Miracle-dz/UniFAR-FTUs huggingface dataset:
Random walk in a birth-and-death dynamical environment

We consider a particle moving in continuous time as a Markov jump process; its discrete chain is given by an ordinary random walk on ${\mathbb Z}^d$ , and its jump rate at $({\mathbf x},t)$ is given by a fixed function $\varphi$ of the state of a birth-and-death (BD) process at $\\mathbf x$ on time $t$; BD processes at different sites are independent and identically distributed, and $\varphi$ is assumed non increasing and vanishing at infinity. We derive a LLN and a CLT for the particle position when the environment is 'strongly ergodic'. In the absence of a viable uniform lower bound for the jump rate, we resort instead to stochastic domination, as well as to a subadditive argument to control the time spent by the particle to give $n$ jumps; and we also impose conditions on the initial (product) environmental initial distribution. We also present results on the asymptotics of the environment seen by the particle (under different conditions on $\varphi$).
Fully Bayesian inference for latent variable Gaussian process models

Real engineering and scientific applications often involve one or more qualitative inputs. Standard Gaussian processes (GPs), however, cannot directly accommodate qualitative inputs. The recently introduced latent variable Gaussian process (LVGP) overcomes this issue by first mapping each qualitative factor to underlying latent variables (LVs), and then uses any standard GP covariance function over these LVs. The LVs are estimated similarly to the other GP hyperparameters through maximum likelihood estimation, and then plugged into the prediction expressions. However, this plug-in approach will not account for uncertainty in estimation of the LVs, which can be significant especially with limited training data. In this work, we develop a fully Bayesian approach for the LVGP model and for visualizing the effects of the qualitative inputs via their LVs. We also develop approximations for scaling up LVGPs and fully Bayesian inference for the LVGP hyperparameters. We conduct numerical studies comparing plug-in inference against fully Bayesian inference over a few engineering models and material design applications. In contrast to previous studies on standard GP modeling that have largely concluded that a fully Bayesian treatment offers limited improvements, our results show that for LVGP modeling it offers significant improvements in prediction accuracy and uncertainty quantification over the plug-in approach.
Two-dimensional spin systems in PECVD-grown diamond with tunable density and long coherence for enhanced quantum sensing and simulation

Systems of spins engineered with tunable density and reduced dimensionality enable a number of advancements in quantum sensing and simulation. Defects in diamond, such as nitrogen-vacancy (NV) centers and substitutional nitrogen (P1 centers), are particularly promising solid-state platforms to explore. However, the ability to controllably create coherent, two-dimensional spin systems and characterize their properties, such as density, depth confinement, and coherence, is an outstanding materials challenge. We present a refined approach to engineer dense (≳1 ppm ⋅ nm), 2D nitrogen, and NV layers in diamond using delta-doping during plasma-enhanced chemical vapor deposition epitaxial growth. We employ both traditional materials techniques, e.g., secondary ion mass spectrometry, alongside NV spin decoherence-based measurements to characterize the density and dimensionality of the P1 and NV layers. We find P1 densities of 5–10 ppm ⋅ nm, NV densities between 1 and 3.5 ppm ⋅ nm tuned via electron irradiation dosage, and depth confinement of the spin layer down to 1.6 nm. We also observe high (up to 0.74) ratios of NV to P1 centers and reproducibly long NV coherence times, dominated by dipolar interactions with the engineered P1 and NV spin baths.
An approach for benchmarking the numerical solutions of stochastic compartmental models

An approach is introduced for comparing the estimated states of stochastic compartmental models for an epidemic or biological process with analytically obtained solutions from the corresponding system of ordinary differential equations (ODEs). Positive integer valued samples from a stochastic model are generated numerically at discrete time intervals using either the Reed-Frost chain Binomial or Gillespie algorithm. The simulated distribution of realisations is compared with an exact solution obtained analytically from the ODE model. Using this novel methodology this work demonstrates it is feasible to check that the realisations from the stochastic compartmental model adhere to the ODE model they represent. There is no requirement for the model to be in any particular state or limit. These techniques are developed using the stochastic compartmental model for a susceptible-infected-recovered (SIR) epidemic process. The Lotka-Volterra model is then used as an example of the generality of the principles developed here. This approach presents a way of testing/benchmarking the numerical solutions of stochastic compartmental models, e.g. using unit tests, to check that the computer code along with its corresponding algorithm adheres to the underlying ODE model.
Spatially Selective Deep Non-Linear Filters For Speaker Extraction

