Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum bridges in phase space: interference and nonclassicality in strong-field enhanced ionisation

We perform a phase-space analysis of strong-field enhanced ionisation in molecules, with emphasis on quantum-interference effects. Using Wigner quasi-probability distributions and the quantum Liouville equation, we show that the momentum gates reported in a previous publication (Takemoto and Becker 2011 Phys. Rev. A 84 023401) may occur for static driving fields, and even for no external field at all. Their primary cause is an interference-induced bridging mechanism that occurs if both wells in the molecule are populated. In the phase-space regions for which quantum bridges occur, the Wigner functions perform a clockwise rotation whose period is intrinsic to the molecule. This evolution is essentially non-classical and non-adiabatic, as it does not follow equienergy curves or field gradients. Quasi-probability transfer via quantum bridges is favoured if the electron’s initial state is either spatially delocalised, or situated at the upfield molecular well. Enhanced ionisation results from the interplay of this cyclic motion, adiabatic tunnel ionisation and population trapping. Optimal conditions require minimising population trapping and using the bridging mechanism to feed into ionisation pathways along the field gradient.
Discovering Encrypted Bot and Ransomware Payloads Through Memory Inspection Without A Priori Knowledge

Malware writers frequently try to hide the activities of their agents within tunnelled traffic. Within the Kill Chain model the infection time is often measured in seconds, and if the infection is not detected and blocked, the malware agent, such as a bot, will often then set up a secret channel to communicate with its controller. In the case of ransomware the communicated payload may include the encryption key used for the infected host to register its infection. As a malware infection can spread across a network in seconds, it is often important to detect its activities on the air, in memory and at-rest. Malware increasingly uses encrypted channels for communicating with their controllers. This paper presents a new approach to discovering the cryptographic artefacts of real malware clients that use cryptographic libraries of the Microsoft Windows operating system. This enables malware secret communications to be discovered without any prior malware knowledge.
A Survey of Magnetic Field Strengths in the Envelopes of Molecular Clouds via the 18 cm OH Zeeman Effect

We present the results of an extensive Arecibo observational survey of magnetic field strengths in the inter-core regions of molecular clouds to determine their role in the evolution and collapse of molecular clouds as a whole. Sensitive 18 cm OH Zeeman observations of absorption lines from Galactic molecular gas in the direction of extragalactic continuum sources yielded 38 independent measurements of magnetic field strengths. Zeeman detections were achieved at the 3σ level toward nine clouds, while the others revealed sensitive upper limits to the magnetic field strength. Our results suggest that total field strengths in the inter-core regions of giant molecular clouds (GMCs) are about 15 μG.
Properties of Subhalos in the Interacting Dark Matter Scenario

One possible and natural derivation from the collisionless cold dark matter (CDM) standard cosmological framework is the assumption of the existence of interactions between dark matter (DM) and photons or neutrinos. Such a possible interacting dark matter (IDM) model would imply a suppression of small-scale structures due to a large collisional damping effect, even though the weakly-interacting massive particle (WIMP) can still be the DM candidate. Because of this, IDM models can help alleviate alleged tensions between standard CDM predictions and observations at small mass scales. In this work, we investigate the properties of the DM halo substructure or subhalos formed in a high-resolution cosmological N-body simulation specifically run within these alternative models. We also run its CDM counterpart, which allowed us to compare subhalo properties in both cosmologies. We show that, in the lower mass range covered by our simulation runs, both subhalo concentrations and abundances are systematically lower in IDM compared to the CDM scenario. Yet, as in CDM, we find that median IDM subhalo concentration values increase towards the innermost regions of their hosts for the same mass subhalos. Similarly to CDM, we find IDM subhalos to be more concentrated than field halos of the same mass. Our work has a direct application to studies aimed at the indirect detection of DM where subhalos are expected to boost the DM signal of their host halos significantly. From our results, we conclude that the role of the halo substructure in DM searches will be less important in interacting scenarios than in CDM, but is nevertheless far from being negligible.
A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data

ABSTRACT This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators, and a control group. Supplementary materials for this article are available online.
A Lower Bound on Cycle-Finding in Sparse Digraphs

We consider the problem of finding a cycle in a sparse directed graph G that is promised to be far from acyclic, meaning that the smallest feedback arc set, i.e., a subset of edges whose deletion results in an acyclic graph, in G is large. We prove an information-theoretic lower bound, showing that for N-vertex graphs with constant outdegree, any algorithm for this problem must make Ω̄(N5/9) queries to an adjacency list representation of G. In the language of property testing, our result is an Ω̄(N5/9) lower bound on the query complexity of one-sided algorithms for testing whether sparse digraphs with constant outdegree are far from acyclic. This is the first improvement on the Ω (√ N) lower bound, implicit in the work of Bender and Ron, which follows from a simple birthday paradox argument.
StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion

Non-parallel multi-domain voice conversion (VC) is a technique for learning mappings among multiple domains without relying on parallel data. This is important but challenging owing to the requirement of learning multiple mappings and the non-availability of explicit supervision. Recently, StarGAN-VC has garnered attention owing to its ability to solve this problem only using a single generator. However, there is still a gap between real and converted speech. To bridge this gap, we rethink conditional methods of StarGAN-VC, which are key components for achieving non-parallel multi-domain VC in a single model, and propose an improved variant called StarGAN-VC2. Particularly, we rethink conditional methods in two aspects: training objectives and network architectures. For the former, we propose a source-and-target conditional adversarial loss that allows all source domain data to be convertible to the target domain data. For the latter, we introduce a modulation-based conditional method that can transform the modulation of the acoustic feature in a domain-specific manner. We evaluated our methods on non-parallel multi-speaker VC. An objective evaluation demonstrates that our proposed methods improve speech quality in terms of both global and local structure measures. Furthermore, a subjective evaluation shows that StarGAN-VC2 outperforms StarGAN-VC in terms of naturalness and speaker similarity. The converted speech samples are provided at this http URL
Parameterized Valiant's Classes

We define a theory of parameterized algebraic complexity classes in analogy to parameterized Boolean counting classes. We define the classes VFPT and VW[t], which mirror the Boolean counting classes #FPT and #W[t], and define appropriate reductions and completeness notions. Our main contribution is the VW[1]-completeness proof of the parameterized clique family. This proof is far more complicated than in the Boolean world. It requires some new concepts like composition theorems for bounded exponential sums and Boolean-arithmetic formulas. In addition, we also look at two polynomials linked to the permanent with vastly different parameterized complexity.
On the Value of Bandit Feedback for Offline Recommender System Evaluation

In academic literature, recommender systems are often evaluated on the task of next-item prediction. The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1? ". Evaluation results obtained through said methodology are then used as a proxy to predict which system will perform better in an online setting. The online setting, however, poses a subtly different question: "Given the natural sequence of user-item interactions up to time t, can we get the user to interact with a recommended item at time t+1? ". From a causal perspective, the system performs an intervention, and we want to measure its effect. Next-item prediction is often used as a fall-back objective when information about interventions and their effects (shown recommendations and whether they received a click) is unavailable. When this type of data is available, however, it can provide great value for reliably estimating online recommender system performance. Through a series of simulated experiments with the RecoGym environment, we show where traditional offline evaluation schemes fall short. Additionally, we show how so-called bandit feedback can be exploited for effective offline evaluation that more accurately reflects online performance.
Generating functions for vector partition functions and a basic recurrence relation

ABSTRACT We define a generalized vector partition function and derive an identity for the generating series of such functions associated with solutions to basic recurrence relations of combinatorial analysis. As a consequence we obtain the generating function of the number of generalized lattice paths and a new version of the Chaundy-Bullard identity for the vector partition function.
Tactile Feedback for Dexterous Manipulation Operations using Assistive Prosthetic Fingers

ii ACKNOWLEDGEMENTS . iii CONTENTS . iv LIST OF FIGURES . vii LIST OF TABLES . ix GLOSSARY OF TERMS . x CHAPTER 1 – INTRODUCTION . 1 1.1 Context . 1 1.2 Research Objectives and Contributions . 4 1.3 Publications Arising from Thesis . 5 1.4 Outline. 6 CHAPTER 2 – FOUNDATIONAL PRINCIPLES . 7 2.1 Human Haptic System . 7 2.1.1 The Tactile Sensory Receptors . 9 2.1.2 The Nervous System . 12 2.1.3 The Brain . 15 2.2 Prosthetics . 17 2.2.1 Prosthesis Control . 20 2.2.2 Prosthesis Feedback . 21 2.3 Haptic Perception. 22
Developing Workforce with Mathematical Modeling Skills

Mathematicians have traditionally been a select group of academics that produce high-impact ideas allowing substantial results in several fields of science. Throughout the past 35 years, undergraduates enrolling in mathematics or statistics have represented a nearly constant rate of approximately 1% of bachelor degrees awarded in the United States. Even within STEM majors, mathematics or statistics only constitute about 6% of undergraduate degrees awarded nationally. However, the need for STEM professionals continues to grow and the list of needed occupational skills rests heavily in foundational concepts of mathematical modeling curricula, where the interplay of data, computer simulation and underlying theoretical frameworks takes center stage. It is not viable to expect a majority of these STEM undergraduates to pursue a double-major that includes mathematics. Here we present our solution, some early results of implementation, and a vision for possible nationwide adoption.
A model for grain boundary thermodynamics

