Miracle-dz/UniFAR-FTUs huggingface dataset:
The return of the traumatized army veteran: a qualitative study of UK ex-servicemen in the aftermath of war, 1945 to 2000

Abstract The challenge of transition from service to civilian life is explored through the experiences of a sample of 225 UK army veterans between 1945 and 2000. All subjects had a war pension for a psychological disorder, and most had served overseas in combat roles. Statements about issues of adjustment and health were analyzed by the constant comparison method. Although 20 themes were identified, three (‘anxiety, nerves and depression’, ‘enduring illness attributed to combat exposure’, and ‘illness interferes with the ability to find or keep employment’) accounted for 46% of the total and were reported by between 53% and 86% of subjects. Consistency was observed in the ranking of themes over time. In content, they replicate those reported by veterans of recent conflicts, suggesting that the core issues of transition have an enduring quality. Most statements (66%) date from the 1940s, a time when the application process for a pension required the veteran to provide an explanation for his illness. A rise in the number of statements during the 1980s and 1990s reflected wider cultural acceptance of post-traumatic illness and veteran population entering retirement with time to reflect on defining experiences.
A Case of Letting the Cat out of The Bag—Why Trap-Neuter-Return Is Not an Ethical Solution for Stray Cat (Felis catus) Management

Simple Summary Trap-Neuter-Return (TNR) is advocated as an effective, humane and ethical solution to problems caused by stray cats living in close association with human habitations. In Australia, TNR has previously been rejected by the Federal Government and the Australian Veterinary Association as an inappropriate management strategy for stray cats. Despite this, and public support for the control of cat numbers and legislative initiatives, calls persist for widespread trials of TNR. We review TNR literature that report empirical data to assess whether TNR resolves problems caused by stray cats and whether cats released under TNR would have a good quality of life. We identify ten ethical and welfare challenges that any cat control program must consider, particularly if cats are to be returned to urban environments. Simply, the weight of these data indicate that TNR cat management is unlikely to solve the problems in most cases and is unethical on animal welfare grounds. We argue instead for a holistic approach to reducing cat numbers using targeted adoption, early-age desexing and community education initiatives. Abstract Trap-Neuter-Return (TNR) programs, in which stray cats are captured, neutered and returned to the environment are advocated as a humane, ethical alternative to euthanasia. We review the TNR literature in light of current debate over whether or not there should be further TNR trials in Australia. We revisit the problems arising from stray cats living in association with human habitation and estimate how many stray cats would have to be processed through a scientifically-guided TNR program to avoid high euthanasia rates. We also identify 10 ethical and welfare challenges that have to be addressed: we consider the quality of life for stray cats, where they would live, whether the TNR process itself is stressful, whether TNR cats are vulnerable to injury, parasites and disease, can be medically treated, stray cats’ body condition and diet, and their impacts on people, pet cats, and urban wildlife, especially endemic fauna. We conclude that TNR is unsuitable for Australia in almost all situations because it is unlikely to resolve problems caused by stray cats or meet ethical and welfare challenges. Targeted adoption, early-age desexing, community education initiatives and responsible pet ownership have greater promise to minimize euthanasia, reduce numbers rapidly, and address the identified issues.
Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service”

“AI as a Service” (AIaaS) is a rapidly growing market, offering various plug-and-play AI services and tools. AIaaS enables its customers (users)—who may lack the expertise, data, and/or resources to develop their own systems—to easily build and integrate AI capabilities into their applications. Yet, it is known that AI systems can encapsulate biases and inequalities that can have societal impact. This paper argues that the context-sensitive nature of fairness is often incompatible with AIaaS’ ‘one-size-fits-all’ approach, leading to issues and tensions. Specifically, we review and systematise the AIaaS space by proposing a taxonomy of AI services based on the levels of autonomy afforded to the user. We then critically examine the different categories of AIaaS, outlining how these services can lead to biases or be otherwise harmful in the context of end-user applications. In doing so, we seek to draw research attention to the challenges of this emerging area.
The Agent-based Modelling for Human Behaviour Special Issue

