Miracle-dz/UniFAR-FTUs huggingface dataset:
Pychastic: Precise Brownian dynamics using Taylor-Itō integrators in Python

In the last decade, Python-powered physics simulations ecosystem has been growing steadily, allowing greater interoperability, and becoming an important tool in numerical exploration of physical phenomena, particularly in soft matter systems. Driven by the need for fast and precise numerical integration in colloidal dynamics, here we formulate the problem of Brownian Dynamics (BD) in a mathematically consistent formalism of the Itō calculus, and develop a Python package to assist numerical computations. We show that, thanks to the automatic differentiation packages, the classical truncated Taylor-Itō integrators can be implemented without the burden of computing the derivatives of the coefficient functions beforehand. Furthermore, we show how to circumvent the difficulties of BD simulations such as calculations of the divergence of the mobility tensor in the diffusion equation and discontinuous trajectories encountered when working with dynamics on S^2S2 and SO(3)SO(3). The resulting Python package, Pychastic, is capable of performing BD simulations including hydrodynamic interactions at speeds comparable to dedicated implementations in lower-level programming languages, but with a much simpler end-user interface.
F-coref: Fast, Accurate and Easy to Use Coreference Resolution

We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-coref, which is the focus of this work. F-coref allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy. The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover batching. https://github.com/shon-otmazgin/fastcoref
A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance

Atmospheric powered descent guidance (APDG) can be solved by successive convexification; however, its onboard application is impeded by high computational cost. When aerodynamic forces are ignored, powered descent guidance (PDG) can be converted to a single convex problem. In contrast, APDG has to be converted into a sequence of convex subproblems, each of which is significantly more complicated. Consequently, the computation increases sharply. A fast real-time interior point method was presented to solve the correlated convex subproblems efficiently onboard in the work. The main contributions are as follows: First, an algorithm was proposed to accelerate the solution of linear systems that cost most of the computation in each iterative step by exploiting the specific problem structure. Second, a warm-starting scheme was introduced to refine the initial value of a subproblem with a rough approximate solution of the former subproblem, which lessened the iterative steps required for each subproblem. The method proposed reduced the run time by a factor of 9 compared with the fastest publicly available solver tested in Monte Carlo simulations to evaluate the efficiency of solvers. Runtimes on the order of 0.6 s are achieved on a radiation-hardened flight processor, which demonstrated the potential of the real-time onboard application.
Algorithms with More Granular Differential Privacy Guarantees

Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parameters have been proposed. In this work, we consider partial differential privacy (DP), which allows quantifying the privacy guarantee on a per-attribute basis. In this framework, we study several basic data analysis and learning tasks, and design algorithms whose per-attribute privacy parameter is smaller that the best possible privacy parameter for the entire record of a person (i.e., all the attributes).
Fair Matrix Factorisation for Large-Scale Recommender Systems

Recommender systems are hedged with various requirements, such as ranking quality, optimisation efficiency, and item fairness. Item fairness is an emerging yet impending issue in practical systems. The notion of item fairness requires controlling the opportunity of items (e.g. the exposure) by considering the entire set of rankings recommended for users. However, the intrinsic nature of fairness destroys the separability of optimisation subproblems for users and items, which is an essential property of conventional scalable algorithms, such as implicit alternating least squares (iALS). Few fairness-aware methods are thus available for large-scale item recommendation. Because of the paucity of simple tools for practition-ers, unfairness issues would be costly to solve or, at worst, would be abandoned. This study takes a step towards solving real-world unfairness issues by developing a simple and scalable collaborative filtering method for fairness-aware item recommendation. We built a method named fiADMM , which inherits the scalability of iALS and maintains a provable convergence guarantee.
Dirichlet problems for second order linear elliptic equations with $ L^{1} $-data

We consider the Dirichlet problems for second order linear elliptic equations in non-divergence and divergence forms on a bounded domain $\Omega$ in $\mathbb{R}^n$, $n \ge 2$: $$ -\sum_{i,j=1}^n a^{ij}D_{ij} u + b \cdot D u + cu = f \;\;\text{ in $\Omega$} \quad \text{and} \quad u=0 \;\;\text{ on $\partial \Omega$} $$ and $$ - {\rm div} \left( A D u \right) + {\rm div}(ub) + cu = {\rm div} F \;\;\text{ in $\Omega$} \quad \text{and} \quad u=0 \;\;\text{ on $\partial \Omega$} , $$ where $A=[a^{ij}]$ is symmetric, uniformly elliptic, and of vanishing mean oscillation (VMO). The main purposes of this paper is to study unique solvability for both problems with $L^1$-data. We prove that if $\Omega$ is of class $C^{1}$, $ {\rm div} A + b\in L^{n,1}(\Omega;\mathbb{R}^n)$, $c\in L^{\frac{n}{2},1}(\Omega) \cap L^s(\Omega)$ for some $1<s<\frac{3}{2}$, and $c\ge0$ in $\Omega$, then for each $f\in L^1 (\Omega )$, there exists a unique weak solution in $W^{1,\frac{n}{n-1},\infty}_0 (\Omega)$ of the first problem. Moreover, under the additional condition that $\Omega$ is of class $C^{1,1}$ and $c\in L^{n,1}(\Omega)$, we show that for each $F \in L^1 (\Omega ; \mathbb{R}^n)$, the second problem has a unique very weak solution in $L^{\frac{n}{n-1},\infty}(\Omega)$.
Sensing Using Coded Communications Signals

