Miracle-dz/UniFAR-FTUs huggingface dataset:
Demonstration of frequency stability limited by thermal fluctuation noise in silicon nitride nanomechanical resonators

The frequency stability of nanomechanical resonators (NMRs) dictates the performance level of many state-of-the-art sensors (e.g., mass, force, temperature, radiation) that relate an external physical perturbation to a resonance frequency shift. While this is obviously of fundamental importance, accurate models and understandings of sources of frequency instability are not always available. The contribution of thermomechanical noise to frequency stability has been well studied in recent years and is often the fundamental performance limitation. Frequency stability limited by thermal fluctuation noise has attracted less interest but is nevertheless of fundamental importance notably in temperature sensing applications. In particular, temperature-sensitive NMR have become promising candidates for replacing traditional bolometers in infrared radiation sensing. However, reaching the ultimate detectivity limit of thermal radiation sensors requires their noise to be dominated by fundamental thermal fluctuation, which has not been demonstrated to date. In this work, we first develop a theoretical model for computing the frequency stability of NMR by considering the effect of both additive phase noise (i.e., thermomechanical and experimental detection noise) and thermal fluctuation noise in a close-loop frequency tracking scheme. We thereafter validate this model experimentally and observe thermal fluctuation noise in SiN drum resonators of various sizes at room temperature. Our work shows that by using resonators of specific characteristics—such as high temperature sensitivity, high mechanical quality factors, and high mass-to-thermal-conductance ratio—one can minimize additive phase noise below thermal fluctuation noise. This paves the way for uncooled NMR-based radiation sensors that can reach the fundamental detectivity limit of thermal radiation sensing and outperform existing technologies.
Maximum likelihood estimates in the multivariate normal with patterned mean and covariance via the em algorithm

The maximum likelihood equations for a multivariate normal model with structured mean and structured covariance matrix may not have an explicit solution. In some cases the model's error term may be decomposed as the sum of two independent error terms, each having a patterned covariance matrix, such that if one of the unobservable error terms is artificially treated as "missing data", the EM algorithm can be used to compute the maximum likelihood estimates for the original problem. Some decompositions produce likelihood equations which do not have an explicit solution at each iteration of the EM algorithm, but within-iteration explicit solutions are shown for two general classes of models including covariance component models used for analysis of longitudinal data.
ArrayFlex: A Systolic Array Architecture with Configurable Transparent Pipelining

Convolutional Neural Networks (CNNs) are the state-of-the-art solution for many deep learning applications. For maximum scalability, their computation should combine high performance and energy efficiency. In practice, the convolutions of each CNN layer are mapped to a matrix multiplication that includes all input features and kernels of each layer and is computed using a systolic array. In this work, we focus on the design of a systolic array with configurable pipeline with the goal to select an optimal pipeline configuration for each CNN layer. The proposed systolic array, called ArrayFlex, can operate in normal, or in shallow pipeline mode, thus balancing the execution time in cycles and the operating clock frequency. By selecting the appropriate pipeline configuration per CNN layer, ArrayFlex reduces the inference latency of state-of-the-art CNNs by 11 %, on average, as compared to a traditional fixed-pipeline systolic array. Most importantly, this result is achieved while using 13 %-23 % less power, for the same applications, thus offering a combined energy-delay-product efficiency between $1.4\times$ and $1.8\times$.
On the hysteresis effect in transitions between accretion and propeller regimes

Some observations and numerical simulations of disc-magnetosphere interaction show that accretion can proceed in the propeller regime. When the Alfvén radius is beyond the corotation radius, matter climbs up to the high latitudes where the Alfvén surface is inside the equilibrium surface and can accrete. We calculate the fraction of the mass flux in the disc that can accrete onto the neutron star depending on the fastness parameter and the inclination angle between rotation and magnetic axis. We find that, for a narrow range of the fastness parameter, the Alfvén and the equilibrium surfaces intersect at two different critical latitudes. While the system is transiting from the propeller to the accretion regime (the initial rise of an outburst), the disc is already thick and the part of the disc between these two critical latitudes cannot accrete. In transitions from the accretion to the propeller regime (decay of an outburst), the disc is thin, hence, full accretion of matter proceeds until the Alfvén radius moves beyond the equilibrium radius at the disc-midplane. Therefore, the accretion regime commences at a smaller fastness parameter than it ceases. As a result, the transition from the propeller to the accretion regime occurs at a luminosity higher than the transition from the accretion to the propeller regime. We discuss the implications of our results for spectral transitions exhibited by low-mass X-ray binaries.
Rega-Net: Retina Gabor Attention for Deep Convolutional Neural Networks