If human societies are so complex, then how can we hope to understand them? Artificial Life gives us one answer. The field of Artificial Life comprises a diverse set of introspective studies that largely ask the same questions, albeit from many different perspectives: Why are we here? Who are we? Why do we behave as we do? Starting with the origins of life provides us with fascinating answers to some of these questions. However, some researchers choose to bring their studies closer to the present day. We are after all, human. It has been a few billion years since our ancestors were self-replicating molecules. Thus, more direct studies of ourselves and our human societies can reveal truths that may lead to practical knowledge. The papers in this special issue bring together scientists who choose to perform this kind of research.
Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Noisy intermediate-scale quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the quantum approximate optimization algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.
Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques

Maize is the most cultivated cereal in Africa in terms of land area and production, but low soil nitrogen availability often constrains yields. Developing new maize varieties with high and reliable yields using traditional crop breeding techniques in field conditions can be slow and costly. Remote sensing has become an important tool in the modernization of field-based high-throughput plant phenotyping (HTPP), providing faster gains towards the improvement of yield potential and adaptation to abiotic and biotic limiting conditions. We evaluated the performance of a set of remote sensing indices derived from red–green–blue (RGB) images along with field-based multispectral normalized difference vegetation index (NDVI) and leaf chlorophyll content (SPAD values) as phenotypic traits for assessing maize performance under managed low-nitrogen conditions. HTPP measurements were conducted from the ground and from an unmanned aerial vehicle (UAV). For the ground-level RGB indices, the strongest correlations to yield were observed with hue, greener green area (GGA), and a newly developed RGB HTPP index, NDLab (normalized difference Commission Internationale de I´Edairage (CIE)Lab index), while GGA and crop senescence index (CSI) correlated better with grain yield from the UAV. Regarding ground sensors, SPAD exhibited the closest correlation with grain yield, notably increasing in its correlation when measured in the vegetative stage. Additionally, we evaluated how different HTPP indices contributed to the explanation of yield in combination with agronomic data, such as anthesis silking interval (ASI), anthesis date (AD), and plant height (PH). Multivariate regression models, including RGB indices (R2 > 0.60), outperformed other models using only agronomic parameters or field sensors (R2 > 0.50), reinforcing RGB HTPP’s potential to improve yield assessments. Finally, we compared the low-N results to the same panel of 64 maize genotypes grown under optimal conditions, noting that only 11% of the total genotypes appeared in the highest yield producing quartile for both trials. Furthermore, we calculated the grain yield loss index (GYLI) for each genotype, which showed a large range of variability, suggesting that low-N performance is not necessarily exclusive of high productivity in optimal conditions.
Enabling Relational Database Analytical Processing in Bulk-Bitwise Processing-In-Memory

Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This paper demonstrates how GROUP-BY and JOIN, database operations not supported by previous works, can be performed efficiently in bulk-bitwise PIM for relational database analytical processing. We extend the gem5 simulator and evaluated our hardware modifications on the Star Schema Benchmark. We show that compared to previous works, our modifications improve (on average) execution time by 1.83×, energy by 4.31×, and the system’s lifetime by 3.21×. We also achieved a speedup of 4.65× over MonetDB, a modern state-of-the-art in-memory database.
Motion ID: Human Authentication Approach

We introduce a novel approach to user authentication called Motion ID. The method employs motion sensing provided by inertial measurement units (IMUs), using it to verify the persons identity via short time series of IMU data captured by the mobile device. The paper presents two labeled datasets with unlock events: the first features IMU measurements, provided by six users who continuously collected data on six different smartphones for a period of 12 weeks. The second one contains 50 hours of IMU data for one specific motion pattern, provided by 101 users. Moreover, we present a two-stage user authentication process that employs motion pattern identification and user verification and is based on data preprocessing and machine learning. The Results section details the assessment of the method proposed, comparing it with existing biometric authentication methods and the Android biometric standard. The method has demonstrated high accuracy, indicating that it could be successfully used in combination with existing methods. Furthermore, the method exhibits significant promise as a standalone solution. We provide the datasets to the scholarly community and share our project code.
Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homogeneous, which is seldom true in practical settings. Data heterogeneity makes Byzantine ML considerably more challenging, since a Byzantine machine can hardly be distinguished from a non-Byzantine outlier. A few solutions have been proposed to tackle this issue, but these provide suboptimal probabilistic guarantees and fare poorly in practice. This paper closes the theoretical gap, achieving optimality and inducing good empirical results. In fact, we show how to automatically adapt existing solutions for (homogeneous) Byzantine ML to the heterogeneous setting through a powerful mechanism, we call nearest neighbor mixing (NNM), which boosts any standard robust distributed gradient descent variant to yield optimal Byzantine resilience under heterogeneity. We obtain similar guarantees (in expectation) by plugging NNM in the distributed stochastic heavy ball method, a practical substitute to distributed gradient descent. We obtain empirical results that significantly outperform state-of-the-art Byzantine ML solutions.
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners

Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the quality of the diffusion model is limited by the insufficient diversity of training data, which hinders the performance of planning and the generalizability to new tasks. This paper introduces AdaptDiffuser, an evolutionary planning method with diffusion that can self-evolve to improve the diffusion model hence a better planner, not only for seen tasks but can also adapt to unseen tasks. AdaptDiffuser enables the generation of rich synthetic expert data for goal-conditioned tasks using guidance from reward gradients. It then selects high-quality data via a discriminator to finetune the diffusion model, which improves the generalization ability to unseen tasks. Empirical experiments on two benchmark environments and two carefully designed unseen tasks in KUKA industrial robot arm and Maze2D environments demonstrate the effectiveness of AdaptDiffuser. For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20.8% on Maze2D and 7.5% on MuJoCo locomotion, but also adapts better to new tasks, e.g., KUKA pick-and-place, by 27.9% without requiring additional expert data. More visualization results and demo videos could be found on our project page.
Online Verification of Deep Neural Networks under Domain Shift or Network Updates

Although neural networks are widely used, it remains challenging to formally verify the safety and robustness of neural networks in real-world applications. Existing methods are designed to verify the network before deployment, which are limited to relatively simple specifications and fixed networks. These methods are not ready to be applied to real-world problems with complex and/or dynamically changing specifications and networks. To effectively handle such problems, verification needs to be performed online when these changes take place. However, it is still challenging to run existing verification algorithms online. Our key insight is that we can leverage the temporal dependencies of these changes to accelerate the verification process. This paper establishes a novel framework for scalable online verification to solve real-world verification problems with dynamically changing specifications and/or networks. We propose three types of acceleration algorithms: Branch Management to reduce repetitive computation, Perturbation Tolerance to tolerate changes, and Incremental Computation to reuse previous results. Experiment results show that our algorithms achieve up to $100\times$ acceleration, and thus show a promising way to extend neural network verification to real-world applications.
ESTIMATION IN FACTOR ANALYSIS UNDER VARIOUS INITIAL ASSUMPTIONS

The object of the following paper is to investigate how far it is practicable to adopt the principle of maximum likelihood for the estimation of factor loadings, when some of the loadings are assumed a priori to be zero. Both the orthogonal and the oblique case arc considered; and the solutions obtained are illustrated by applying them to a simple example. Owing to the need for an iterative procedure the labour involved increases enormously as the number of factors increases. It is concluded that one possibility would be to use methods which, though not fully efficient, would entail only fairly simple calculations. To devise such methods is comparatively easy, but it is far more difficult to assess their efficiency.
Development and validation of a quantitative food frequency questionnaire to assess free sugar intake among Sri Lankan preschool children

Abstract The present study aims to develop a quantitative food frequency questionnaire (FFQ) to assess free sugar intake as a whole and at the food group levels, retrospectively, over the past 3 months among 4 to 5-year-old preschool children in the Colombo district, Sri Lanka. Then, to assess its reliability and relative validity. In the development phase, three 24-hour dietary recalls (24 hDRs) of 518 preschool children were collected from caregivers. Based on that, a 67-item FFQ was developed, including commonly consumed free sugar-containing food items. The validation study was conducted among another 108 preschool children. The relative validity of the FFQ was assessed by comparing it with the 24 hDRs. The test–retest reliability was assessed by repeated application of the FFQ to the same population after 6 weeks. Wilcoxon sign rank test, cross-classification with weighted Kappa statistic, Spearman rank correlation and Bland–Altman plots were used for comparison. Comparing the free sugar intake calculated by the two methods showed no difference (P = 0⋅13), a good correlation (0⋅89), good agreement in cross-classifying participants (78⋅4 % correctly classified) and a good agreement in Bland–Altman plots. Repeated application of the FFQ yielded; no differences in free sugar intake values (P = 0⋅45) a good correlation (0⋅71), acceptable agreement in cross-classifying participants (52⋅3 % correctly classified) and acceptable agreement in the Bland–Altman plot. Results were the same for all food groups. According to the results, the newly developed quantitative FFQ provides a relatively valid and reliable measure for quantifying free sugar intake among preschool children as a whole or by food group.
Coevolutionary strategies at the collective level for improved generalism

