Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast-and-Effective Early-Stage Multi-level Cache Optimization Method Based on Reuse-Distance Analysis

In this paper, we propose a practical and effective approach allowing designers to optimize multi-level cache size at the early system design phase. Our key contribution is to generalize the reuse distance analysis method and develop an effective and practical cache design optimization approach. We adopt a simple scanning search method to locate optimal cache solutions in terms of cache size, power consumption, or average data access delay. The proposed approach is particularly useful for early-phase system designers and is verified to be 150 to 250 times faster than the traditional simulation-based approach. In addition, we also introduce a simplified analytical model and provide designers insights about how cache design parameters may affect the expected results. As a result, designers can make an adequate decision in the early system design phase.
Response of an Unruh-DeWitt detector near an extremal black hole

We consider the response of an Unruh-DeWitt detector near an extremal charged black hole, modeling the near-horizon region of this extremal spacetime by the Bertotti-Robinson spacetime. The advantage of employing the Bertotti-Robinson limit is that the two-point functions for a massless scalar field are obtainable in closed form for the field in a number of quantum states of interest. We consider the detector coupled to a massless field in both the Boulware vacuum state and arbitrary thermal states, including the Hartle-Hawking state, and analyse the detector’s response for a broad range of trajectories. Particular attention is paid to the thermalization of the detector, the antiUnruh and anti-Hawking effect.
Large monochromatic components of small diameter

Gyárfás conjectured in 2011 that every r ‐edge‐colored K n contains a monochromatic component of bounded (“perhaps three”) diameter on at least n ∕ ( r − 1 ) vertices. Letzter proved this conjecture with diameter four. In this note we improve the result in the case of r = 3 : We show that in every 3‐edge‐coloring of K n either there is a monochromatic component of diameter at most three on at least n ∕ 2 vertices or every color class is spanning and has diameter at most four.
Resolving gas bubbles ascending in liquid metal from low-SNR neutron radiography images

We demonstrate a new image processing methodology for resolving gas bubbles travelling through liquid metal from dynamic neutron radiography images with an intrinsically low signal-to-noise ratio. Image pre-processing, denoising and bubble segmentation are described in detail, with practical recommendations. Experimental validation is presented—stationary and moving reference bodies with neutron-transparent cavities are radiographed with imaging conditions representative of the cases with bubbles in liquid metal. The new methods are applied to our experimental data from previous and recent imaging campaigns, and the performance of the methods proposed in this paper is compared against our previously achieved results. Significant improvements are observed as well as the capacity to reliably extract physically meaningful information from measurements performed under highly adverse imaging conditions. The showcased image processing solution and separate elements thereof are readily extendable beyond the present application, and have been made open-source.
Termination Analysis of Programs with Multiphase Control-Flow

Programs with multiphase control-flow are programs where the execution passes through several (possibly implicit) phases. Proving termination of such programs (or inferring corresponding runtime bounds) is often challenging since it requires reasoning on these phases separately. In this paper we discuss techniques for proving termination of such programs, in particular: (1) using multiphase ranking functions, where we will discuss theoretical aspects of such ranking functions for several kinds of program representations; and (2) using control-flow refinement, in particular partial evaluation of Constrained Horn Clauses, to simplify the control-flow allowing, among other things, to prove termination with simpler ranking functions.
High‐Resolution Label‐Free Detection of Biocompatible Polymeric Nanoparticles in Cells

The design of efficient drug nanocarriers necessitates a deep understanding of their interaction with targeted cells. Polymeric poly(lactic acid) (PLA) or poly(d,l‐lactic‐co‐glycolic acid) nanoparticles (NPs) with sizes lower than 200 nm are among the most employed nanocarriers in drug delivery. Their detection inside cells requires appropriate labeling for high‐resolution imaging techniques, which unfortunately often alter their physicochemical properties and biological fate. Moreover, nowadays no high‐resolution method allows precise detection simultaneously to the identification of NPs chemical composition in cells, which is of outmost interest to gain insights on their fate. Here, this challenge is addressed by using an innovative atomic force microscope coupled with a tunable infrared laser source (nanoIR). NanoIR is used to unambiguously identify PLA NPs of around 170 nm with high resolution. A reliable, nondestructive, and direct method able to precisely locate and chemically characterize PLA NPs within a cell without the need of labeling is presented.
CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems

As the labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge is to learn different tasks with the least amount of labeled data. Recently, pre-trained language models (PLMs) have shown promising results for few-shot learning in ToD. To better utilize the power of PLMs, this paper proposes Comprehensive Instruction (CINS) that exploits PLMs with extra task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD, ie. intent classification, dialog state tracking, and natural language generation. A sequence-to-sequence model (T5) is adopted to solve these three tasks in a unified framework. Extensive experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data. Empirical results demonstrate that the proposed CINS approach consistently improves techniques that finetune PLMs with raw input or short prompt.
Speech Enhancement by Noise Self-Supervised Rank-Constrained Spatial Covariance Matrix Estimation via Independent Deeply Learned Matrix Analysis