Systematic microstructure design requires reliable thermodynamic descriptions of each and all microstructure elements. While such descriptions are well established for most bulk phases, thermodynamic assessment of microstructure defects is challenging because of their individualistic nature. In this paper, a model is devised for assessing grain boundary thermodynamics based on available bulk thermodynamic data. We propose a continuous relative atomic density field and its spatial gradients to describe the grain boundary region with reference to the homogeneous bulk and derive the grain boundary Gibbs free energy functional. The grain boundary segregation isotherm and phase diagram are computed for a regular binary solid solution, and qualitatively benchmarked for the Pt–Au system. The relationships between the grain boundary's atomic density, excess free volume, and misorientation angle are discussed. Combining the current density-based model with available bulk thermodynamic databases enables constructing databases, phase diagrams, and segregation isotherms for grain boundaries, opening possibilities for studying and designing heterogeneous microstructures.
A View on Edge caching Applications

Devices with the ability to connect to the internet are growing in numbers day by day thus creating the need for a new way of manag-ing the way the produced traffic travels through data networks. Smart Cities, Vehicular Content Networks, Healthcare and Virtual Reality Videos are a few examples that require high volume data while maintaining low latency. Edge caching practices are a prom-ising solution in such cases in order meet the requirements of low latency in high volume traffic. This paper is a survey on four indic-ative areas, Smart Cities, Vehicular Content Networks, Healthcare and Virtual Reality Videos that make use of edge caching.
Near-Field Radiation Exposure Control in Slot-Loaded Microstrip Antenna: A Characteristic Mode Approach

Microstip antenna topology is commonly loaded with a narrow slot to manipulate the resonance frequency or impedance bandwidth. However, the tuning of the resonance frequency or impedance bandwidth results in the variation of the current and field distributions. In this regard, this work adopts the concept of characteristic modes to gain an initial understanding of the perturbation mechanism of the rectangular patch when loaded with a slot. The performance of microstrip antennas with finite ground plane is then studied using full-wave simulation. It has been found that the distribution of the induced current density is highly dependent on the orientation of the slot The incorporation of a narrow slot suppresses the nearby orthogonal eigen mode and, as a consequence, the radiation behavior is affected. Specifically, in the presence of biological tissues in the near-field region, both antenna input impedance properties and the realized gain are dependent on the slot orientation. Different examples are included for understanding the impact of slot loading on the energy absorption by biological tissues, by calculating the the specific absorption rate (SAR). The proposed analysis facilitates the design of miniaturized antenna geometries for biomedical applications via systematic loading of narrow slots.
Learning Body Shape and Pose from Dense Correspondences

In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose). The idea is to use dense correspondences between image points and a body surface, which can be annotated on in-the wild 2D images, and extract and aggregate 3D information from them. To do so, we propose a training strategy called ``deform-and-learn" where we alternate deformable surface registration and training of deep convolutional neural networks (ConvNets). Unlike previous approaches, our method does not require 3D pose annotations from a motion capture (MoCap) system or human intervention to validate 3D pose annotations.
Computing the Value of Data: Towards Applied Data Minimalism

We present an approach to compute the monetary value of individual data points, in context of an automated decision system. The proposed method enables us to explore and implement a paradigm of data minimalism for large-scale machine learning systems. Data minimalistic implementations enhance scalability, while maintaining or even optimizing a system's performance. Using two types of recommender systems, we first demonstrate how much data is ineffective in both settings. We then present a general account of computing data value via sensitivity analysis, and how, in theory, individual data points can be priced according to their informational contribution to automated decisions. We further exemplify this method to lab-scale recommender systems and outline further steps towards commercial data-minimalistic applications.
CAiRE: An End-to-End Empathetic Chatbot

In this paper, we present an end-to-end empathetic conversation agent CAiRE. Our system adapts TransferTransfo (Wolf et al., 2019) learning approach that fine-tunes a large-scale pre-trained language model with multi-task objectives: response language modeling, response prediction and dialogue emotion detection. We evaluate our model on the recently proposed empathetic-dialogues dataset (Rashkin et al., 2019), the experiment results show that CAiRE achieves state-of-the-art performance on dialogue emotion detection and empathetic response generation.
Rotating ionized gas ring around the Galactic center IRS13E3

