Miracle-dz/UniFAR-FTUs huggingface dataset:
Regimes of atomic diffraction: Raman versus Bragg diffraction in retroreflective geometries

We provide a comprehensive study of atomic Raman and Bragg diffraction when coupling to a pair of counter-propagating light gratings (double diffraction) or to a single one (single diffraction) and discuss the transition from one case to the other in a retro-reflective geometry as the Doppler detuning changes. In contrast to single diffraction, double Raman loses its advantage of high diffraction efficiency for short pulses and has to be performed in a Bragg-type regime. Moreover, the structure of double diffraction leads to further limitations for broad momentum distributions on the efficiency of mirror pulses, making the use of ultra-cold ensembles essential for high diffraction efficiency.
Multiple monoenergetic gamma radiography (MMGR) with a compact superconducting cyclotron

Smuggling of special nuclear materials (SNM) and nuclear devices through borders and ports of entry constitutes a major risk to global security. Technologies are needed to reliably screen the flow of commerce for the presence of high-$Z$ materials such as uranium and plutonium. Here we present an experimental proof-of-concept of a technique which uses inelastic ($p,p'$) nuclear reactions to generate monoenergetic photons, which provide means to measure the areal density and the effective-$Z$ ($Z_{\text{eff}}$) of an object with an accuracy which surpasses that achieved by current methods. We use an ION-12$^{ \text{SC}}$ superconducting 12~MeV proton cyclotron to produce 4.4, 6.1, 6.9, and 7.1~MeV photons from a variety of nuclear reactions. Using these photons in a transmission mode we show that we are able to accurately reconstruct the areal densities and $Z_{\text{eff}}$ of a test object. This methodology could enable mobile applications to screen commercial cargoes with high material specificity, providing a means of distinguishing common cargo materials from high-Z materials that include uranium and plutonium.
Jejueo Datasets for Machine Translation and Speech Synthesis

Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated by this, we construct two new Jejueo datasets: Jejueo Interview Transcripts (JIT) and Jejueo Single Speaker Speech (JSS). The JIT dataset is a parallel corpus containing 170k+ Jejueo-Korean sentences, and the JSS dataset consists of 10k high-quality audio files recorded by a native Jejueo speaker and a transcript file. Subsequently, we build neural systems of machine translation and speech synthesis using them. All resources are publicly available via our GitHub repository. We hope that these datasets will attract interest of both language and machine learning communities.
An Analysis of Speech Enhancement and Recognition Losses in Limited Resources Multi-Talker Single Channel Audio-Visual ASR

In this paper, we analyzed how audio-visual speech enhancement can help to perform the ASR task in a cocktail party scenario. Therefore we considered two simple end-to-end LSTM-based models that perform single-channel audiovisual speech enhancement and phone recognition respectively. Then, we studied how the two models interact, and how to train them jointly affects the final result.We analyzed different training strategies that reveal some interesting and unexpected behaviors. The experiments show that during optimization of the ASR task the speech enhancement capability of the model significantly decreases and vice-versa. Nevertheless the joint optimization of the two tasks shows a remarkable drop of the Phone Error Rate (PER) compared to the audio-visual baseline models trained only to perform phone recognition. We analyzed the behaviors of the proposed models by using two limited-size datasets, and in particular we used the mixed-speech versions of GRID and TCD-TIMIT.
Viscous Evolution of Magnetized Clumps: A Source for X-Ray Flares in Gamma-Ray Bursts

X-ray flares may indicate the late-time activity of the central engines of gamma-ray bursts. Such long-term activity has been described through some models, one of which is the viscous evolution of the outer disk’s fragments proposed by Perna et al., and developed quantitatively by Dall’Osso et al. Here, we reconstruct the framework of Dall’Osso et al. by taking both small- and large-scale effects of magnetic field into account. To consider the magnetic barrier as a possible mechanism that might govern the accretion process of each magnetized clump, we construct a simple pattern in boundary conditions through which this mechanism might act. Regarding various model parameters, we probe for their influence and follow some key analogies between our model predictions and previous phenomenological estimates, for two different choices of boundary conditions (with and without a magnetic barrier). Our model is remarkably capable of matching the bolometric and X-ray light curves of flares, as well as reproducing their statistical properties, such as the ratios between rise and decay time, width parameter and peak time, and the power-law correlation between peak luminosity and peak time. Combining our results with the conclusions of previous studies, we are led to interpret a magnetic barrier as a less probable mechanism that might control the evolution of these clumps, especially those created later (or viscously evolved ones).
Scalable additive manufacturing via integral image formation