Rank-constrained spatial covariance matrix estimation (RCSCME) is a method for the situation that the directional target speech and the diffuse noise are mixed. In conventional RCSCME, independent low-rank matrix analysis (ILRMA) is used as the preprocessing method. We propose RCSCME using independent deeply learned matrix analysis (IDLMA), which is a supervised extension of ILRMA. In this method, IDLMA requires deep neural networks (DNNs) to separate the target speech and the noise. We use Denoiser, which is a single-channel speech enhancement DNN, in IDLMA to estimate not only the target speech but also the noise. We also propose noise self-supervised RCSCME, in which we estimate the noise-only time intervals using the output of Denoiser and design the prior distribution of the noise spatial covariance matrix for RCSCME. We confirm that the proposed methods outperform the conventional methods under several noise conditions.
MOJAVE. XIX. Brightness Temperatures and Intrinsic Properties of Blazar Jets

We present multiepoch, parsec-scale core brightness temperature observations of 447 active galactic nucleus (AGN) jets from the MOJAVE and 2 cm Survey programs at 15 GHz from 1994 to 2019. The brightness temperature of each jet over time is characterized by its median value and variability. We find that the range of median brightness temperatures for AGN jets in our sample is much larger than the variations within individual jets, consistent with Doppler boosting being the primary difference between the brightness temperatures of jets in their median state. We combine the observed median brightness temperatures with apparent jet speed measurements to find the typical intrinsic Gaussian brightness temperature of 4.1( ± 0.6) × 1010 K, suggesting that jet cores are at or below equipartition between particle and magnetic field energy in their median state. We use this value to derive estimates for the Doppler factor for every source in our sample. For the 309 jets with both apparent speed and brightness temperature data, we estimate their Lorentz factors and viewing angles to the line of sight. Within the BL Lac optical class, we find that high-synchrotron-peaked BL Lacs have smaller Doppler factors, lower Lorentz factors, and larger angles to the line of sight than intermediate and low-synchrotron-peaked BL Lacs. We confirm that AGN jets with larger Doppler factors measured in their parsec-scale radio cores are more likely to be detected in γ rays, and we find a strong correlation between γ-ray luminosity and Doppler factor for the detected sources.
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19 Spike Sequences

With the rapid global spread of COVID-19, more and more data related to this virus is becoming available, including genomic sequence data. The total number of genomic sequences that are publicly available on platforms such as GISAID is currently several million, and is increasing with every day. The availability of such Big Data creates a new opportunity for researchers to study this virus in detail. This is particularly important with all of the dynamics of the COVID-19 variants which emerge and circulate. This rich data source will give us insights on the best ways to perform genomic surveillance for this and future pandemic threats, with the ultimate goal of mitigating or eliminating such threats. Analyzing and processing the several million genomic sequences is a challenging task. Although traditional methods for sequence classification are proven to be effective, they are not designed to deal with these specific types of genomic sequences. Moreover, most of the existing methods also face the issue of scalability. Previous studies which were tailored to coronavirus genomic data proposed to use spike sequences (corresponding to a subsequence of the genome), rather than using the complete genomic sequence, to perform different machine learning (ML) tasks such as classification and clustering. However, those methods suffer from scalability issues.In this paper, we propose an approach called Spike2Vec, an efficient and scalable feature vector representation for each spike sequence that can be used for downstream ML tasks. Through experiments, we show that Spike2Vec is not only scalable on several million spike sequences, but also outperforms the baseline models in terms of prediction accuracy, F1 score, etc. Since this type of study on such huge numbers of spike sequences has not been done before (to the best of our knowledge), we believe that it will open new doors for researchers to use this data and perform different tasks to unfold new information that was not available before. We also use information gain (IG) to compute the importance of each amino acid in the spike sequence. The amino acids with higher IG values tend to be the same as many reported by the USA based Centers for Disease Control and Prevention (CDC) for different variants.
Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework

Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition

When a sufficiently large far-field training data is presented, jointly optimizing a multichannel frontend and an end-to-end (E2E) Automatic Speech Recognition (ASR) backend shows promising results. Recent literature has shown traditional beamformer designs, such as MVDR (Minimum Variance Distortionless Response) or fixed beamformers can be successfully integrated as the frontend into an E2E ASR system with learnable parameters. In this work, we propose the self-attention channel combinator (SACC) ASR frontend, which leverages the self-attention mechanism to combine multichannel audio signals in the magnitude spectral domain. Experiments conducted on a multichannel playback test data shows that the SACC achieved a 9.3% WERR compared to a state-of-the-art fixed beamformer-based frontend, both jointly optimized with a ContextNet-based ASR backend. We also demonstrate the connection between the SACC and the traditional beamformers, and analyze the intermediate outputs of the SACC.
BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

—Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a two- stage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO signiﬁcantly improves the optimization speed without changing the speciﬁc algorithm of DPGO in realistic datasets. What’s more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.
Learning to Swarm with Knowledge-Based Neural Ordinary Differential Equations

