Miracle-dz/UniFAR-FTUs huggingface dataset:
Multifunctional High-Frequency Circuit Capabilities of Ambipolar Carbon Nanotube FETs

An experimentally-calibrated carbon nanotube compact transistor model has been used here to design two high-frequency (HF) circuits with two different functionalities each: a phase configurable amplifier (PCA) and a frequency configurable amplifier (FCA). The former design involves an in-phase amplifier and an inverting amplifier while the latter design embraces a frequency doubler as well as a distinct inverting amplifier. The specific functionality selection of each of the two HF circuit designs is enabled mainly by the inherent ambipolar feature at a device level. Furthermore, at a circuit level the matching networks are the same regardless the operation mode. In-phase and inverting amplification are enabled in the PCA by switching the gate-to-source voltage (<inline-formula><tex-math notation="LaTeX">$V_{\rm GS}$</tex-math></inline-formula>) from −0.3 V to 0.9 V while the drain-to-source voltage (<inline-formula><tex-math notation="LaTeX">$V_{\rm DS}$</tex-math></inline-formula>) remains at 3 V. By designing carefully the matching and stability networks, power gains of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>4.5 dB and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>6.7 dB at 2.4 GHz for the in-phase and inverting operation mode have been achieved, respectively. The FCA, in its frequency doubler operation mode, exhibits <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>20 dBc of fundamental-harmonic suppression at 2.4 GHz when an input signal at 1.2 GHz is considered. This frequency doubler functionality is enabled at <inline-formula><tex-math notation="LaTeX">$V_{\rm GS}={0.3} $</tex-math></inline-formula> V, whereas at <inline-formula><tex-math notation="LaTeX">$V_{\rm GS}={0.9}$</tex-math></inline-formula> V amplification of <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>4.5 dB is obtained while <inline-formula><tex-math notation="LaTeX">$V_{\rm DS}$</tex-math></inline-formula> remains at 3 V in both cases. In both configurable circuits the stabilization and matching networks are the same regardless the bias-chosen operation mode. The circuits performance degradation due to metallic tubes in the device channel is studied as well as the impact of non-ideal inductors in each design. PCA and FCA operation modes are further exploited in high-frequency modulators.
Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting

This paper explores the task of Difficulty-Controllable Question Generation (DCQG), which aims at generating questions with required difficulty levels. Previous research on this task mainly defines the difficulty of a question as whether it can be correctly answered by a Question Answering (QA) system, lacking interpretability and controllability. In our work, we redefine question difficulty as the number of inference steps required to answer it and argue that Question Generation (QG) systems should have stronger control over the logic of generated questions. To this end, we propose a novel framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain. A dataset is automatically constructed to facilitate the research, on which extensive experiments are conducted to test the performance of our method.
A DDR method for the Reissner-Mindlin plate bending problem on polygonal meshes

In this work we propose a discretisation method for the Reissner–Mindlin plate bending problem in primitive variables that supports general polygonal meshes and arbitrary order. The method is inspired by a two-dimensional discrete de Rham complex for which key commutation properties hold that enable the cancellation of the contribution to the error linked to the enforcement of the Kirchhoff constraint. Denoting by k ≥ 0 the polynomial degree for the discrete spaces and by h the meshsize, we derive for the proposed method an error estimate in hk+1 for general k , as well as a locking-free error estimate for the lowest-order case k = 0. The theoretical results are validated on a complete panel of numerical tests.
FNAS: Uncertainty-Aware Fast Neural Architecture Search

Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. In this paper, we propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We first introduce an uncertainty-aware critic (value function) in Proximal Policy Optimization (PPO) to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times. It is the first to introduce block-level weight sharing in RLbased NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show that a simply designed off-policy correction factor used in"replay buffer"in RL optimization can further reduce half of the searching time. Experiments on the Mobile Neural Architecture Search (MNAS) search space show the proposed Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS process by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour ->2,000 GPU x hour for MNAS), and guarantees better performance on various vision tasks.
Model-Free Adaptive Control Compensated with Disturbance