Additive manufacturing techniques are used to fabricate functional microstructures with customised mechanical and chemical properties. One common technique is projection microstereolithography, which has recently been developed to support smaller features, larger build areas, and/or faster speeds. The limitation of such systems is that they typically utilise a single-aperture imaging configuration, which restricts their ability to produce microstructures in large volumes owing to the tradeoff between image resolution and image field area. In this paper, we propose an integral lithography technique based on integral image reconstruction coupled with a planar lens array. The individual microlenses in the planar lens array maintain a high numerical aperture and are employed to create digital light patterns that can expand the printable area by the number of microlenses (10^3-10^4). The proposed lens array-based integral imaging system can simultaneously scale up and scale down incoming image fields, thereby allowing for the scalable stereolithographic fabrication of three-dimensional features that surpass the resolution-to-area scaling limit. We extend the printing capability of integral lithography to fabricate deterministic nonperiodic structures through the rotational overlapping or stacking of multiple exposures with controlled angular offsets. The proposed system provides new possibilities for producing periodic and deterministic aperiodic microarchitectures spanning four orders of magnitude from micrometres to centimetres. These microarchitectures can be applied to biological scaffolds, chemical reactors, functional surfaces, and metastructures.
Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning

In this introductory paper, we discuss how quantitative finance problems under some common risk factor dynamics for some common instruments and approaches can be formulated as time-continuous or time-discrete forward-backward stochastic differential equations (FBSDE) final-value or control problems, how these final value problems can be turned into control problems, how time-continuous problems can be turned into time-discrete problems, and how the forward and backward stochastic differential equations (SDE) can be time-stepped. We obtain both forward and backward time-stepped time-discrete stochastic control problems (where forward and backward indicate in which direction the $Y$ SDE is time-stepped) that we will solve with optimization approaches using deep neural networks for the controls and stochastic gradient and other deep learning methods for the actual optimization/learning. We close with examples for the forward and backward methods for an European option pricing problem. Several methods and approaches are new.
Effects of spatial smoothing on group-level differences in functional brain networks

Abstract Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.
Fr\'echet Change Point Detection.

We propose a method to infer the presence and location of change-points in the distribution of a sequence of independent data taking values in a general metric space, where change-points are viewed as locations at which the distribution of the data sequence changes abruptly in terms of either its Frechet mean or Frechet variance or both. The proposed method is based on comparisons of Frechet variances before and after putative change-point locations and does not require a tuning parameter except for the specification of cut-off intervals near the endpoints where change-points are assumed not to occur. Our results include theoretical guarantees for consistency of the test under contiguous alternatives when a change-point exists and also for consistency of the estimated location of the change-point if it exists, where under the null hypothesis of no change-point the limit distribution of the proposed scan function is the square of a standardized Brownian Bridge. These consistency results are applicable for a broad class of metric spaces under mild entropy conditions. Examples include the space of univariate probability distributions and the space of graph Laplacians for networks. Simulation studies demonstrate the effectiveness of the proposed methods, both for inferring the presence of a change-point and estimating its location. We also develop theory that justifies bootstrap-based inference and illustrate the new approach with sequences of maternal fertility distributions and communication networks.
Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking

Multi-target multi-camera tracking (MTMCT) systems track targets across cameras. Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood. In single camera tracking, local neighborhood refers to consecutive frames; in multi-camera tracking, it refers to neighboring cameras that the target may appear successively. For similarity estimation, tracking systems often adopt appearance features learned from the re-identification (re-ID) perspective. Different from tracking, re-ID usually does not have access to the trajectory cues that can limit the search space to a local neighborhood. Due to its global matching property, the re-ID perspective requires to learn global appearance features. We argue that the mismatch between the local matching procedure in tracking and the global nature of re-ID appearance features may compromise MTMCT performance. To fit the local matching procedure in MTMCT, in this work, we introduce locality aware appearance metric (LAAM). Specifically, we design an intra-camera metric for single camera tracking, and an inter-camera metric for multi-camera tracking. Both metrics are trained with data pairs sampled from their corresponding local neighborhoods, as opposed to global sampling in the re-ID perspective. We show that the locally learned metrics can be successfully applied on top of several globally learned re-ID features. With the proposed method, we report new state-of-the-art performance on the DukeMTMC dataset, and a substantial improvement on the CityFlow dataset.
Quantum ultra-walks: Walks on a line with hierarchical spatial heterogeneity