In a scenario with multiple persons talking simultaneously, the spatial characteristics of the signals are the most distinct feature for extracting the target signal. In this work, we develop a deep joint spatial-spectral non-linear filter that can be steered to an arbitrary target direction. For this we propose a simple and effective conditioning mechanism, which sets the initial state of the filter’s recurrent layers based on the target direction. We show that this scheme is more effective than the baseline approach and increases the flexibility of the filter at no performance cost. The resulting spatially selective non-linear filters can also be used for speech separation of an arbitrary number of speakers and enable very accurate multi-speaker localization as we demonstrate in this paper.
A New Family of q-analogue of Genocchi Numbers and Polynomials of Higher Order

In the present paper, we introduce the new generalization of q-Genocchi polynomials and numbers of higher order. Also, we give some interesting identi- ties. Finally, by applying q-Mellin transformation to the generating function for q-Genocchi polynomials of higher order put we define novel q-Hurwitz-Zeta type function which is an interpolation for this polynomials at negative integers.
Inexact Proximal-Gradient Methods with Support Identification

We consider the proximal-gradient method for minimizing an objective function that is the sum of a smooth function and a non-smooth convex function. A feature that distinguishes our work from most in the literature is that we assume that the associated proximal operator does not admit a closed-form solution. To address this challenge, we study two adaptive and implementable termination conditions that dictate how accurately the proximal-gradient subproblem is solved. We prove that the number of iterations required for the inexact proximal-gradient method to reach a $\tau>0$ approximate first-order stationary point is $\mathcal{O}(\tau^{-2})$, which matches the similar result that holds when exact subproblem solutions are computed. Also, by focusing on the overlapping group $\ell_1$ regularizer, we propose an algorithm for approximately solving the proximal-gradient subproblem, and then prove that its iterates identify (asymptotically) the support of an optimal solution. If one imposes additional control over the accuracy to which each subproblem is solved, we give an upper bound on the maximum number of iterations before the support of an optimal solution is obtained.
Real-Time Target Sound Extraction

We present the first neural network model to achieve real-time and streaming target sound extraction. To accomplish this, we propose Waveformer, an encoder-decoder architecture with a stack of dilated causal convolution layers as the encoder, and a transformer decoder layer as the decoder. This hybrid architecture uses dilated causal convolutions for processing large receptive fields in a computationally efficient manner, while also leveraging the generalization performance of transformer-based architectures. Our evaluations show as much as 2.2–3.3 dB improvement in SI-SNRi compared to the prior models for this task while having a 1.2–4x smaller model size and a 1.5–2x lower runtime. We provide code, dataset, and audio samples: https://waveformer.cs. washington.edu/.
Extended models of gravity in SNIa cosmological data using genetic algorithms

In this talk I explained briefly the advantages of using genetic algorithms on any measured data but specially astronomical ones. This kind of algorithms are not only a better computational paradigm, but they also allow for a more profound data treatment enhancing theoretical developments. As an example, I will use the SNIa cosmological data to fit the extended metric theories of gravity of Carranza et al. (2013, 2014) showing that the best parameters combination deviate from theoretical predicted ones by a minimal amount. This means that these kind of gravitational extensions are statistically robust and show that no dark matter and/or energy is required to explain the observations.
Can RBMs be trained with zero step contrastive divergence?

Restricted Boltzmann Machines (RBMs) are probabilistic generative models that can be trained by maximum likelihood in principle, but are usually trained by an approximate algorithm called Contrastive Divergence (CD) in practice. In general, a CD-k algorithm estimates an average with respect to the model distribution using a sample obtained from a k-step Markov Chain Monte Carlo Algorithm (e.g., block Gibbs sampling) starting from some initial configuration. Choices of k typically vary from 1 to 100. This technical report explores if it's possible to leverage a simple approximate sampling algorithm with a modified version of CD in order to train an RBM with k=0. As usual, the method is illustrated on MNIST.
The norm of time-frequency and wavelet localization operators

Time-frequency localization operators (with Gaussian window) $L_F:L^2(\mathbb{R}^d)\to L^2(\mathbb{R}^d)$, where $F$ is a weight in $\mathbb{R}^{2d}$, were introduced in signal processing by I. Daubechies in 1988, inaugurating a new, geometric, phase-space perspective. Sharp upper bounds for the norm (and the singular values) of such operators turn out to be a challenging issue with deep applications in signal recovery, quantum physics and the study of uncertainty principles. In this note we provide optimal upper bounds for the operator norm $\|L_F\|_{L^2\to L^2}$, assuming $F\in L^p(\mathbb{R}^{2d})$, $1<p<\infty$ or $F\in L^p(\mathbb{R}^{2d})\cap L^\infty(\mathbb{R}^{2d})$, $1\leq p<\infty$. It turns out that two regimes arise, depending on whether the quantity $\|F\|_{L^p}/\|F\|_{L^\infty}$ is less or greater than a certain critical value. In the first regime the extremal weights $F$, for which equality occurs in the estimates, are certain Gaussians, whereas in the second regime they are proved to be truncated Gaussians, degenerating in a multiple of a characteristic function of a ball for $p=1$. This phase transition through truncated Gaussians appears to be a new phenomenon in time-frequency concentration problems. For the analogous problem for wavelet localization operators -- where the Cauchy wavelet plays the role of the above Gaussian window -- a complete solution is also provided.
Hilbert space fragmentation and slow dynamics in particle-conserving quantum East models

