Miracle-dz/UniFAR-FTUs huggingface dataset:
WaterLily.jl: A differentiable fluid simulator in Julia with fast heterogeneous execution

Integrating computational fluid dynamics (CFD) software into optimization and machine-learning frameworks is hampered by the rigidity of classic computational languages and the slow performance of more flexible high-level languages. WaterLily.jl is an open-source incompressible viscous flow solver written in the Julia language. The small code base is multi-dimensional, multi-platform and backend-agnostic (serial CPU, multi-threaded,&GPU execution). The simulator is differentiable and uses automatic-differentiation internally to immerse solid geometries and optimize the pressure solver. The computational time per time step scales linearly with the number of degrees of freedom on CPUs, and we see up to a 182x speed-up using CUDA kernels. This leads to comparable performance with Fortran solvers on many research-scale problems opening up exciting possible future applications on the cutting edge of machine-learning research.
Predicting Malaria Incidence Using Artifical Neural Networks and Disaggregation Regression

Disaggregation modelling is a method of predicting disease risk at high resolution using aggregated response data. High resolution disease mapping is an important public health tool to aid the optimisation of resources, and is commonly used in assisting responses to diseases such as malaria. Current disaggregation regression methods are slow, inflexible, and do not easily allow non-linear terms. Neural networks may offer a solution to the limitations of current disaggregation methods. This project aimed to design a neural network which mimics the behaviour of disaggregation, then benchmark it against current methods for accuracy, flexibility and speed. Cross-validation and nested cross-validation tested neural networks against traditional disaggregation for accuracy and execution speed was measured. Neural networks did not improve on the accuracy of current disaggregation methods, although did see an improvement in execution time. The neural network models are more flexible and offer potential for further improvements on all metrics. The R package 'Kedis' (Keras-Disaggregation) is introduced as a user-friendly method of implementing neural network disaggregation models.
BVIP Guiding System with Adaptability to Individual Differences

Guiding robots can not only detect close-range obstacles like other guiding tools, but also extend its range to perceive the environment when making decisions. However, most existing works over-simplified the interaction between human agents and robots, ignoring the differences between individuals, resulting in poor experiences for different users. To solve the problem, we propose a data-driven guiding system to cope with the effect brighten by individual differences. In our guiding system, we design a Human Motion Predictor (HMP) and a Robot Dynamics Model (RDM) based on deep neural network, the time convolutional network (TCN) is verified to have the best performance, to predict differences in interaction between different human agents and robots. To train our models, we collected datasets that records the interactions from different human agents. Moreover, given the predictive information of the specific user, we propose a waypoints selector that allows the robot to naturally adapt to the user's state changes, which are mainly reflected in the walking speed. We compare the performance of our models with previous works and achieve significant performance improvements. On this basis, our guiding system demonstrated good adaptability to different human agents. Our guiding system is deployed on a real quadruped robot to verify the practicability.
Sidon sets, sum-free sets and linear codes

Finding the maximum size of a Sidon set in $\mathbb{F}_2^t$ is of research interest for more than 40 years. In order to tackle this problem we recall a one-to-one correspondence between sum-free Sidon sets and linear codes with minimum distance greater or equal 5. Our main contribution about codes is a new non-existence result for linear codes with minimum distance 5 based on a sharpening of the Johnson bound. This gives, on the Sidon set side, an improvement of the general upper bound for the maximum size of a Sidon set. Additionally, we characterise maximal Sidon sets, that are those Sidon sets which can not be extended by adding elements without loosing the Sidon property, up to dimension 6 and give all possible sizes for dimension 7 and 8 determined by computer calculations.
Temperature-anisotropy conjugate magnon squeezing in antiferromagnets