A key challenge for common waveforms for Integrated Sensing and Communications – widely regarded as a resource-efficient way to achieve high performance for both functionalities – lies in leveraging information-bearing channel-coded communications signal(s) (c.c.s) for sensing. In this paper, we investigate the range-Doppler sensing performance of c.c. s in multi-user interference-limited scenarios, and show that it is affected by sidelobes whose form depends on whether the c.c. s modulates a single-carrier or OFDM waveform. While uncoded signals give rise to asymptotically zero sidelobes due to the law of large numbers, it is not obvious that the same holds for c.c. s, as structured codes (e.g., linear block codes) induce dependence across codeword symbols. In this paper, we show that c.c. s also give rise to asymptotically zero sidelobes – for both single-carrier and OFDM waveforms – by deriving upper bounds for the tail probabilities of the sidelobe magnitudes that decay as $\exp ( - O(\text {code rate} \times \text {block length}))$ . Consequently, for any code rate, c.c. s are effective sensing signals that are robust to multi-user interference at sufficiently large block lengths, with negligible difference in performance based on whether they modulate a single-carrier or OFDM waveform. We verify the latter implication through simulations, where we observe the sensing performance (i.e., the detection and false-alarm probabilities) of a QPSK-modulated c.c. s (code rate = 120/1024, block length = 1024 symbols) to match that of a comparable interference-free FMCW waveform even at high interference levels (SIR of −11dB), for both single-carrier and OFDM waveforms.
Asymmetrical contact scaling and measurements in MoS2 FETs

Two-dimensional (2D) materials have great potential for use in future electronics due to their atomically thin nature, which withstands short channel effects and thus enables better scalability. Device scaling is the process of reducing all device dimensions to achieve higher device density in a certain chip area. For 2D materials-based transistors, both the channel and contact scalability must be investigated. The channel scalability of 2D materials has been thoroughly investigated, confirming their resilience to short-channel effects. However, systematic studies on contact scalability remain rare, and the current understanding of contact scaling in 2D FET is inconsistent and oversimplified. Here we combine physically scaled contacts and asymmetrical contact measurements to investigate the contact scaling behavior in 2D field-effect transistors (FETs). The asymmetrical contact measurements directly compare electron injection with different contact lengths while using the exact same channel, eliminating channel-to-channel variations. Compared to devices with long contact lengths, devices with short contact lengths (scaled contacts) exhibit larger variations, 15% lower drain currents at high drain-source voltages, and a higher chance of showing early saturation and negative differential resistance. Quantum transport simulations show that the transfer length of Ni-MoS 2 contacts can be as short as 5 nm. Our results suggest that charge injection at the source contact differs from injection at the drain side: scaled source contacts can limit the drain current, whereas scaled drain contacts cannot. Furthermore, we clearly identified that the transfer length depends on the quality of the metal-2D interface. The asymmetrical contact measurements demonstrated here will enable further understanding of contact scaling behavior at various interfaces. protect the sample surface, followed by a 2 μ m Pt deposition with ion beam. The STEM samples were thinned with 30 kV Ga-ions beam while final thinning was performed at 2 kV to reduce damage. The Z-contrast HAADF-STEM images were collected using an FEI Titan 80-300 probe-corrected STEM/TEM microscope operating at 300 keV, with a beam convergence angle of 20 mrad and collection angles > 50 mrad.
Non-autoregressive Error Correction for CTC-based ASR with Phone-conditioned Masked LM

