Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Uniform-Segmented Neuron Model for Large-Scale Neuromorphic Circuit Design: Simulation and FPGA Synthesis Results

Large-scale simulation of spiking neural networks on hardware with a remarkable resemblance to their mathematical models is a key objective of the neuromorphic discipline. This issue is, however, considerably resource-intensive due to the presence of nonlinear terms in neuron models. This paper proposes a novel uniform piecewise linear segmentation approach for nonlinear function evaluations. Employing the proposed approach, we present a uniform-segmented adaptive exponential neuron model capable of accurately producing various responses exhibited by the original model and suitable for efficient large-scale implementation. In contrast to previous nonuniform-segmented neuron models, the proposed model enjoys a high-speed and extremely simple segment address encoder unit regardless of the number of segments. Simulations and hardware implementations on FPGA confirm that the proposed model significantly outperforms previous works in terms of resource utilization, accuracy, and maximum clock frequency. Moreover, employing the resource sharing technique, a fully pipelined hardware-efficient network containing one neuron circuit is proposed aimed at simulating a large number of neurons in both real and accelerated time scales. To assess the performance of the proposed network as a high-speed accelerator, four networks composed of 1k to 4k neurons are simulated using both FPGA and standard PC workstation. Results demonstrate that the proposed fully pipelined network accelerates the simulation process, on average, 89.96 times compared with the computer-based simulation.
Integrated Buck and Modified Push−Pull DC−DC Converter With High Step-Down Ratio

This paper proposes a novel isolated high step-down conversion circuit, called an integrated buck and modified push−pull (IBMPP) converter, which is able to lower the voltage level on the primary side of the ideal transformer, resulting in a lower turns-ratio and decreased leakage inductance. The IBMPP converter is able to prevent the duty cycle from operating in extremely low conditions. Furthermore, the proposed converter adopts active-clamp techniques to recycle leakage energy and to suppress voltage spikes, so that the conversion efficiency can be effectively improved. The advantages of the IBMPP converter are its simple topology, easy control mechanism (which requires only two signals with a 180-degree phase shift), high conversion ratio, low component counts for power switches, and less voltage stress on some of the switches on the high-voltage side. The operation principle, steady-state analysis, design considerations, and experimental results of the proposed IBMPP converter are presented in detail. The feasibility of the IBMPP is verified by hardware implementation. The full-load efficiency at 250 W is 81.44%, and the input and output voltages are 380 and 5 V, respectively.
An ω-Power of a Finitary Language Which is a Borel Set of Infinite Rank

ω-powers of finitary languages are ω-languages in the form V ω, where V is a finitary language over a finite alphabet Sigma. Since the set Σ ω of infinite words over Σ can be equipped with the usual Cantor topology, the question of the topological complexity of ω-powers naturally arises and has been raised by Niwinski [13], by Simonnet [15], and by Staiger [18]. It has been proved in [14] that for each integer n≥1, there exist some ω-powers of context free languages which are Π n 0-complete Borel sets, and in [5] that there exists a context free language L such that L ω is analytic but not Borel. But the question was still open whether there exists a finitary language V such that V ω is a Borel set of infinite rank. We answer this question in this paper, giving an example of a finitary language whose ω-power is Borel of infinite rank.
TestSGD: Interpretable Testing of Neural Networks against Subtle Group Discrimination

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied, compared with identifying individual discrimination. In this work, we propose TestSGD, an interpretable testing approach that systematically identifies and measures hidden (which we call “subtle”) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes. Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set that categorizes the input space into two groups. Alongside, TestSGD also provides an estimated group discrimination score based on sampling the input space to measure the degree of the identified subtle group discrimination, which is guaranteed to be accurate up to an error bound. We evaluate TestSGD on multiple neural network models trained on popular datasets including both structured data and text data. The experiment results show that TestSGD is effective and efficient in identifying and measuring such subtle group discrimination that has never been revealed before. Furthermore, we show that the testing results of TestSGD can be used to mitigate such discrimination through retraining with negligible accuracy drop.
H i HOD. I. The Halo Occupation Distribution of H i Galaxies