Quantum kinetically constrained models have recently attracted significant attention due to their anomalous dynamics and thermalization. In this work, we introduce a hitherto unexplored family of kinetically constrained models featuring conserved particle number and strong inversion-symmetry breaking due to facilitated hopping. We demonstrate that these models provide a generic example of so-called quantum Hilbert space fragmentation, that is manifested in disconnected sectors in the Hilbert space that are not apparent in the computational basis. Quantum Hilbert space fragmentation leads to an exponential in system size number of eigenstates with exactly zero entanglement entropy across several bipartite cuts. These eigenstates can be probed dynamically using quenches from simple initial product states. In addition, we study the particle spreading under unitary dynamics launched from the domain wall state, and find faster than diffusive dynamics at high particle densities, that crosses over into logarithmically slow relaxation at smaller densities. Using a classically simulable cellular automaton, we reproduce the logarithmic dynamics observed in the quantum case. Our work suggests that particle conserving constrained models with inversion symmetry breaking realize so far unexplored dynamical behavior and invite their further theoretical and experimental studies.
Patch DCT vs LeNet

This paper compares the performance of a NN taking the output of a DCT (Discrete Cosine Transform) of an image patch with leNet for classifying MNIST hand written digits. The basis functions underlying the DCT bear a passing resemblance to some of the learned basis function of the Visual Transformer but are an order of magnitude faster to apply.
Context-Dependent Function of Long Noncoding RNA PURPL in Transcriptome Regulation during p53 Activation

PURPL is a p53-induced lncRNA that suppresses basal p53 levels. Here, we investigated PURPL upon p53 activation in liver cancer cells, where it is expressed at significantly higher levels than other cell types. ABSTRACT PURPL is a p53-induced lncRNA that suppresses basal p53 levels. Here, we investigated PURPL upon p53 activation in liver cancer cells, where it is expressed at significantly higher levels than other cell types. Using isoform sequencing, we discovered novel PURPL transcripts that have a retained intron and/or previously unannotated exons. To determine PURPL function upon p53 activation, we performed transcriptome sequencing (RNA-Seq) after depleting PURPL using CRISPR interference (CRISPRi), followed by Nutlin treatment to induce p53. Strikingly, although loss of PURPL in untreated cells altered the expression of only 7 genes, loss of PURPL resulted in altered expression of ~800 genes upon p53 activation, revealing a context-dependent function of PURPL. Pathway analysis suggested that PURPL is important for fine-tuning the expression of specific genes required for mitosis. Consistent with these results, we observed a significant decrease in the percentage of mitotic cells upon PURPL depletion. Collectively, these data identify novel transcripts from the PURPL locus and suggest that PURPL delicately moderates the expression of mitotic genes in the context of p53 activation to control cell cycle arrest.
Laplacians on quantum hypergraphs

We introduce quantum hypergraphs, in analogy with the theory of quantum graphs developed over the last 15 years by many authors. We emphasize some problems that arise when one tries to define a Laplacian on a hypergraph. (© 2014 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)
The Muckenhoupt $A_{\infty}$ class as a metric space and continuity of weighted estimates

We show how the A∞ class of weights can be considered as a metric space. As far as we know this is the first time that a metric d∗ is considered on this set. We use this metric to generalize the results obtained in [8]. Namely, we show that for any Calderon–Zygmund operator T and an Ap, 1 < p < ∞, weight w0, the numbers ‖T‖Lp(w)→Lp(w) converge to ‖T‖Lp(w0)→Lp(w0) as d∗(w,w0) → 0. We also find the rate of this convergence and prove that it is sharp.
Two Schemes for Fractional Diffusion and Diffusion-Wave Equations with Nonsmooth Data

We consider the initial/boundary value problem for the fractional diffusion and diffusion-wave equations involving a Caputo fractional derivative in time. We develop two "simple" fully discrete schemes based on the Galerkin finite element method in space and convolution quadrature in time with the generating function given by the implicit backward Euler method/second-order backward difference method, and establish error estimates optimal with respect to the regularity of the initial data. These two schemes are first and second-order accurate in time for nonsmooth initial data. Extensive numerical experiments for one and two-dimensional problems confirm the convergence analysis. A detailed comparison with several popular time stepping schemes is also performed. The numerical results indicate that the proposed fully discrete schemes are accurate and robust for nonsmooth data, and competitive with existing schemes.
Compact structures in standard field theory