Connectionist temporal classification (CTC) -based models are attractive in automatic speech recognition (ASR) because of their non-autoregressive nature. To take advantage of text-only data, language model (LM) integration approaches such as rescoring and shallow fusion have been widely used for CTC. However, they lose CTC's non-autoregressive nature because of the need for beam search, which slows down the inference speed. In this study, we propose an error correction method with phone-conditioned masked LM (PC-MLM). In the proposed method, less confident word tokens in a greedy decoded output from CTC are masked. PC-MLM then predicts these masked word tokens given unmasked words and phones supplementally predicted from CTC. We further extend it to Deletable PC-MLM in order to address insertion errors. Since both CTC and PC-MLM are non-autoregressive models, the method enables fast LM integration. Experimental evaluations on the Corpus of Spontaneous Japanese (CSJ) and TED-LIUM2 in domain adaptation setting shows that our proposed method outperformed rescoring and shallow fusion in terms of inference speed, and also in terms of recognition accuracy on CSJ.
Double detonations: variations in Type Ia supernovae due to different core and He shell masses -- II: synthetic observables

Double detonations of sub-Chandrasekhar mass white dwarfs are a promising explosion scenario for Type Ia supernovae, whereby a detonation in a surface helium shell triggers a secondary detonation in a carbon-oxygen core. Recent work has shown that low mass helium shell models reproduce observations of normal SNe Ia. We present 3D radiative transfer simulations for a suite of 3D simulations of the double detonation explosion scenario for a range of shell and core masses. We find light curves broadly able to reproduce the faint end of the width-luminosity relation shown by SNe Ia, however, we find that all of our models show extremely red colours, not observed in normal SNe Ia. This includes our lowest mass helium shell model. We find clear Ti II absorption features in the model spectra, which would lead to classification as peculiar SNe Ia, as well as line blanketing in some lines of sight by singly ionised Cr and Fe-peak elements. Our radiative transfer simulations show that these explosion models remain promising to explain peculiar SNe Ia. Future full non-LTE simulations may improve the agreement of these explosion models with observations of normal SNe Ia.
Constraining the growth rate of structure with redshift space distortions including the wide-angle effect

We introduce an improved method for constraining the growth rate of structure with the galaxy overdensity and peculiar velocity power spectrum. This method reduces the modelling systematic error by accounting for the wide-angle effect and the zero-point calibration uncertainty during the modelling process. We also speed up the posterior sampling by around 30 times by first calculating the likelihood at a small number of fiducial points and then interpolating the likelihood values during MCMC sampling. We test the new method on mocks and we find it is able to recover the fiducial growth rate of structure. We applied our new method to the SDSS PV catalogue, which is the largest single peculiar velocity catalogue to date. Our constraint on the growth rate of structure is f σ8 = 0.405+0.076 −0.071 (stat) ±0.009 (sys) at the effective redshift of 0.073. Our constraint is consistent with a Planck 2018 cosmological model, f σ8 = 0.448, within one standard deviation. Our improved methodology will enable similar analysis on future data, with even larger sample sizes and covering larger angular areas on the sky.
Distributed Complementary Fusion for Connected Vehicles

We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.
Emergent classical gauge symmetry from quantum entanglement

We describe explicitly how entanglement between quantum mechanical subsystems can lead to emergent gauge symmetry in a classical limit. We first provide a precise characterisation of when it is consistent to treat a quantum subsystem classically in such a limit, namely: in any quantum state corresponding to a definite classical state in the classical limit, the reduced density matrix of the subsystem must be approximately proportional to a projection operator, and the projection operators for different classical subsystem states must obey an approximate mutual orthogonality condition. These are strong constraints on the entanglement structure of classical states. They generically give rise to fundamentally non-local classical degrees of freedom, which may nevertheless be accounted for using a completely local kinematical description, if one gauges this description in the right way. The mechanism we describe is very general, but for concreteness we exhibit a toy example involving three entangled spins at high angular momentum, and we also describe a significant group-theoretic generalisation of this toy example. Finally, we give evidence that this phenomenon plays a role in the emergence of bulk diffeomorphism invariance in gravity.
The Binary Two-Up Sequence

The Binary Two-Up Sequence is the lexicographically earliest sequence of distinct nonnegative integers with the property that the binary expansion of the n -th term has no 1-bits in common with any of the previous ⌊ n 2 ⌋ terms. We show that the sequence can be decomposed into \atoms", which are sequences of 4, 6, or 8 numbers whose binary expansions match certain patterns, and that the sequence is the limiting form of a certain \word" involving the atoms. This leads to a fairly explicit formula for the terms, and in particular establishes the conjecture that every nonzero term is the sum of at most two powers of 2.
Analysis of the Power Imbalance in Power-Domain NOMA on Correlated Rayleigh Fading Channels