The next generation of galaxy surveys will provide more precise measurements of galaxy clustering than have previously been possible. The 21 cm radio signals that are emitted from neutral atomic hydrogen (H i) gas will be detected by large-area radio surveys such as the Widefield Australian Square Kilometre Array (SKA) Pathfinder L-band Legacy All-sky Blind Survey and SKA, and deliver galaxy positions and velocities that can be used to measure galaxy clustering statistics. However, to harness this information to improve our cosmological understanding and learn about the physics of dark matter and dark energy, we need to accurately model the manner in which galaxies detected in H i trace the underlying matter distribution of the universe. For this purpose, we develop a new H i-based halo occupation distribution (HOD) model, which makes predictions for the number of galaxies present in dark matter halos conditional on their H i mass. The parameterized HOD model is fit and validated using the Dark Sage semi-analytic model, where we show that the HOD parameters can be modeled by simple linear and quadratic functions of the H i mass. However, we also find that the clustering predicted by the HOD depends sensitively on the radial distributions of the H i galaxies within their host dark matter halos, which does not follow the Navarro–Frenk–White profile in the Dark Sage simulation. As such, this work enables—for the first time—a simple prescription for placing galaxies of different H i masses within dark matter halos in a way that is able to reproduce the H i mass-dependent galaxy clustering and H i mass function simultaneously and without requiring knowledge of the optical properties of the galaxies. Further efforts are required to demonstrate that this model can be used to produce large ensembles of mock galaxy catalogs for upcoming surveys.
Randomized sketching for Krylov approximations of large-scale matrix functions

The computation of f(A)b, the action of a matrix function on a vector, is a task arising in many areas of scientific computing. In many applications, the matrix A is sparse but so large that only a rather small number of Krylov basis vectors can be stored. Here we discuss a new approach to overcome these limitations by randomized sketching combined with an integral representation of f(A)b. Two different approximations are introduced, one based on sketched FOM and another based on sketched GMRES approximation. The convergence of the latter method is analyzed for Stieltjes functions of positive real matrices. We also derive a closed form expression for the sketched FOM approximant and bound its distance to the full FOM approximant. Numerical experiments demonstrate the potential of the presented sketching approaches.
Data-driven stabilization of switched and constrained linear systems

We consider the design of state feedback control laws for both the switching signal and the continuous input of an unknown switched linear system, given past noisy input-state trajectories measurements. Based on Lyapunov-Metzler inequalities, we derive data-dependent bilinear programs whose solution directly returns a provably stabilizing controller and ensures $\mathcal{H}_2$ or $\mathcal{H}_{\infty}$ performance. We further present relaxations that considerably reduce the computational cost, still without requiring stabilizability of any of the switching modes. Finally, we showcase the flexibility of our approach on the constrained stabilization problem for a perturbed linear system.
An Improved Bernstein-type Inequality for C-Mixing-type Processes and Its Application to Kernel Smoothing

There are many processes, particularly dynamic systems, that cannot be described as strong mixing processes. \citet{maume2006exponential} introduced a new mixing coefficient called C-mixing, which includes a large class of dynamic systems. Based on this, \citet{hang2017bernstein} obtained a Bernstein-type inequality for a geometric C-mixing process, which, modulo a logarithmic factor and some constants, coincides with the standard result for the iid case. In order to honor this pioneering work, we conduct follow-up research in this paper and obtain an improved result under more general preconditions. We allow for a weaker requirement for the semi-norm condition, fully non-stationarity, non-isotropic sampling behavior. Our result covers the case in which the index set of processes lies in $\mathbf{Z}^{d+}$ for any given positive integer $d$. Here $\mathbf{Z}^{d+}$ denotes the collection of all nonnegative integer-valued $d$-dimensional vector. This setting of index set takes both time and spatial data into consideration. For our application, we investigate the theoretical guarantee of multiple kernel-based nonparametric curve estimators for C-Mixing-type processes. More specifically we firstly obtain the $L^{\infty}$-convergence rate of the kernel density estimator and then discuss the attainability of optimality, which can also be regarded as an upate of the result of \citet{hang2018kernel}. Furthermore, we investigate the uniform convergence of the kernel-based estimators of the conditional mean and variance function in a heteroscedastic nonparametric regression model. Under a mild smoothing condition, these estimators are optimal. At last, we obtain the uniform convergence rate of conditional mode function.
Flow past an inclined spheroid in homogeneous and stratified environments

Large eddy simulations (LES) are performed to study the ﬂow past a 6:1 prolate spheroid placed at an angle of incidence of 𝛼 = 10 ◦ . The diameter-based Reynolds number ( Re = 𝑈 ∞ 𝐷 / 𝜈 ) is set to a value of 5 × 10 3 and four values of diameter-based Froude numbers ( Fr = 𝑈 ∞ / 𝑁𝐷 ) are analyzed: Fr = ∞ , 6 , 1 . 9 , and 1. Visualizations of coeﬃcient of pressure ( 𝐶 𝑝 ) and friction ( 𝐶 𝑓 ) contours reveal asymmetry in the Fr = ∞ and 6 ﬂows while, at Fr = 1 and Fr = 1 . 9, the ﬂow over body does not have any visible asymmetry. This ﬁnding is further corroborated through the analysis of force coeﬃcients on the body. The changes in the pressure coeﬃcients ( 𝐶 𝑝 ), friction coeﬃcients ( 𝐶 𝑓 ), and drag coeﬃcients ( 𝐶 𝑑 ) with the Froude number are described in detail for 𝛼 = 10 ◦ . We also present the analyses of forces on the body at 𝛼 = 0 ◦ angle of incidence for comparison with the 𝛼 = 10 ◦ cases.
2022 Electrodynamics of Metamaterials Formed by Accelerated Modulation

