an additional potential term to the derived Hamiltonian, we obtain exact soliton solutions for particular sets of parameters of the model. The vacuum of the exact solution can be interpreted as a spin nematic state. For a wider range of coupling constants, we construct numerical solutions, which possess the same type of asymptotic decay as the exact analytical solution, both decaying into a spin nematic state.