Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiating Approach and Avoidance from Traditional Notions of Sentiment in Economic Contexts

There is growing interest in the role of sentiment in economic decision-making. However, most research on the subject has focused on positive and negative valence. Conviction Narrative Theory (CNT) places Approach and Avoidance sentiment (that which drives action) at the heart of real-world decision-making, and argues that it better captures emotion in financial markets. This research, bringing together psychology and machine learning, introduces new techniques to differentiate Approach and Avoidance from positive and negative sentiment on a fundamental level of meaning. It does this by comparing word-lists, previously constructed to capture these concepts in text data, across a large range of semantic features. The results demonstrate that Avoidance in particular is well defined as a separate type of emotion, which is evaluative/cognitive and action-orientated in nature. Refining the Avoidance word-list according to these features improves macroeconomic models, suggesting that they capture the essence of Avoidance and that it plays a crucial role in driving real-world economic decision-making.
Effect of High-Fidelity Ice Accretion Simulations on the Performance of a Full-Scale Airfoil Model

Abstract The simulation of ice accretion on a wing or other surface is often required for aerodynamic evaluation, particularly at small scale or low-Reynolds number. While there are commonly accepted practices for ice simulation, there are no established and validated guidelines. The purpose of this article is to report the results of an experimental study establishing a high-fidelity, full-scale, iced-airfoil aerodynamic performance database. This research was conducted as a part of a larger program with the goal of developing subscale aerodynamic simulation methods for iced airfoils. Airfoil performance testing was carried out at the ONERA F1 pressurized wind tunnel using a 72-in. (1828.8-mm) chord NACA 23012 airfoil over a Reynolds number range of 4.5×10 6 to 16.0×10 6 and a Mach number range of 0.10 to 0.28. The high-fidelity, ice-casting simulations had a significant impact on the aerodynamic performance. A spanwise-ridge ice shape resulted in a maximum lift coefficient of 0.56 compared to the clean value of 1.85 at
Tracking patches for open source software vulnerabilities

Open source software (OSS) vulnerabilities threaten the security of software systems that use OSS. Vulnerability databases provide valuable information (e.g., vulnerable version and patch) to mitigate OSS vulnerabilities. There arises a growing concern about the information quality of vulnerability databases. However, it is unclear what the quality of patches in existing vulnerability databases is; and existing manual or heuristic-based approaches for patch tracking are either too expensive or too specific to apply to all OSS vulnerabilities. To address these problems, we first conduct an empirical study to understand the quality and characteristics of patches for OSS vulnerabilities in two industrial vulnerability databases. Inspired by our study, we then propose the first automated approach, Tracer, to track patches for OSS vulnerabilities from multiple knowledge sources. Our evaluation has demonstrated that i) Tracer can track patches for up to 273.8% more vulnerabilities than heuristic-based approaches while achieving a higher F1-score by up to 116.8%; and ii) Tracer can complement industrial vulnerability databases. Our evaluation has also indicated the generality and practical usefulness of Tracer.
Governing inter-organisational R&D supplier collaborations: a study at Jaguar Land Rover

This article discusses the importance of collaboration with suppliers and partners during research and development (R&D) technology projects. It details how this can be accomplished using the collaborative enterprise governance (CEG) concept to manage a technology project. CEG is based on the premise that parts of companies work with parts of other companies, which are reconfigured on dynamic bases according to a variety of different internal and external factors. This article presents an overview of the founding literature, the CEG and its methodology, and examples based at Jaguar Land Rover in the UK. CEG has been used here to explain why some technology projects have succeeded while others have done less well. This article concludes by offering new propositions, inducted through grounded theory, relating to the successful management of R&D projects, which should be picked up by future research studies in the area.
A New Sentence Extraction Strategy for Unsupervised Extractive Summarization Methods

In recent years, text summarization methods have attracted much attention again thanks to the researches on neural network models. Most of the current text summarization methods based on neural network models are supervised methods which need large-scale datasets. However, large-scale datasets are difficult to obtain in practical applications. In this paper, we model the task of extractive text summarization methods from the perspective of Information Theory, and then describe the unsupervised extractive methods with a uniform framework. To improve the feature distribution and to decrease the mutual information of summarization sentences, we propose a new sentence extraction strategy which can be applied to existing unsupervised extractive methods. Experiments are carried out on different datasets, and results show that our strategy is indeed effective and in line with expectations.
Updating Zigzag Persistence and Maintaining Representatives over Changing Filtrations