Extensive research works demonstrate that the attention mechanism in convolutional neural networks (CNNs) effectively improves accuracy. Nevertheless, few works design attention mechanisms using large receptive fields. In this work, we propose a novel attention method named Rega-Net to increase CNN accuracy by enlarging the receptive field. To the best of our knowledge, increasing the receptive field of the CNN requires increasing the size of the convolution kernel, which also increases the number of parameters. For solving this problem, we design convolutional kernels to resemble the nonuniformly distributed structure inspired by the mechanism of the human retina. Then, we sample variable-resolution values in the Gabor function distribution and fill these values in retina-like kernels. This distribution allows essential features to be more visible in the center position of the receptive field. We further design an attention module including these retina-like kernels. Experiments demonstrate that our Rega-Net achieves 79.96% Top-1 accuracy on ImageNet-1k for classification and 43.1% mAP on COCO2017 for object detection. The mAP of the Rega-Net increased by up to 3.5% compared to baseline networks.
Evolutionary Generalized Zero-Shot Learning

An open problem on the path to artiﬁcial intelligence is generalization from the known to the unknown, which is in-stantiated as Generalized Zero-Shot Learning (GZSL) task. In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, and (ii) is more in line with the needs of real-world deployments than transductive GZSL. In the proposed setting, a zero-shot model with poor initial performance is able to achieve online evolution during application. We elaborate on three challenges of this special task, i.e . , catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuing to evolve on a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets show that our model can learn from the test data stream while other baselines fail.
An information security monitoring and management system for 5G and 6G Networks based on SDN/NFV

- An approach to using the concept of Software-Defined Networking and Network Functions Virtualization (SDN/NFV) for the implementation of an information security monitoring and management system in 5G and 6G networks is proposed. SDN switches based on the OpenFlow protocol are offered as network sensors. In order to reduce the time for finding a subset of the right rules in the vast array of all rules on traffic filtering systems that are logically located on sensors, a method of processing and filtering traffic in 5G and 6G transport networks is proposed. This method is based on DPDK with the LPM algorithm and is capable of processing up to 8 megapackets per second on 1 CPU core; the packet processing takes O(1), which is significantly lower than with similar algorithms. The managing subsystem consists of regional monitoring centres and a main one. The main Monitoring Centre includes a main cluster of SDN controllers along with Active/Active redundancy scheme. The regional centres represent SDN software controllers that manage locally subordinate sensors. All the managing centres are interconnected via the Transport subsystem and form a network. An algorithm for network sensor load balancing between SDN controllers has been developed in order to provide fault tolerance, load balancing and network connectivity. The algorithm results in a set of optimal sensor groups with total load not exceeding the maximum capacity of the SDN controllers.
Galaxy And Mass Assembly (GAMA): extended intragroup light in a group at z = 0.2 from deep Hyper Suprime-Cam images

We present a pilot study to assess the potential of Hyper Suprime-Cam Public Data Release 2 (HSC-PDR2) images for the analysis of extended faint structures within groups of galaxies. We examine the intragroup light (IGL) of the group 400138 (Mdyn = 1.3 ± 0.5 × 1013 M⊙, z ∼ 0.2) from the Galaxy And Mass Assembly (GAMA) survey using Hyper Suprime-Cam Subaru Strategic Program Public Data Release 2 (HSC-SSP PDR2) images in g, r, and i bands. We present the most extended IGL measurement to date, reaching down to $\mu _{g}^{\rm {lim}}=30.76$ mag arcsec−2 (3σ; 10 × 10 arcsec2) at a semimajor axis of 275 kpc. The IGL shows mean colour values of g − i = 0.92, g − r = 0.60, and r − i = 0.32 (±0.01). The IGL stellar populations are younger (2–2.5 Gyr) and less metal rich ([Fe/H] ∼ −0.4) than those of the host group galaxies. We find a range of IGL fractions as a function of total group luminosity of ${\sim} 2\! -\! 36 {{\ \rm per\ cent}}$ depending on the definition of IGL, with larger fractions the bluer the observation wavelength. The early-type to late-type galaxy ratio suggests that 400138 is a more evolved group, dominated by early-type galaxies, and the IGL fraction agrees with that of other similarly evolved groups. These results are consistent with tidal stripping of the outer parts of Milky Way-like galaxies as the main driver of the IGL build-up. This is supported by the detection of substructure in the IGL towards the galaxy member 1660615 suggesting a recent interaction (<1 Gyr ago) of that galaxy with the core of the group.
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks

Recently, there has been significant progress in teaching language models to perform step-by-step reasoning to solve complex numerical reasoning tasks. Chain-of-thoughts prompting (CoT) is by far the state-of-art method for these tasks. CoT uses language models to perform both reasoning and computation in the multi-step `thought' process. To disentangle computation from reasoning, we propose `Program of Thoughts' (PoT), which uses language models (mainly Codex) to express the reasoning process as a program. The computation is relegated to an external computer, which executes the generated programs to derive the answer. We evaluate PoT on five math word problem datasets (GSM, AQuA, SVAMP, TabMWP, MultiArith) and three financial-QA datasets (FinQA, ConvFinQA, TATQA) for both few-shot and zero-shot setups. Under both few-shot and zero-shot settings, PoT can show an average performance gain over CoT by around 12\% across all the evaluated datasets. By combining PoT with self-consistency decoding, we can achieve SoTA performance on all math problem datasets and near-SoTA performance on financial datasets. All of our data and code are released in Github https://github.com/wenhuchen/Program-of-Thoughts
Reinforcement learning for traffic signal control in hybrid action space

The prevailing reinforcement-learning-based traffic signal control methods are typically staging-optimizable or duration-optimizable, depending on the action spaces. In this paper, we propose a novel control architecture, TBO, which is based on hybrid proximal policy optimization. To the best of our knowledge, TBO is the first RL-based algorithm to implement synchronous optimization of the staging and duration. Compared to discrete and continuous action spaces, hybrid action space is a merged search space, in which TBO better implements the trade-off between frequent switching and unsaturated release. Experiments are given to demonstrate that TBO reduces the queue length and delay by 13.78% and 14.08% on average, respectively, compared to the existing baselines. Furthermore, we calculate the Gini coefficients of the right-of-way to indicate TBO does not harm fairness while improving efficiency.
The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate

Variational Quantum Algorithms (VQAs), such as the Quantum Approximate Optimization Algorithm (QAOA) of [Farhi, Goldstone, Gutmann, 2014], have seen intense study towards near-term applications on quantum hardware. A crucial parameter for VQAs is the \emph{depth} of the variational ``ansatz'' used -- the smaller the depth, the more amenable the ansatz is to near-term quantum hardware in that it gives the circuit a chance to be fully executed before the system decoheres. In this work, we show that approximating the optimal depth for a given VQA ansatz is intractable. Formally, we show that for any constant $\epsilon>0$, it is QCMA-hard to approximate the optimal depth of a VQA ansatz within multiplicative factor $N^{1-\epsilon}$, for $N$ denoting the encoding size of the VQA instance. (Here, Quantum Classical Merlin-Arthur (QCMA) is a quantum generalization of NP. ) We then show that this hardness persists in the even ``simpler'' QAOA-type settings. To our knowledge, this yields the first natural QCMA-hard-to-approximate problems.
Crown-CAM: Reliable Visual Explanations for Tree Crown Detection in Aerial Images

—Visual explanation of “black-box” models has enabled researchers and experts in artiﬁcial intelligence (AI) to exploit the localization abilities of such methods to a much greater extent. Despite most of the developed visual explanation methods applied to single object classiﬁcation problems, they are not well-explored in the detection task, where the challenges may go beyond simple coarse area-based discrimination. This is of particular importance when a detector should face several objects with different scales from various viewpoints or if the objects of interest are absent. In this paper, we propose Crown-CAM to generate reliable visual explanations for the challenging and dynamic problem of tree crown detection in aerial images. It efﬁciently provides ﬁne-grain localization of tree crowns and non-contextual background suppression for scenarios with highly dense forest trees in the presence of potential distractors or scenes without tree crowns. Additionally, two Intersection over Union (IoU)-based metrics are introduced that can effectively quantify both the accuracy and inaccuracy of generated visual explanations with respect to regions with or without tree crowns in the image. Empirical evaluations demonstrate that the proposed Crown-CAM outperforms the Score-CAM, Augmented Score-CAM, and Eigen-CAM methods by an average IoU margin of 8.7, 5.3, and 21.7 (and 3.3, 9.8, and 16.5) respectively in improving the accuracy (and decreasing inaccuracy) of visual explanations on the challenging NEON tree crown dataset.
Application of Convolutional Neural Network to TSOM Images for Classification of 6 nm Node Patterned Defects

With the rapid growth in the semiconductor industry, it is becoming critical to detect and classify increasingly smaller patterned defects. Recently machine learning, including deep learning, has come to aid in this endeavor in a big way. However, the literature shows that it is challenging to successfully classify defect types at the 6 nm node with 100% accuracy using low-cost and high-volume-manufacturing compatible optical imaging methods. Here we combine a convolutional neural network (CNN) with that of an optical imaging method called through-focus scanning optical microscopy (TSOM) to successfully classify patterned defects for the 6 nm node targets using simulated optical images at the 193 nm illumination wavelength. We demonstrate the successful classification of eight variations of the defects, including the 3 nm difference in the defect size in one dimension, which is over 50 times smaller than the illumination wavelength used.
Explainable AI for Pre-Trained Code Models: What Do They Learn? When They Do Not Work?