We discuss the model of a one-dimensional, discrete-time walk on a line with spatial heterogeneity in the form of a variable set of ultrametric barriers. Inspired by the homogeneous quantum walk on a line, we develop a formalism by which the classical ultrametric random walk as well as the quantum walk can be treated in parallel by using a "coined" walk with internal degrees of freedom. For the random walk, this amounts to a $2^{\rm nd}$-order Markov process with a \emph{stochastic} coin, better known as an (anti-)persistent walk. When this coin varies spatially in the hierarchical manner of "ultradiffusion," it reproduces the well-known results of that model. The exact analysis employed for obtaining the walk dimension $d_{w}$, based on the real-space renormalization group (RG), proceeds virtually identical for the corresponding quantum walk with a $unitary$ coin. However, while the classical walk remains robustly diffusive ($d_{w}=\frac{1}{2}$) for a wide range of barrier heights, unitarity provides for a quantum walk dimension $d_{w}$ that varies continuously, for even the smallest amount of heterogeneity, from ballistic spreading ($d_{w}=1$) in the homogeneous limit to confinement ($d_{w}=\infty$) for diverging barriers. Yet for any $d_{w}<\infty$ the quantum ultra-walk never appears to localize.
Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting

Traffic forecasting is of great importance to transportation management and public safety, and very challenging due to the complicated spatial-temporal dependency and essential uncertainty brought about by the road network and traffic conditions. Latest studies mainly focus on modeling the spatial dependency by utilizing graph convolutional networks (GCNs) throughout a fixed weighted graph. However, edges, i.e., the correlations between pair-wise nodes, are much more complicated and interact with each other. In this paper, we propose the Multi-Range Attentive Bicomponent GCN (MRA-BGCN), a novel deep learning model for traffic forecasting. We first build the node-wise graph according to the road network distance and the edge-wise graph according to various edge interaction patterns. Then, we implement the interactions of both nodes and edges using bicomponent graph convolution. The multi-range attention mechanism is introduced to aggregate information in different neighborhood ranges and automatically learn the importance of different ranges. Extensive experiments on two real-world road network traffic datasets, METR-LA and PEMS-BAY, show that our MRA-BGCN achieves the state-of-the-art results.
Evaluating Commonsense in Pre-trained Language Models

Contextualized representations trained over large raw text data have given remarkable improvements for NLP tasks including question answering and reading comprehension. There have been works showing that syntactic, semantic and word sense knowledge are contained in such representations, which explains why they benefit such tasks. However, relatively little work has been done investigating commonsense knowledge contained in contextualized representations, which is crucial for human question answering and reading comprehension. We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models' commonsense ability while bi-directional context and larger training set are bonuses. We additionally find that current models do poorly on tasks require more necessary inference steps. Finally, we test the robustness of models by making dual test cases, which are correlated so that the correct prediction of one sample should lead to correct prediction of the other. Interestingly, the models show confusion on these test cases, which suggests that they learn commonsense at the surface rather than the deep level. We release a test set, named CATs publicly, for future research.
Testing Modified Gravity with Acceleration Relations in the Milky Way

The dynamical mass of galaxies and the Newtonian acceleration generated from the baryons have been found to be strongly correlated. This correlation is known as 'Mass-Discrepancy Acceleration Relation' (MDAR). Further investigations have revealed a tighter relation - 'Radial Acceleration Relation' (RAR) - between the observed total acceleration and the (Newtonian) acceleration produced by the baryons. So far modified gravity theories have remained more successful than $\Lambda$CDM to explain these relations. However, a recent investigation has pointed out that, when RAR is expressed as a difference between the observed acceleration and the expected Newtonian acceleration due to baryons (which has been called the 'Halo acceleration relation or HAR'), it provides a stronger test for modified gravity theories and dark matter hypothesis. Extending our previous work \citep{kt2018}, we present a case study of modified gravity theories, in particular Weyl conformal gravity and Modified Newtonian Dynamics (MOND), using recent inferred acceleration data for the Milky Way. We investigate how well these theories of gravity and the RAR scaling law can explain the current observation.
Holographic astigmatic particle tracking velocimetry (HAPTV)