In this paper, we restudy how to modify the model-free adaptive control (MFAC) to reject the disturbance both in single-input single-output (SISO) systems and multiple-input multiple-output (MIMO) systems, with the aim to pave the way for the development of this interesting controller in the future. First of all, in order to accurately describe the nonlinear system model at each time, we compensate the equivalent dynamic linearization model (EDLM) with disturbance and prove it through the definition of differentiability and the Taylor series. Then based on modified EDLM, we redesign MFAC compensated with disturbance and firstly reanalyze the discrete-time nonlinear system through the closed-loop system equation at each time. This is all possible because some nonlinear system functions can be accurately described by the EDLM compensated with disturbance according to Taylor series. At last, several examples are given to verify the theorem.
Resolving the Berezinskii-Kosterlitz-Thouless transition in the two-dimensional XY model with tensor-network-based level spectroscopy

Berezinskii-Kosterlitz-Thouless transition of the classical XY model is re-investigated, combining the Tensor Network Renormalization (TNR) and the Level Spectroscopy method based on the finite-size scaling of the Conformal Field Theory. By systematically analyzing the spectrum of the transfer matrix of the systems of various moderate sizes which can be accurately handled with a finite bond dimension, we determine the critical point removing the logarithmic corrections. This improves the accuracy by an order of magnitude over previous studies including those utilizing TNR. Our analysis also gives a visualization of the celebrated Kosterlitz Renormalization Group flow based on the numerical data.
Measurements and Modeling of Atomic‐Scale Sidewall Roughness and Losses in Integrated Photonic Devices

Atomic‐level imperfections play an increasingly critical role in nanophotonic device performance. However, it remains challenging to accurately characterize the sidewall roughness with sub‐nanometer resolution and directly correlate this roughness with device performance. A method that allows to measure the sidewall roughness of waveguides made of any material (including dielectrics) using the high resolution of atomic force microscopy is developed. This method is illustrated by measuring state‐of‐the‐art photonic devices made of silicon nitride. The roughness of devices fabricated using both deep ultraviolet (DUV) photo‐lithography and electron‐beam lithography for two different etch processes is compared. To correlate roughness with device performance, a new Payne–Lacey Bending model is described, which adds a correction factor to the widely used Payne–Lacey model so that losses in resonators and waveguides with bends can be accurately predicted given the sidewall roughness, waveguide width and bending radii. Having a better way to measure roughness and use it to predict device performance can allow researchers and engineers to optimize fabrication for state‐of‐the‐art photonics using many materials.
Multi-view 3D Reconstruction of a Texture-less Smooth Surface of Unknown Generic Reflectance

Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging task in multi-view reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper proposes a simple and practical solution to overcome this challenge based on a co-located camera-light scanner device. Unlike existing solutions, we do not explicitly solve for correspondence. Instead, we argue the problem is generally well-posed by multi-view geometrical and photometric constraints, and can be solved from a small number of input views. We formulate the reconstruction task as a joint energy minimization over the surface geometry and reflectance. Despite this energy is highly non-convex, we develop an optimization algorithm that robustly recovers globally optimal shape and reflectance even from a random initialization. Extensive experiments on both simulated and real data have validated our method, and possible future extensions are discussed.
Timing measurements with a 3D silicon sensor on Timepix3 in a 180 GeV/c hadron beam

Test beam measurements have been carried out with a 3D sensor on a Timepix3 ASIC and the time measurements are presented. The measurements are compared to those of a thin planar sensor on Timepix3. It is shown that for a perpendicularly incident beam the time resolution of both detectors is dominated by the Timepix3 front-end. The 3D detector is dominated by the time-to-digital conversion whereas the analog front-end jitter also gives a significant contribution for the thin planar detector. The 3D detector reaches an overall time resolution of 567 ± 6 ps compared to 683 ± 8 ps for the thin planar detector. For a grazing angle beam, however, the thin planar detector achieves a better time resolution because it has a lower pixel capacitance, and therefore suffers less from jitter in the analog front-end for the low charge signals that mainly occur in this type of measurement. Finally, it is also shown that the 3D and thin planar detector can achieve time resolutions for large clusters of about 100 ps and 250 ps, respectively, by combining many single hit measurements.
Modeling neutral viral mutations in the spread of SARS-CoV-2 epidemics