Computing persistence over changing ﬁltrations give rise to a stack of 2D persistence diagrams where the birth-death points are connected by the so-called ‘vines’ [5]. We consider computing these vines over changing ﬁltrations for zigzag persistence. We observe that eight atomic operations are suﬃcient for changing one zigzag ﬁltration to another and provide an update algorithm for each of them. As with the zigzag persistence algorithms for a static ﬁltration, these updates are implemented with the maintenance of representatives. Since ﬁnding consistent representatives for zigzag persistence is more involved, the updates for the zigzag case are more costly than their counterparts in the non-zigzag case. As motivations, we identify some potential use of our update algorithms including the case of dynamic point cloud data, where a vineyard of zigzag persistence diagrams captures changing homological features across distance and time.
ProbNum: Probabilistic Numerics in Python

Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference. They have been developed for linear algebra, optimization, integration and differential equation simulation. PNMs naturally incorporate prior information about a problem and quantify uncertainty due to finite computational resources as well as stochastic input. In this paper, we present ProbNum: a Python library providing state-of-the-art probabilistic numerical solvers. ProbNum enables custom composition of PNMs for specific problem classes via a modular design as well as wrappers for off-the-shelf use. Tutorials, documentation, developer guides and benchmarks are available online at www.probnum. org.
Genetic characterization of chicken anemia viruses newly isolated from diseased chicks in Japan in 2020

In this study, a total of nine chicken samples obtained from two broiler flocks in Oita and Tottori prefectures in 2020 were examined for Chicken anemia virus (CAV) infection. The samples were collected from clinically suspected flocks and diseased chickens. The CAV genome was detected in all nine samples tested by real-time PCR. Phylogenetic analyses and sequence comparisons of the full-length VP1 gene sequences indicated that all the Japanese CAV strains obtained in this study formed a similar cluster of genotype III and shared high nucleotide (99.62–100%) identity. The current Japanese CAV strains were closely related to Chinese CAV strains but not related to vaccine strains. One positive selection site of VP1 was detected among the Japanese CAV strains.
Two-step Lookahead Bayesian Optimization with Inequality Constraints

Recent advances in computationally efficient non-myopic Bayesian optimization (BO) improve query efficiency over traditional myopic methods like expected improvement while only modestly increasing computational cost. These advances have been largely limited, however, to unconstrained optimization. For constrained optimization, the few existing non-myopic BO methods require heavy computation. For instance, one existing non-myopic constrained BO method [Lam and Willcox, 2017] relies on computationally expensive unreliable brute-force derivative-free optimization of a Monte Carlo rollout acquisition function. Methods that use the reparameterization trick for more efficient derivative-based optimization of non-myopic acquisition functions in the unconstrained setting, like sample average approximation and infinitesimal perturbation analysis, do not extend: constraints introduce discontinuities in the sampled acquisition function surface that hinder its optimization. Moreover, we argue here that being non-myopic is even more important in constrained problems because fear of violating constraints pushes myopic methods away from sampling the boundary between feasible and infeasible regions, slowing the discovery of optimal solutions with tight constraints. In this paper, we propose a computationally efficient two-step lookahead constrained Bayesian optimization acquisition function (2-OPT-C) supporting both sequential and batch settings. To enable fast acquisition function optimization, we develop a novel likelihood-ratio-based unbiased estimator of the gradient of the two-step optimal acquisition function that does not use the reparameterization trick. In numerical experiments, 2-OPT-C typically improves query efficiency by 2x or more over previous methods, and in some cases by 10x or more.
Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Concise Introduction

—In this paper, I give a concise introduction to a generic theoretical framework termed Bayesian Dynamic Ensemble of Multiple Models (BDEMM), which is used for robust sequential online prediction with time series data. This framework has three major features: (1) it employs a model pool, rather than a single model, to capture possible statistical regularities underlying the data; (2) the model pool consists of multiple weighted candidate models, wherein the model weights are adapted online to capture possible temporal evolutions of the data; (3) the adaptation for the model weights follows Bayesian formalism. These features together deﬁne BDEMM. To make this introduction comprehensive, I describe BDEMM from ﬁve perspectives, namely the basic theories, its different forms of algorithmic implementations, its applications, its connections to related research, open resources for algorithm implementations, followed by a discussion of practical issues for applying it and some open problems that are worth further research.
IMCRYPTO: An In-Memory Computing Fabric for AES Encryption and Decryption