We present a general theoretical framework, for space-time metamaterials formed by accelerated modulation. By using the tools of the general theory of relativity, we derive the light propagation properties in accelerated moving-matter media and accelerated bilayer moving-modulation media in relativistic velocities. In particular, we obtain the dispersion relations and the light deﬂection angle for the two cases and compare their eﬀects for diﬀerent refractive index values. This allows us to deepen our understanding of how light bending can be achieved using space-time modulation. Moreover, we leverage this light bending phenomenon to mimic the eﬀect of a Schwarzschild black hole on the light.
Deflection of charged signals in a dipole magnetic field in a Schwarzschild background using the Gauss-Bonnet theorem

This paper studies the deflection of charged particles in a dipole magnetic field in Schwarzschild spacetime background in the weak field approximation. To calculate the deflection angle, we use Jacobi metric and Gauss-Bonnet theorem. Since the corresponding Jacobi metric is a Finsler metric of Randers type, we use both the osculating Riemannian metric method and generalized Jacobi metric method. The deflection angle up to fourth order is obtained and the effect of the magnetic field is discussed. It is found that the magnetic dipole will increase (or decrease) the deflection angle of a positively charged signal when its rotation angular momentum is parallel (or antiparallel) to the magnetic field. It is argued that the difference in the deflection angles of different rotation directions can be viewed as a Finslerian effect of the non-reversibility of the Finsler metric. The similarity of the deflection angle in this case with that for the Kerr spacetime allows us to directly use the gravitational lensing results in the latter case. The dependence of the apparent angles on the magnetic field suggests that by measuring these angles the magnetic dipole might be constrained.
Absorbing Blackwell Games

It was shown in Flesch and Solan (2022) with a rather involved proof that all two-player stochastic games with ﬁnite state and action spaces and shift-invariant payoﬀs admit an ε -equilibrium, for every ε > 0. Their proof also holds for two-player absorbing games with tail-measurable payoﬀs. In this paper we provide a simpler proof for the existence of ε -equilibrium in two-player absorbing games with tail-measurable payoﬀs, by combining recent mathematical tools for such payoﬀ functions with classical tools for absorbing games.
Auditing Membership Leakages of Multi-Exit Networks

Relying on the truth that not all inputs require the same level of computational cost to produce reliable predictions, multi-exit networks are gaining attention as a prominent approach for pushing the limits of efficient deployment. Multi-exit networks endow a backbone model with early exits, allowing predictions at intermediate layers of the model and thus saving computation time and energy. However, various current designs of multi-exit networks are only considered to achieve the best trade-off between resource usage efficiency and prediction accuracy, the privacy risks stemming from them have never been explored. This prompts the need for a comprehensive investigation of privacy risks in multi-exit networks. In this paper, we perform the first privacy analysis of multi-exit networks through the lens of membership leakages. In particular, we first leverage the existing attack methodologies to quantify the multi-exit networks' vulnerability to membership leakages. Our experimental results show that multi-exit networks are less vulnerable to membership leakages, and the exit (number and depth) attached to the backbone model is highly correlated with the attack performance. Furthermore, we propose a hybrid attack that exploits the exit information to improve the performance of existing attacks. We evaluate membership leakage threat caused by our hybrid attack under three different adversarial setups, ultimately arriving at a model-free and data-free adversary. These results clearly demonstrate that our hybrid attacks are very broadly applicable, thereby the corresponding risks are much more severe than shown by existing membership inference attacks. We further present a defense mechanism called TimeGuard specifically for multi-exit networks and show that TimeGuard mitigates the newly proposed attacks perfectly.
Induced Natural Language Rationales and Interleaved Markup Tokens Enable Extrapolation in Large Language Models

The ability to extrapolate, i.e., to make predictions on sequences that are longer than those presented as training examples, is a challenging problem for current deep learning models. Recent work shows that this limitation persists in state-of-the-art Transformer-based models. Most solutions to this problem use specific architectures or training methods that do not generalize to other tasks. We demonstrate that large language models can succeed in extrapolation without modifying their architecture or training procedure. Our experimental results show that generating step-by-step rationales and introducing marker tokens are both required for effective extrapolation. First, we induce a language model to produce step-by-step rationales before outputting the answer to effectively communicate the task to the model. However, as sequences become longer, we find that current models struggle to keep track of token positions. To address this issue, we interleave output tokens with markup tokens that act as explicit positional and counting symbols. Our findings show how these two complementary approaches enable remarkable sequence extrapolation and highlight a limitation of current architectures to effectively generalize without explicit surface form guidance. Code available at https://anonymous.4open. science/r/induced-rationales-markup-tokens-0650/README.md
K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation

We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net. Previous 3D pose estimation methods have focused on various modifications to the graph convolution kernel, such as abandoning weight sharing or increasing the receptive field. Some of these methods employ attention-based non-local modules as auxiliary modules. In order to better model the relationship between nodes in graph-structured data and fuse the information of different neighbor nodes in a differentiated way, we make targeted modifications to the attention module and propose two modules designed for graph-structured data, graph relative positional encoding multi-head self-attention (GR-MSA) and K-order graph-oriented multi-head self-attention (KOG-MSA). By stacking GR-MSA and KOG-MSA, we propose a novel network KOG-Transformer for 2D-to-3D pose estimation. Furthermore, we propose a network for shape estimation on hand data, called GraAttention shape estimation network (GASE-Net), which takes a 3D pose as input and gradually models the shape of the hand from sparse to dense. We have empirically shown the superiority of KOG-Transformer through extensive experiments. Experimental results show that KOG-Transformer significantly outperforms the previous state-of-the-art methods on the benchmark dataset Human3.6M. We evaluate the effect of GASE-Net on two public available hand datasets, ObMan and InterHand2.6M. GASE-Net can predict the corresponding shape for input pose with strong generalization ability.
Attacking Neural Binary Function Detection

—Binary analyses based on deep neural net- works (DNNs), or neural binary analyses (NBAs), have become a hotly researched topic in recent years. DNNs have been wildly successful at pushing the performance and accuracy envelopes in the natural language and image processing domains. Thus, DNNs are highly promising for solving binary analysis problems that are typically hard due to a lack of complete information resulting from the lossy compilation process. Despite this promise, it is unclear that the prevailing strategy of repurposing embeddings and model architectures originally developed for other problem domains is sound given the adversarial contexts under which binary analysis often operates. In this paper, we empirically demonstrate that the current state of the art in neural function boundary detection is vulnerable to both inadvertent and deliberate adversarial attacks. We proceed from the insight that current generation NBAs are built upon embeddings and model architectures intended to solve syntactic problems. We devise a simple, reproducible, and scalable black-box methodology for exploring the space of inadvertent attacks – instruction sequences that could be emitted by common compiler toolchains and configurations – that exploits this syntactic design focus. We then show that these inadvertent misclassifications can be exploited by an attacker, serving as the basis for a highly effective black-box adversarial example generation process. We evaluate this methodology against two state-of-the-art neural function boundary detectors: XDA and DeepDi. We conclude with an analysis of the evaluation data and recommendations for how future research might avoid succumbing to similar attacks.
Interpreting Song Lyrics with an Audio-Informed Pre-trained Language Model

Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives. In this paper we propose BART-fusion, a novel model for generating lyric interpretations from lyrics and music audio that combines a large-scale pre-trained language model with an audio encoder. We employ a cross-modal attention module to incorporate the audio representation into the lyrics representation to help the pre-trained language model understand the song from an audio perspective, while preserving the language model's original generative performance. We also release the Song Interpretation Dataset, a new large-scale dataset for training and evaluating our model. Experimental results show that the additional audio information helps our model to understand words and music better, and to generate precise and fluent interpretations. An additional experiment on cross-modal music retrieval shows that interpretations generated by BART-fusion can also help people retrieve music more accurately than with the original BART.
Why Deep Learning’s Performance Data Are Misleading

This is a theoretical paper, as a companion paper of the keynote talk at the same conference AIEE 2023. In contrast to conscious learning, many projects in AI have employed so-called “deep learning” many of which seemed to give impressive performance. This paper explains that such performance data are deceptively inflated due to two misconducts: “data deletion” and “test on training set”. This paper clarifies “data deletion” and “test on training set” in deep learning and why they are misconducts. A simple classification method is defined, called Nearest Neighbor With Threshold (NNWT). A theorem is established that the NNWT method reaches a zero error on any validation set and any test set using the two misconducts, as long as the test set is in the possession of the author and both the amount of storage space and the time of training are finite but unbounded like with many deep learning methods. However, many deep learning methods, like the NNWT method, are all not generalizable since they have never been tested by a true test set. Why? The so-called “test set” was used in the Post-Selection step of the training stage. The evidence that misconducts actually took place in many deep learning projects is beyond the scope of this paper.