Abstract In many complex practical optimization cases, the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialized approach to each application. The previously developed multilevel selection genetic algorithm (MLSGA) already shows good performance on a range of problems due to its diversity-first approach, which is rare among evolutionary algorithms. To increase the generality of its performance, this paper proposes utilization of multiple distinct evolutionary strategies simultaneously, similarly to algorithm selection, but with coevolutionary mechanisms between the subpopulations. This distinctive approach to coevolution provides less regular communication between subpopulations with competition between collectives rather than individuals. This encourages the collectives to act more independently creating a unique subregional search, leading to the development of coevolutionary MLSGA (cMLSGA). To test this methodology, nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The mechanisms are tested on 100 different functions and benchmarked against the 9 state-of-the-art competitors to evaluate the generality of each approach. The results show that the diversity divergence in the principles of working of the selected coevolutionary approaches is more important than their individual performances. The proposed methodology has the most uniform performance on the divergent problem types, from across the tested state of the art, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space, but is outperformed by more specialized solvers on simpler benchmarking studies.
Efficient Dynamic Walking: Design Strategies to Reduce Energetic Losses of a Compass Walker at Heel Strike

Abstract Understanding the dynamics of bipedal locomotion is a key issue for the design of efficient walking systems. The mechanical analysis of the gait involves the study of the single support phase and the impulsive motion of heel strike. The aim of this work is to gain insight into the dynamics and energetics of heel-strike impacts. For this purpose, we use a concept that decouples the dynamics to the spaces of admissible and constrained motions. Detailed analysis and discussions are presented to quantify the effects of various parameters and the configuration of a compass-gait biped on the energetic aspects of heel strike.
Intersubband transitions and ultrafast all-optical modulation using multiple InGaAs-AlAsSb-InP coupled double-quantum-well structures

InP-based InGaAs-AlAs-AlAsSb coupled double-quantum-well structures have been optimized using wave-function engineering techniques to achieve near-infrared intersubband (ISB) transitions. Intersubband transitions at communication wavelengths of 1.3 and 1.55 /spl mu/m can be achieved in both near-symmetric and asymmetric coupled quantum-well structures by tailoring the well width, the central barrier width, and the carrier population in the conduction subband states by controlling either the doping level or the carrier temperature. We demonstrate ultrafast all-optical modulation of interband-resonant light at 1.3 /spl mu/m using intersubband-resonant light pulses at 1.55 /spl mu/m. An ultrafast absorption recovery time of 1.3 ps has been observed at 1.3 /spl mu/m, which can be reduced to 800 fs by probing at a higher energy above the Fermi level in the conduction band.
The emergence of a neutral Herbig–Haro jet into a photoionized nebula

Recent observations show the existence of an increasing number of collimated outflows ejected by young, low-mass stars which are embedded in H ii regions. At distances of a few tens of au from the star, at least one lobe of these outflows will be shielded from the ambient ionizing radiation by the compact, high-extinction circumstellar disc. Within these shielded regions, the jets are probably mostly neutral, similar to the jets in ‘normal’ Herbig–Haro (HH) objects. At larger distances, these jets emerge into the photoionized nebula, and start to be photoionized by the radiation from the ionizing photon source of the nebula. In this paper, we model the photoionization of an initially neutral HH jet. This process begins as an ionization front at the side of the jet, which is directed towards the ionizing star of the nebula, and progresses into the beam of the jet. There are two possible solutions. In the first solution, the jet beam becomes fully ionized through the passage of an R-type ionization front. In the second solution, the ionization front slows down enough to become a D-type front (or is already a D-type front at the point in which the jet emerges into the photoionized nebula), forming a partially ionized jet beam, with an expanding photoionized region and a compressed neutral region. We explore these two types of solutions both analytically and numerically, and discuss the observational effects introduced by this jet photoionization process, concentrating in a region of parameter space that straddles the parameters deduced for HH 444 (the jet from V 510 Orionis).
Duality Invariance and Riemannian Geometry

It is shown that the postulate of indistinguishability of the Maxwell field tensor from its dual leads to the concept of the electromagnetic field tensor as a spinor component in dual space. The demand for algebraic consistency dictates a unique connection with the gravitational field. The Maxwell field must be viewed as a set of potentials, and the necessity for a duality gauge condition excludes the existence of magnetic monopoles.