Although traditional models of epidemic spreading focus on the number of infected, susceptible and recovered individuals, a lot of attention has been devoted to integrate epidemic models with population genetics. Here we develop an individual-based model for epidemic spreading on networks in which viruses are explicitly represented by finite chains of nucleotides that can mutate inside the host. Under the hypothesis of neutral evolution we compute analytically the average pairwise genetic distance between all infecting viruses over time. We also derive a mean-field version of this equation that can be added directly to compartmental models such as SIR or SEIR to estimate the genetic evolution. We compare our results with the inferred genetic evolution of SARS-CoV-2 at the beginning of the epidemic in China and found good agreement with the analytical solution of our model. Finally, using genetic distance as a proxy for different strains, we use numerical simulations to show that the lower the connectivity between communities, e.g., cities, the higher the probability of reinfection.
Robust Fairness-aware Learning Under Sample Selection Bias

The underlying assumption of many machine learning algorithms is that the training data and test data are drawn from the same distributions. However, the assumption is often violated in real world due to the sample selection bias between the training and test data. Previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data. However, how to achieve fairness in the built classification models is under-explored. In this paper, we propose a framework for robust and fair learning under sample selection bias. Our framework adopts the reweighing estimation approach for bias correction and the minimax robust estimation approach for achieving robustness on prediction accuracy. Moreover, during the minimax optimization, the fairness is achieved under the worst case, which guarantees the model's fairness on test data. We further develop two algorithms to handle sample selection bias when test data is both available and unavailable. We conduct experiments on two real-world datasets and the experimental results demonstrate its effectiveness in terms of both utility and fairness metrics.
Polarimetric Spatio-Temporal Light Transport Probing

Fig. 1. We propose a computational light transport probingmethod that decomposes transport into full polarization, spatial and temporal dimensions.Wemodel this multi-dimensional light transport as a tensor and analyze low-rank structure in the polarization domain which is exploited by our polarimetric probing method. We instantiate our approach with two imaging systems for spatio-polarimetric and coaxial temporal-polarimetric capture. (a)&(d) Conventional intensity imagers integrate incident light intensity over space and time independently of the polarization states of light, losing geometric and material information encoded in the polarimetric transport. Capturing polarization-resolved spatial transport components of (b) epipolar and (c) non-epipolar dimensions enable fine-grained decomposition of light transport. Combining temporal and polarimetric dimensions, we separate (e) geometry-dependent reflections and (f) direct/indirect reflections that cannot be resolved in the temporal-only measurements.
On the Ulam Type Stability of Nonlinear Volterra Integral Equations

In this paper, we examine the Hyers-Ulam and Hyers-Ulam-Rassias stability of solutions of a general class of nonlinear Volterra integral equations. By using a fixed point alternative and improving a technique commonly used in similar problems, we extend and improve some well-known results on this problem. We also provide some examples visualizing the improvement of the results mentioned.
FENXI: Deep-learning Traffic Analytics at the edge

Live traffic analysis at the first aggregation point in the ISP network enables the implementation of complex traffic engineering policies but is limited by the scarce processing capabilities, especially for Deep Learning (DL) based analytics. The introduction of specialized hardware accelerators, offers the opportunity to enhance processing capabilities of network devices at the edge. Yet, no packet processing pipeline is capable of offering DL-based analysis capabilities in the data-plane, without interfering with network operations. In this paper, we present FENXI, a system to run complex analytics by leveraging Tensor Processing Unit (TPU). The design of FENXI decouples forwarding and traffic analytics which operates at different granularities i.e., packet and flow levels. We conceive two independent modules that asynchronously communicate to exchange network data and analytics results, and design data structures to extract flow level statistics without impacting per-packet processing. We prototyped and evaluated FENXI on general-purpose servers considering both adversarial and realistic network conditions. Our analysis shows that FENXI can sustain 100 Gbps line rate traffic processing requiring only limited resources, while also dynamically adapting to variable network conditions.
Quadratic variations for Gaussian isotropic random fields on the sphere

In this paper we define (empirical) quadratic variations for a Gaussian isotropic random field f on a unit sphere as sums over equidistant increments on one single geodesic line on the surface of the sphere. We prove a noncentral limit theorem for a fixed Fourier component of such a field as well as quantitative central limit theorems in the increasing frequency regime. Based on these results we propose estimators of the angular power spectrum and study their properties. Moreover, we show a quantitative central limit theorem for quadratic variations over the field f and construct an estimator for the Hurst parameter of a L(S)-valued fractional Brownian motion.
Elastic shape analysis of brain structures for predictive modeling of PTSD