Understanding decentralized dynamics from collective behaviors in swarms is crucial for informing robot controller designs in artificial swarms and multi-agent robotic systems. However, the complexity in agent-to-agent interactions and the decentralized nature of most swarms pose a significant challenge to the extraction of single-robot control laws from collective behaviors. In this work, we consider the important task of learning decentralized single-robot controllers based solely on the state observations of a swarm's trajectory. We present a general framework by adopting knowledge-based neural ordinary differential equations (KNODE) ─ a hybrid machine learning method capable of combining artificial neural networks with known agent dynamics. Our approach distinguishes itself from most prior works in that we do not require action data for learning. We apply our framework to two different flocking swarms in 2D and 3D respectively, and demonstrate efficient training by leveraging the graphical structure of the swarms' information network. We further show that the learnt single-robot controllers can not only mimic flocking behavior in the original swarm but also scale to swarms with more robots.
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees

We introduce novel convergence results for asynchronous iterations that appear in the analysis of parallel and distributed optimization algorithms. The results are simple to apply and give explicit estimates for how the degree of asynchrony impacts the convergence rates of the iterates. Our results shorten, streamline and strengthen existing convergence proofs for several asynchronous optimization methods and allow us to establish convergence guarantees for popular algorithms that were thus far lacking a complete theoretical understanding. Specifically, we use our results to derive better iteration complexity bounds for proximal incremental aggregated gradient methods, to obtain tighter guarantees depending on the average rather than maximum delay for the asynchronous stochastic gradient descent method, to provide less conservative analyses of the speedup conditions for asynchronous block-coordinate implementations of Krasnoselskii-Mann iterations, and to quantify the convergence rates for totally asynchronous iterations under various assumptions on communication delays and update rates.
Hamiltonian neural networks for solving equations of motion.

There has been a wave of interest in applying machine learning to study dynamical systems. We present a Hamiltonian neural network that solves the differential equations that govern dynamical systems. This is an equation-driven machine learning method where the optimization process of the network depends solely on the predicted functions without using any ground truth data. The model learns solutions that satisfy, up to an arbitrarily small error, Hamilton's equations and, therefore, conserve the Hamiltonian invariants. The choice of an appropriate activation function drastically improves the predictability of the network. Moreover, an error analysis is derived and states that the numerical errors depend on the overall network performance. The Hamiltonian network is then employed to solve the equations for the nonlinear oscillator and the chaotic Hénon-Heiles dynamical system. In both systems, a symplectic Euler integrator requires two orders more evaluation points than the Hamiltonian network to achieve the same order of the numerical error in the predicted phase space trajectories.
Accurate Lung Nodule Segmentation With Detailed Representation Transfer and Soft Mask Supervision.

Accurate lung lesion segmentation from computed tomography (CT) images is crucial to the analysis and diagnosis of lung diseases, such as COVID-19 and lung cancer. However, the smallness and variety of lung nodules and the lack of high-quality labeling make the accurate lung nodule segmentation difficult. To address these issues, we first introduce a novel segmentation mask named "soft mask", which has richer and more accurate edge details description and better visualization, and develop a universal automatic soft mask annotation pipeline to deal with different datasets correspondingly. Then, a novel network with detailed representation transfer and soft mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results. Our DSNet contains a special detailed representation transfer module (DRTM) for reconstructing the detailed representation to alleviate the small size of lung nodules images and an adversarial training framework with soft mask for further improving the accuracy of segmentation. Extensive experiments validate that our DSNet outperforms other state-of-the-art methods for accurate lung nodule segmentation, and has strong generalization ability in other accurate medical segmentation tasks with competitive results. Besides, we provide a new challenging lung nodules segmentation dataset for further studies (https://drive.google.com/file/d/15NNkvDTb_0Ku0IoPsNMHezJR TH1Oi1wm/view? usp=sharing).
On the correctness of monadic backward induction

Abstract In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman’s backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg–Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. ’s generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material.
FLUORESCENT N-HETEROCYCLES VIA ONE-POT TANDEM REACTIONS

Synthetic indolizines have attracted special attention in the past years mainly due to their manifold practical utilities. An improved preparation of bis-indolizines using microwave-assisted synthesis is described. The microwave-mediated three component reaction of acyl bromide, bipyridine and dipolarophyl is catalyzed by basic alumina to give corresponding bis-indolizines in excellent yields in a one –pot reaction. This strategy would provide access to fast synthesis of bis-indolizines which otherwise are accessible only through multistep synthesis.
Safety Assessment of Saccharide Esters as Used in Cosmetics

This is a safety assessment of 40 saccharide ester ingredients as used in cosmetics. The saccharide esters are reported to function in cosmetics as emollients, skin-conditioning agents, fragrance ingredients, and emulsion stabilizers. The Expert Panel for Cosmetic Ingredient Safety (Panel) reviewed the relevant data for these ingredients. The Panel concluded that the saccharide esters are safe in cosmetics in the present practices of use and concentrations described in this safety assessment.