In recent years, there has been a wide interest in designing deep neural network-based models that automate downstream software engineering tasks, such as program document generation, code search, and program repair. Although the main objective of these studies is to improve the eﬀectiveness of the downstream task, many studies only attempt to employ the next best neural network model, without a proper in-depth analysis of why a particular solution works or does not, on particular tasks or scenarios. In this paper, using an eXplainable AI (XAI) method (attention mechanism), we study state-of-the-art Transformer-based models (CodeBERT and GraphCodeBERT) on a set of software engineering downstream tasks: code document generation (CDG), code reﬁnement (CR), and code translation (CT). We ﬁrst evaluate the validity of the attention mechanism on each particular task. Then, through quantitative and qualitative studies, we identify what CodeBERT and Graph-CodeBERT learn (put the highest attention on, in terms of source code token types), on these tasks. Finally, we show some of the common patterns when the model does not work as expected (perform poorly while the problem in hand is easy) and suggest recommendations that may alleviate the observed challenges.
OpenFE: Automated Feature Generation beyond Expert-level Performance

The goal of automated feature generation is to liberate machine learning experts from the laborious task of manual feature generation, which is crucial for improving the learning performance of tabular data. The major challenge in automated feature generation is to efﬁciently and accurately identify useful features from a vast pool of candidate features. In this paper, we present OpenFE, an automated feature generation tool that provides competitive results against machine learning experts. OpenFE achieves efﬁciency and accuracy with two components: 1) a novel feature boosting method for accurately estimating the incremental performance of candidate features. 2) a feature-scoring framework for retrieving effective features from a large number of candidates through successive feature-wise halving and feature importance attribution. Extensive experiments on seven benchmark datasets show that OpenFE outperforms existing baseline methods. We further evaluate OpenFE in two famous Kaggle competitions with thousands of data science teams participating. In one of the competitions, features generated by OpenFE with a simple baseline model can beat 99.3% data science teams. In addition to the empirical results, we provide a theoretical perspective to show that feature generation is beneﬁcial in a simple yet representative setting. The code is available at https:
Vectors, viewpoint and viewpoint shift

Cognitive Linguistics has developed many insights into language but no systematic formalism. This article proposes that the discipline can benefit from standard geometrical formalisms. It outlines an approach called Discourse Space Theory (DST) that uses coordinate geometry and vector spaces. It describes how vectors have been used by several scholars to characterise the semantics of spatial prepositions, extending this approach to an abstract, deictically centred discourse space. Vector geometry comes with standard properties and operations that can serve to describe many linguistic and discourse phenomena. One of these is transformation of axes, which can be used to represent the phenomenon of viewpoint shift in discourse. The paper illustrates how DST can systematically describe viewpoint shift in foreground-background shifts, deictic verbs, tense, complement clauses and conditional constructions, especially counterfactuals.
SeedBERT: Recovering Annotator Rating Distributions from an Aggregated Label

Many machine learning tasks—particularly those in affective computing—are inherently subjective. When asked to clas-sify facial expressions or to rate an individual’s attractive- ness, humans may disagree with one another, and no single answer may be objectively correct. However, machine learn- ing datasets commonly have just one “ground truth” label for each sample, so models trained on these labels may not per- form well on tasks that are subjective in nature. Though al-lowing models to learn from the individual annotators’ ratings may help, most datasets do not provide annotator-speciﬁc labels for each sample. To address this issue, we propose Seed- BERT, a method for recovering annotator rating distributions from a single label by inducing pre-trained models to attend to different portions of the input. Our human evaluations indicate that SeedBERT’s attention mechanism is consistent with human sources of annotator disagreement. Moreover, in our empirical evaluations using large language models, SeedBERT demonstrates substantial gains in performance on downstream subjective tasks compared both to standard deep learning models and to other models that account explicitly for annotator disagreement.
Scalable and Effective Conductance-based Graph Clustering

Conductance-based graph clustering has been recognized as a fundamental operator in numerous graph analysis applications. Despite the significant success of conductance-based graph clustering, existing algorithms are either hard to obtain satisfactory clustering qualities, or have high time and space complexity to achieve provable clustering qualities. To overcome these limitations, we devise a powerful peeling-based graph clustering framework PCon. We show that many existing solutions can be reduced to our framework. Namely, they first define a score function for each vertex, then iteratively remove the vertex with the smallest score. Finally, they output the result with the smallest conductance during the peeling process. Based on our framework, we propose two novel algorithms PCon_core and PCon_de with linear time and space complexity, which can efficiently and effectively identify clusters from massive graphs with more than a few billion edges. Surprisingly, we prove that PCon_de can identify clusters with near-constant approximation ratio, resulting in an important theoretical improvement over the well-known quadratic Cheeger bound. Empirical results on real-life and synthetic datasets show that our algorithms can achieve 5~42 times speedup with a high clustering accuracy, while using 1.4~7.8 times less memory than the baseline algorithms.