It is well-known that morphological features in the brain undergo changes due to traumatic events and associated disorders such as post-traumatic stress disorder (PTSD). However, existing approaches typically offer group-level comparisons, and there are limited predictive approaches for modeling behavioral outcomes based on brain shape features that can account for heterogeneity in PTSD, which is of paramount interest. We propose a comprehensive shape analysis framework representing brain sub-structures, such as the hippocampus, amygdala, and putamen, as parameterized surfaces and quantifying their shape differences using an elastic shape metric. Under this metric, we compute shape summaries (mean, covariance, PCA) of brain sub-structures and represent individual brain shapes by their principal scores under a shape-PCA basis. These representations are rich enough to allow visualizations of full 3D structures and help understand localized changes. In order to validate the elastic shape analysis, we use the principal components (PCs) to reconstruct the brain structures and perform further evaluation by performing a regression analysis to model PTSD and trauma severity using the brain shapes represented via PCs and in conjunction with auxiliary exposure variables. We apply our method to data from the Grady Trauma Project (GTP), where the goal is to predict clinical measures of PTSD. The framework seamlessly integrates accurate morphological features and other clinical covariates to yield superior predictive performance when modeling PTSD outcomes. Compared to vertex-wise analysis and other widely applied shape analysis methods, the elastic shape analysis approach results in considerably higher reconstruction accuracy for the brain shape and reveals significantly greater predictive power. It also helps identify local deformations in brain shapes associated with PTSD severity.
ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys

We present a method for creating simulated galaxy catalogs with realistic galaxy luminosities, broadband colors, and projected clustering over large cosmic volumes. The technique, denoted Addgals (Adding Density Dependent GAlaxies to Lightcone Simulations), uses an empirical approach to place galaxies within lightcone outputs of cosmological simulations. It can be applied to significantly lower-resolution simulations than those required for commonly used methods such as halo occupation distributions, subhalo abundance matching, and semi-analytic models, while still accurately reproducing projected galaxy clustering statistics down to scales of r ∼ 100 h −1kpc . We show that Addgals catalogs reproduce several statistical properties of the galaxy distribution as measured by the Sloan Digital Sky Survey (SDSS) main galaxy sample, including galaxy number densities, observed magnitude and color distributions, as well as luminosity- and color-dependent clustering. We also compare to cluster–galaxy cross correlations, where we find significant discrepancies with measurements from SDSS that are likely linked to artificial subhalo disruption in the simulations. Applications of this model to simulations of deep wide-area photometric surveys, including modeling weak-lensing statistics, photometric redshifts, and galaxy cluster finding, are presented in DeRose et al., and an application to a full cosmology analysis of Dark Energy Survey (DES) Year 3 like data is presented in DeRose et al. We plan to publicly release a 10,313 square degree catalog constructed using Addgals with magnitudes appropriate for several existing and planned surveys, including SDSS, DES, VISTA, Wide-field Infrared Survey Explorer, and Rubin Observatory’s Legacy Survey of Space and Time.
Near-threshold scaling of resonant inelastic collisions at ultralow temperatures

We show that the cross sections for a broad range of resonant inelastic processes accompanied by excitation exchange (such as spin-exchange, Förster resonant, or angular momentum exchange) exhibit an unconventional near-threshold scaling E∆m12 , where E is the collision energy, ∆m12 = m1 + m2 − m1 − m2, and mi and mi are the initial and final angular momentum projections of the colliding species (i = 1, 2). In particular, the inelastic cross sections for ∆m12 = 0 transitions display an unconventional E0 scaling similar to that of elastic cross sections, and their rates vanish as T∆m12+1/2. For collisions dominated by even partial waves (such as those of identical bosons in the same internal state) the scaling is modified to σinel ∝ E∆m12+1 if ∆m12 is odd. We present accurate quantum scattering calculations that illustrate these modified threshold laws for resonant spin exchange in ultracold Rb + Rb and O2 + O2 collisions. Our results illustrate that the threshold scaling of collision cross sections is determined only by the energetics of the underlying process (resonant vs. exothermic) rather than by whether the internal states of colliding particles is changed in the collision.