The formation of twin images in digital inline holography (DIH) prevents the placement of the focal plane in the center of a sample volume for DIH-based particle tracking velocimetry (DIH-PTV) with a single camera. As a result, it is challenging to apply DIH-PTV for flow measurements in large-scale laboratory facilities or many field applications where it would otherwise be desirable due to the low cost and compact setup. Here we introduce holographic astigmatic PTV (HAPTV) by inserting a cylindrical lens in the optical setup of DIH-PTV, generating distorted holograms. Such distortion is subsequently utilized in a customized reconstruction algorithm to distinguish tracers positioned on different sides of the focal plane which can in turn be placed in the middle of a sample volume. Our HAPTV approach is calibrated under high (1 µm pixel−1) and low (10 µm pixel−1) magnifications with an error standard deviation of 4.2 µm (one particle diameter) and 120.7 µm (~5 times the particle diameter), respectively. We compare the velocity field of a laminar jet flow obtained using HAPTV and conventional PIV to illustrate the accuracy of the technique when applied to practical flow measurement applications. The work demonstrates that HAPTV improves upon the depth of field of conventional astigmatic PTV and enables the implementation of DIH-based PTV for in situ applications.
Low density majority codes and the problem of graceful degradation

We study a problem of constructing codes that transform a channel with high bit error rate (BER) into one with low BER (at the expense of rate). Our focus is on obtaining codes with smooth ("graceful'') input-output BER curves (as opposed to threshold-like curves typical for long error-correcting codes). This paper restricts attention to binary erasure channels (BEC) and contains three contributions. First, we introduce the notion of Low Density Majority Codes (LDMCs). These codes are non-linear sparse-graph codes, which output majority function evaluated on randomly chosen small subsets of the data bits. This is similar to Low Density Generator Matrix codes (LDGMs), except that the XOR function is replaced with the majority. We show that even with a few iterations of belief propagation (BP) the attained input-output curves provably improve upon performance of any linear systematic code. The effect of non-linearity bootstraping the initial iterations of BP, suggests that LDMCs should improve performance in various applications, where LDGMs have been used traditionally. Second, we establish several \textit{two-point converse bounds} that lower bound the BER achievable at one erasure probability as a function of BER achieved at another one. The novel nature of our bounds is that they are specific to subclasses of codes (linear systematic and non-linear systematic) and outperform similar bounds implied by the area theorem for the EXIT function.
Aspects of astrophysical particle production and beyond the Standard Model phenomenology

This PhD thesis deals with various aspects of (astro)particle physics phenomenology and consists out of two parts: beyond the Standard Model physics and neutrino astronomy. In the first part, I focus on beyond the Standard Model physics at the Large Hadron collider. Concretely, I discuss the interpretation of a 2015 excess seen by ATLAS in events with jets, missing transverse momentum and 2 same-flavour, opposite-charge leptons within a model of gauge-mediated supersymmetry breaking. Our model, which features supersymmetry breaking in multiple hidden sectors, was able to explain the 2015 data because of the appearance of an additional massive neutral particle at the bottom of the spectrum, with couplings dictated by supersymmetry. However, using more recent data, I show that this specific implementation of the model is now ruled out. In the second part, I focus on astroparticle physics and neutrino astronomy. This part consists of two projects. The first and largest of these investigates the possibility that the neutrinos seen by IceCube are produced by sources obscured by matter. We show that, in this case, existing bounds from the extragalactic gamma-ray background can be evaded. In addition, we apply our model to a set of sources selected for possible obscuration and obtain relevant bounds on the parameter space. In the final project, I investigate current constraints on neutrino emission from binary black hole mergers. While no such neutrinos are typically expected, current constraints are not yet able to rule out a substantial contribution of such events to the astrophysical neutrino flux. I show that in the near future, this possibility will be constrained. Both parts are preceded by an extensive introduction and review of their respective fields, written to be of general interest.
The Reconstruction Approach: From Interpolation to Regression

Abstract This article introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least squares. Some popular methods including kernel ridge regression can be viewed as its special cases. It is shown that the reconstruction idea not only provides different angles to look into existing methods, but also produces new effective experimental design and estimation methods for nonparametric models. In particular, for some methods of complexity , where n is the sample size, this approach provides effective surrogates with much less computational burden. This point makes it very suitable for large datasets. Supplementary materials for this article are available online.