This paper analyzes the power imbalance issue in power-domain NOMA (PD-NOMA) in the presence of channel correlations, typically encountered on the downlink of cellular systems when the base station antennas have an insufficient separation. In a recent paper, the authors analyzed this issue for a typical uplink scenario with uncorrelated channels, and the study revealed an astounding result that the optimum in terms of average error probability is achieved when the user signals are perfectly balanced in terms of power as in multi-user MIMO with power control. This result led to some questioning of the concept of PD-NOMA for uncorrelated Rayleigh fading channels. In the present paper, we make a similar analysis for the downlink, and the study gives a very clear insight into the influence of the power imbalance at different levels of channel correlation. First, with full correlation (user signals transmitted from the same antenna), the PD-NOMA concept reduces to simple signal constellation design. The optimum is achieved when the power imbalance between the user signals is such that the resulting constellation has uniform spacing. Any deviation from this optimum will lead to a hierarchical constellation with performance loss. Also, this optimum power imbalance is shown to hold for a range of strong channel correlations, but for moderate and low correlation values perfectly power balanced NOMA takes over as in the presence of uncorrelated channels.
Injection seeding of a Ti:sapphire laser using a ramp-hold-fire technique

The capability of neodymium doped glass to store a high level of energy depends on the concentration of ions, which can be as high as 4-8 10,' ions/cm3. However it is known that dipole-dipole interaction between neighbors ions Nd3+ rise with population, implying a decrease of the lifetime emission of the lasing excited state 4F,iz.1 This increased spontaneous emission becomes an important mechanism loss when using a pumping with duration comparable to the lifetime. Furthermore high densities of inversion are associated to an additional loss mechanism that corresponds to the interaction between two neighboring excited 4F,i, ions. In this process, one ion is further excited to a higher level, while the other returns to the ground ~ t a t e . ~ , ~ Although the highly excited state can relax to 4F3,2 via nonradiative process, this Auger upconversion mechanism limits the energy storage capability of the media and should be taken into account when using laser or diode pumping. This is the motivation of this study. Basically, this loss depends quadratically on the population N,(4F3,,) and appears in the usual rates equations as a -yNf term. After the initial buildup of the excited state population NI, its relaxation follows the law:
ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference

Edge training of deep neural networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware approximate multipliers have shown their effectiveness in gaining resource efficiency in DNN inference accelerators; however, training with approximate multipliers is largely unexplored. To build resource-efficient accelerators with approximate multipliers supporting DNN training, a thorough evaluation of training convergence and accuracy for different DNN architectures and different approximate multipliers is needed. This article presents ApproxTrain, an open-source framework that allows fast evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires only a high-level description of a DNN architecture along with C/C++ functional models of the approximate multiplier. We improve the speed of the simulation at the multiplier level by using a novel LUT-based approximate floating-point (FP) multiplier simulator on GPU (AMSim). Additionally, a novel flow is presented to seamlessly convert C/C++ functional models of approximate FP multipliers into AMSim. ApproxTrain leverages CUDA and efficiently integrates AMSim into the TensorFlow library to overcome the absence of native hardware approximate multiplier in commercial GPUs. We use ApproxTrain to evaluate the convergence and accuracy performance of DNN training with approximate multipliers for three application domains: image classification, object detection, and neural machine translation. The evaluations demonstrate similar convergence behavior and negligible change in test accuracy compared to FP32 and Bfloat16 multipliers. Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than $2500\times $ faster. Based on highly optimized closed-source cuDNN/cuBLAS libraries with native hardware multipliers, the original TensorFlow is, on average, only $8\times $ faster than ApproxTrain.
Constellations on the Sphere With Efficient Encoding-Decoding for Noncoherent Communications

In this paper, we propose a new structured Grassmannian constellation for noncoherent communications over single-input multiple-output (SIMO) Rayleigh block-fading channels. The constellation, which we call Grass-Lattice, is based on a measure preserving mapping from the unit hypercube to the Grassmannian of lines. The constellation structure allows for on-the-fly symbol generation, low-complexity decoding, and simple bit-to-symbol Gray-like coding. Simulation results show that Grass-Lattice has symbol and bit error rate performance close to that of a numerically optimized unstructured constellation, and is more power efficient than other structured constellations proposed in the literature and a coherent pilot-based scheme.
Robustness of shape descriptors and dynamics of learning vector quantization

In this thesis the author deals with two important aspects of a computer-based pattern analysis system, viz., representation and classification. The representation involves extraction of characteristic features of the patterns and the classification deals with learning and discriminant analysis. The author investigates the performance of: (a) representation schemes of contour based shape patterns of objects present in digital images and (b) Learning Vector Quantization (LVQ) classifier in a proto-typical scenario. In the context of (a) more specifically the robustness of contour based shape descriptors to incomplete representations of objects is investigated. Using concepts of statistical physics and theory of on-line learning, the dynamics and the generalization ability of LVQ algorithms in a given data model are studied in the context of (b).