This article proposes IMCRYPTO, an in-memory computing (IMC) fabric for accelerating advanced encryption standard (AES) encryption and decryption. IMCRYPTO employs a unified structure to implement encryption and decryption in a single-hardware architecture with combined (Inv)SubBytes and (Inv)MixColumns steps. Because of this step combination and the high parallelism achieved by multiple units of random access memory (RAM) and random access/content addressable memory (RA/CAM) arrays, IMCRYPTO achieves high-throughput encryption and decryption without sacrificing area and power consumption. In addition, due to the integration of an RISC-V core, IMCRYPTO offers programmability and flexibility. IMCRYPTO improves the throughput per area by a minimum (maximum) of <inline-formula> <tex-math notation="LaTeX">$3.3\times $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$223.1\times $ </tex-math></inline-formula>) compared to previous ASICs/IMC architectures for AES-128 encryption. Projections show added benefit from emerging technologies of up to <inline-formula> <tex-math notation="LaTeX">$5.3\times $ </tex-math></inline-formula> to the area–delay–power product of IMCRYPTO.
Noise and Low frequency noise from Wind Turbines

Noise is a key issue when planning wind farms. The presentation will look into details in noise from wind turbines, noise measurements, noise assessment, including the Danish noise limits for low frequency wind turbine noise, and noise propagation. Development of noise and low frequency noise with the size of the turbines will be discussed. Questions like how far does low frequency noise propagate will be addressed.
Jigsaw: Large Language Models meet Program Synthesis

Large pre-trained language models such as GPT-3 [10], Codex [11], and Coogle's language model [7] are now capable of generating code from natural language specifications of programmer intent. We view these developments with a mixture of optimism and caution. On the optimistic side, such large language models have the potential to improve productivity by providing an automated AI pair programmer for every programmer in the world. On the cautionary side, since these large language models do not understand program semantics, they offer no guarantees about quality of the suggested code. In this paper, we present an approach to augment these large language models with post-processing steps based on program analysis and synthesis techniques, that understand the syntax and semantics of programs. Further, we show that such techniques can make use of user feedback and improve with usage. We present our experiences from building and evaluating such a tool Jigsaw, targeted at synthesizing code for using Python Pandas API using multi-modal inputs. Our experience suggests that as these large language models evolve for synthesizing code from intent, Jigsaw has an important role to play in improving the accuracy of the systems.
Antenna miniaturization: definitions, concepts, and a review with emphasis on metamaterials

The ability to shrink the physical dimensions of an antenna without significant performance degradation has been of great interest for over half a century. Over the decades, several antenna miniaturization techniques have been proposed, from structural modifications and lumped component loading, to the use of high permittivity/permeability materials, to the more recent applications of metamaterials. In this paper, we provide an overview of antenna miniaturization and a review of various miniaturization techniques with an emphasis on metamaterials. We choose to restrict the scope of our review primarily to antennas for which prototypes have been built and their properties measured. In particular, we concentrate on those metamaterial techniques categorized into the following groups: composite right/left-handed metamaterials, high impedance surfaces, and metamaterial-inspired techniques. Finally, we provide a summary table that allows the reader to compare the performance trade-offs between various miniaturized antennas.
A New Active Disturbance Controller Based on an Improved Fraction-Order Extended State Observer

This paper proposes a new fraction-order active disturbance rejection controller based on an improved fraction-order extended state observer (IFESO) for a class of fraction-order systems. Applying the IFESO, the fraction-order system can be approximated as an integer-order integrator (1/s). The parameters that ensure the stability of the IFESO and closed-loop system are provided. The frequency-domain analysis shows that the IFADRC has a stronger disturbance estimation performance for the fraction-order system than an integer-order active disturbance rejection controller (IADRC). The simulation results demonstrates that the proposed IFADRC behaves better than the IADRC and the FADRC.
Sequential Recommendation with Adaptive Preference Disentanglement

Sequential recommendation holds the promise of being able to infer user preference from the history information. Existing methods mostly assume coherent user preference in the history information, and deploy a unified model to predict the next behavior. However, user preferences are naturally diverse, and different users may enjoy their own personalities, which makes the history information mixed of heterogeneous user preferences. Inspired by this practical consideration, in this paper, we proposed a novel sequential recommender model by disentangling different user preferences. The main building block of our idea is a behavior allocator, which determines how many sub-sequences the history information should be decomposed into, and how to allocate each item into these sub-sequences. In particular, we regard the disentanglement of user preference as a Markov decision process, and design a reinforcement learning method to implement the behavior allocator. The reward in our model is designed to assign the target item to the nearest sub-sequence, and simultaneously encourage orthogonality between the generated sub-sequences. To make the disentangled sub-sequences not too sparse, we introduce a curriculum reward, which adaptively penalizes the action of creating a new sub-sequence. We conduct extensive experiments based on real-world datasets, and compare with many state-of-the-art models to verify the effectiveness of our model. Empirical studies manifest that our model can on average improve the performance by about 7.42 % and 11.98 % on metrics NDCG and MRR, respectively.
Design and Simulation of a Second-Order Universal Switched-Capacitor Filter as a 10-Pin Dual-In-Line Package Integrated Circuit