We investigate the presence of static solutions in models described by real scalar field in two-dimensional spacetime. After taking advantage of a procedure introduced sometime ago, we solve intricate nonlinear ordinary differential equations and illustrate how to find compact structures in models engendering standard kinematics. In particular, we study linear stability and show that all the static solutions we have found are linearly stable.
Poster: TaintGrep: A Static Analysis Tool for Detecting Vulnerabilities of Android Apps Supporting User-defined Rules

In this poster, we present TaintGrep, a novel static analysis approach to detect vulnerabilities of Android applications. This approach combines the advantages of semantic pattern matching and taint analysis to get better accuracy and be able to detect cross-function vulnerabilities. Compared with many traditional tools, TaintGrep does not require the full source code or building environment to analyze. Moreover, it supports users in defining their customized matching rules using their vulnerability mining experience, which makes this approach more flexible and scalable. In the preliminary experiment, we give a detailed analysis of the rules of two typical vulnerabilities: generic DoS and arbitrary file read/write, and have detected 77 0day vulnerabilities with these rules in 16 well-known Android applications.
Practical Volume-Hiding Encrypted Multi-Maps with Optimal Overhead and Beyond

Encrypted multi-map (EMM), as a special case of structured encryption, has attracted extensive attention recently. However, most of EMM constructions reveal the real volumes of queried keys, which can be leveraged to launch leakage-abuse attacks, as demonstrated by Kellaris et al. in CCS 2016 and Kornaropoulos et al. in S&P 2021. In this paper, we propose a practical non-lossy volume-hiding EMM scheme, XorMM, that can achieve optimal query communication complexity with minimal storage cost. Specifically, compared to the state-of-the-art dprfMM (Patel et al. CCS 2019), the client in our scheme receives only ℓ matching results while not suffering from data loss, where ℓ is the maximum volume of all keys. In addition, the storage cost of XorMM is approximately 1.23n, where n is the total number of key/value pairs. In contrast, the query communication and storage complexity of dprfMM is 2ℓ and 2(1+α)n respectively, where 0<α<1 is a small constant. Furthermore, we initiate the study of volume-hiding EMM against malicious servers. To the best of our knowledge, we present the first verifiable volume-hiding EMM scheme, XorMM, from merely symmetric cryptographic tools. The scheme still outperforms dprfMM while supporting verifiability, the query complexity and storage overhead of which are approximately ℓ +1 and 2.46n, respectively. Finally, we implement our proposed schemes and compare them with the most efficient scheme dprfMM (Patel et al. CCS 2019). The experimental results demonstrate that both of our schemes are superior to the state-of-the-art in both search and storage cost. In particular, XorMM (resp. VXorMM) brings a saving of 76% (resp. 52%) in server storage cost and achieves a speedup of 1.8x (resp. 1.6x) in search latency.
We Are the Experts, and We Are the Problem: The Security Advice Fiasco

In an ideal world, automated tools and systems could manage security and privacy seamlessly and transparently with minimal human input. In the real world, we are nowhere close to that ideal. Instead, in order to achieve good security and privacy outcomes, people need to absorb and apply high-quality security and privacy information and advice. This applies not only to end users, but also to software developers, product managers, and even security operations professionals. Sadly, the current state of the security advice and information ecosystem is in many respects a disaster. End users often get their advice from TV shows, movies, and even misleading influencer ads [2, 4], while soft ware developers take unvetted suggestions from Stack Overflow [1, 3]. Even compliance standards -- which are designed to provide authoritative security guidance -- have numerous problems [6, 7]. Our review of security advice on the web found 374 unique advice imperatives, many of which directly contradict one another [5]. This sad state of affairs is, in many ways, our fault. Security experts, like the ones who attend conferences such as CCS, often refuse to prioritize, recommending maximum security without tailoring to specific situations. Researchers evaluate tools and techniques in idealized rather than realistic use contexts, and have made little progress in accurately measuring the costs and benefits of any particular intervention. In this talk, I will review the many problems of the security and privacy information and advice ecosystem, and how we got here. I'll outline our responsibility, as experts and researchers, to help improve the quality, availability, and usability of security and privacy information. Finally, I'll discuss at what we know (and what we need to find out) about how to make progress.
Non-local PDEs with discrete state-dependent delays: well-posedness in a metric space

Partial differential equations with discrete (concentrated) state-dependent delays are studied. The existence and uniqueness of solutions with initial data from a wider linear space is proven first and then a subset of the space of continuously differentiable (with respect to an appropriate norm) functions is used to construct a dynamical system. This subset is an analogue of \textit{the solution manifold} proposed for ordinary equations in [H. -O. Walther, The solution manifold and $C\sp 1$-smoothness for differential equations with state-dependent delay, J. Differential Equations, {195}(1), (2003) 46--65]. The existence of a compact global attractor is proven.