We detected a compact ionized gas associated physically with IRS13E3, an intermediate mass black hole (IMBH) candidate in the Galactic center, in the continuum emission at 232 GHz and H30α recombination line using ALMA Cy.5 observation (2017.1.00503.S, P.I. M.Tsuboi). The continuum emission image shows that IRS13E3 is surrounded by an oval-like structure. The angular size is 0${^{\prime\prime}_{. }}$093 ± 0${^{\prime\prime}_{. }}$006 × 0${^{\prime\prime}_{. }}$061 ± 0${^{\prime\prime}_{. }}$004 (1.14 × 1016 cm × 0.74 × 1016 cm). The structure is also identified in the H30α recombination line. This is seen as an inclined linear feature in the position–velocity diagram, which is usually a defining characteristic of a rotating gas ring around a large mass. The gas ring has a rotating velocity of Vrot ≃ 230 km s−1 and an orbit radius of r ≃ 6 × 1015 cm. From these orbit parameters, the enclosed mass is estimated to be $M_{\mathrm{IMBH}}\simeq 2.4\times 10^{4}\, M_{\odot }$. The mass is within the astrometric upper limit mass of the object adjacent to Sgr A*. Considering IRS13E3 has an X-ray counterpart, the large enclosed mass would be supporting evidence that IRS13E3 is an IMBH. Even if a dense cluster corresponds to IRS13E3, the cluster would collapse into an IMBH within τ < 107 yr due to the very high mass density of $\rho \gtrsim 8\times 10^{11}\, M_{\odot }\:$pc−3. Because the orbital period is estimated to be as short as T = 2πr/Vrot ∼ 50–100 yr, the morphology of the observed ionized gas ring is expected to be changed in the next several decades. The mean electron temperature and density of the ionized gas are $\bar{T}_{\mathrm{e}}=6800\pm 700\:$K and $\bar{n}_{\mathrm{e}}=6\times 10^{5}\:$cm−3, respectively. Then the mass of the ionized gas is estimated to be $M_{\mathrm{gas}}=4\times 10^{-4}\, M_{\odot }$.
New physics from COHERENT data with an improved quenching factor

A recent new measurement and re-analysis of past measurements suggested an improved quenching factor value and uncertainty for CsI[Na]. This implies a measurement of the COHERENT experiment of coherent elastic neutrino-nucleus scattering that is closer to the Standard Model prediction and has less uncertainty. We illustrate the impact of this improvement by revisiting fits to the Weinberg angle, neutrino magnetic moments, neutron rms and neutrino charge radii, neutrino non-standard interactions (in particular those relevant for LMA-Dark) and new scalar as well as vector bosons. Significant improvement is observed, particularly for those scenarios coherently affecting the electroweak SM process.
A Higher-Order Swiss Army Infinitesimal Jackknife

Cross validation (CV) and the bootstrap are ubiquitous model-agnostic tools for assessing the error or variability of machine learning and statistical estimators. However, these methods require repeatedly re-fitting the model with different weighted versions of the original dataset, which can be prohibitively time-consuming. For sufficiently regular optimization problems the optimum depends smoothly on the data weights, and so the process of repeatedly re-fitting can be approximated with a Taylor series that can be often evaluated relatively quickly. The first-order approximation is known as the "infinitesimal jackknife" in the statistics literature and has been the subject of recent interest in machine learning for approximate CV. In this work, we consider high-order approximations, which we call the "higher-order infinitesimal jackknife" (HOIJ). Under mild regularity conditions, we provide a simple recursive procedure to compute approximations of all orders with finite-sample accuracy bounds. Additionally, we show that the HOIJ can be efficiently computed even in high dimensions using forward-mode automatic differentiation. We show that a linear approximation with bootstrap weights approximation is equivalent to those provided by asymptotic normal approximations. Consequently, the HOIJ opens up the possibility of enjoying higher-order accuracy properties of the bootstrap using local approximations. Consistency of the HOIJ for leave-one-out CV under different asymptotic regimes follows as corollaries from our finite-sample bounds under additional regularity assumptions. The generality of the computation and bounds motivate the name "higher-order Swiss Army infinitesimal jackknife. "
Multi-Granularity Fusion Network for Proposal and Activity Localization: Submission to ActivityNet Challenge 2019 Task 1 and Task 2

This technical report presents an overview of our solution used in the submission to ActivityNet Challenge 2019 Task 1 (\textbf{temporal action proposal generation}) and Task 2 (\textbf{temporal action localization/detection}). Temporal action proposal indicates the temporal intervals containing the actions and plays an important role in temporal action localization. Top-down and bottom-up methods are the two main categories used for proposal generation in the existing literature. In this paper, we devise a novel Multi-Granularity Fusion Network (MGFN) to combine the proposals generated from different frameworks for complementary filtering and confidence re-ranking. Specifically, we consider the diversity comprehensively from multiple perspectives, e.g. the characteristic aspect, the data aspect, the model aspect and the result aspect. Our MGFN achieves the state-of-the-art performance on the temporal action proposal task with 69.85 AUC score and the temporal action localization task with 38.90 mAP on the challenge testing set.