This paper explains the design of a Universal (Low, High, Band and Notch) Second-Order Filter using switched capacitor (SC) technique. The design depends on the crystal oscillator circuit that generates two non-overlapping clocks by making full use of the two independent comparators of the LM741. The oscillator circuit is used to drive a switched-capac itor integrator which is used in the design of the universal second order filter. The circuit is performed as a single IC (Integrated circuit) which can be used for different standard applications. The proposed IC design differs than other IC's such as MF10 by adding the notch filter to this design for used in wide band of applications. The design is simulated using MultiSim 9 program.
Detecting heavy Higgs bosons from natural SUSY at a 100 TeV hadron collider

Supersymmetric models with radiatively-driven naturalness (RNS) enjoy low electroweak fine-tuning whilst respecting LHC search limits on gluinos and top squarks and allowing for $m_h\simeq 125$ GeV. While the heavier Higgs bosons $H,\ A$ may have TeV-scale masses, the SUSY conserving $\mu$ parameter must lie in the few hundred GeV range. Thus, in natural SUSY models there should occur large heavy Higgs boson branching fractions to electroweakinos, with Higgs boson decays to higgsino plus gaugino dominating when they are kinematically accessible. These SUSY decays can open up new avenues for discovery. We investigate the prospects of discovering heavy neutral Higgs bosons $H$ and $A$ decaying into light plus heavy chargino pairs which can yield a four isolated lepton plus missing transverse energy signature at the LHC and at a future 100 TeV $pp$ collider. We find that discovery of heavy Higgs decay to electroweakinos via its $4\ell$ decay mode is very difficult at HL-LHC. For FCC-hh or SPPC, we study the $H,\ A \to $ SUSY reaction along with dominant physics backgrounds from the Standard Model and devise suitable selection requirements to extract a clean signal for FCC-hh or SPPC with $\sqrt{s}=100$ TeV, assuming an integrated luminosity of 15 $ab^{-1}$. We find that while a conventional cut-and-count analysis yields a signal statistical significance greater than $5\sigma$ for $m_{A,H}\sim 1.1-1.65$ TeV, a boosted-decision-tree analysis allows for heavy Higgs signal discovery at FCC-hh or SPPC for $m_{A,H}\sim 1-2$ TeV.
Highly reliable optical bidirectional path switched ring networks applicable to photonic IP networks

The reliability of the optical bidirectional path switched ring (OBPSR) network is studied by using optical networking equipment (ONE) with microelectromechanical system (MEMS)-based optical switching fabrics. The reliability of the wavelength path in OBPSR networks is shown to be highly dependent on redundant schemes of switching fabrics in the ONE. This study proposes a new redundant scheme utilizing optical couplers together with controlled switch fabrics without using active selectors, and shows that reliability of the wavelength path can be remarkably improved. A fast redundant switching operation of 10 ms was experimentally confirmed with a prototype of the ONE based on the proposed redundant switching fabrics. Furthermore, interworking operations in terms of the protection and restoration between the optical layer and the Internet protocol (IP) layer are experimentally investigated. In a 500-km optical ring network, fast wavelength path protection switching was confirmed without reconfiguring the link state database of the IP layer, and the measured protection time in the IP layer was as short as 21 ms.
A hybrid convolutional neural network/active contour approach to segmenting dead trees in aerial imagery

The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established convolutional neural networks with a novel active contour model in an energy minimization framework. Our approach yields superior performance accuracy over state-of-the-art in terms of precision, recall, and intersection over union of detected dead trees. This improved performance is essential to meet emerging challenges caused by climate change (and other man-made perturbations to the systems), particularly to monitor and estimate carbon stock decay rates, monitor forest health and biodiversity, and the overall effects of dead wood on and from climate change.
MDPGT: Momentum-based Decentralized Policy Gradient Tracking

We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations. Specifically, we propose a momentum-based decentralized policy gradient tracking (MDPGT) where a new momentum-based variance reduction technique is used to approximate the local policy gradient surrogate with importance sampling, and an intermediate parameter is adopted to track two consecutive policy gradient surrogates. MDPGT provably achieves the best available sample complexity of O(N -1 e -3) for converging to an e-stationary point of the global average of N local performance functions (possibly nonconcave). This outperforms the state-of-the-art sample complexity in decentralized model-free reinforcement learning and when initialized with a single trajectory, the sample complexity matches those obtained by the existing decentralized policy gradient methods. We further validate the theoretical claim for the Gaussian policy function. When the required error tolerance e is small enough, MDPGT leads to a linear speed up, which has been previously established in decentralized stochastic optimization, but not for reinforcement learning. Lastly, we provide empirical results on a multi-agent reinforcement learning benchmark environment to support our theoretical findings.