Quantum squeezing is an essential asset in the field of quantum science and technology. In this study, we investigate the impact of temperature and anisotropy on squeezing of quantum fluctuations in two-mode magnon states within uniaxial antiferromagnetic materials. Through our analysis, we discover that the inherent nonlinearity in these bipartite magnon systems gives rise to a conjugate magnon squeezing effect across all energy eigenbasis states, driven by temperature and anisotropy. We show that temperature induces amplitude squeezing, whereas anisotropy leads to phase squeezing. In addition, we observe that the two-mode squeezing characteristic of magnon eigenenergy states is associated with amplitude squeezing. This highlights the constructive impact of temperature and the destructive impact of anisotropy on two-mode magnon squeezing. Nonetheless, our analysis shows that the destructive effect of anisotropy is bounded. We demonstrate this by showing that, at a given temperature, the squeezing of the momentum (phase) quadrature (or equivalently, the stretching of the position (amplitude) quadrature) approaches a constant function of anisotropy after a finite value of anisotropy. Moreover, our study demonstrates that higher magnon squeeze factors can be achieved at higher temperatures, smaller levels of anisotropy, and closer to the Brillouin zone center. All these characteristics are specific to low-energy magnons in the uniaxial antiferromagnetic materials that we examine here.
Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion

Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC), which aims to predict missing facts for unseen relations with few-shot associated facts, has attracted increasing attention from practitioners and researchers. However, existing FKGC methods are based on metric learning or meta-learning, which often suffer from the out-of-distribution and overfitting problems. Meanwhile, they are incompetent at estimating uncertainties in predictions, which is critically important as model predictions could be very unreliable in few-shot settings. Furthermore, most of them cannot handle complex relations and ignore path information in KGs, which largely limits their performance. In this paper, we propose a normalizing flow-based neural process for few-shot knowledge graph completion (NP-FKGC). Specifically, we unify normalizing flows and neural processes to model a complex distribution of KG completion functions. This offers a novel way to predict facts for few-shot relations while estimating the uncertainty. Then, we propose a stochastic ManifoldE decoder to incorporate the neural process and handle complex relations in few-shot settings. To further improve performance, we introduce an attentive relation path-based graph neural network to capture path information in KGs. Extensive experiments on three public datasets demonstrate that our method significantly outperforms the existing FKGC methods and achieves state-of-the-art performance. Code is available at https://github.com/RManLuo/NP-FKGC.git.
Orthogonal AMP for Problems With Multiple Measurement Vectors and/or Multiple Transforms

Approximate message passing (AMP) algorithms break a (high-dimensional) statistical problem into manageable parts and then repeatedly solve each part in turn, akin to alternating projections. One notable characteristic is that their asymptotic behaviours can be accurately predicted via their associated state evolution equations. Orthogonal AMP (OAMP) was recently developed to avoid the need for computing the so-called Onsager term in traditional AMP algorithms, providing two clear benefits: the derivation of an OAMP algorithm is both straightforward and more broadly applicable. OAMP was originally demonstrated for statistical problems with a single measurement vector and single transform. This paper extends OAMP to statistical problems with multiple measurement vectors (MMVs) and multiple transforms (MTs). We name the resulting algorithms as OAMP-MMV and OAMP-MT, respectively, and their combination as augmented OAMP (A-OAMP). Whereas the extension of traditional AMP algorithms to such problems would be challenging, the orthogonal principle underpinning OAMP makes these extensions straightforward. The MMV and MT models are widely applicable to signal processing and communications. We present an example of a MIMO relay system with correlated source data and signal clipping, which can be effectively modelled as a joint MMV-MT system. While existing methods encounter challenges when applied to this example, OAMP offers an efficient solution with excellent performance.
Center and Spatial Extent of Auditory Events as Caused by Multiple Sound Sources in Frequency-Dependent Directions

In everyday life, humans often perceive complex auditory events. Many natural sound sources are not point-like, but spatially extensive. Also, sound reproduction techniques sometimes produce virtual sources whose directional cues propose multiple directions at a time. Perceptual mechanisms for decoding the perceived direction and the spatial distribution of such auditory events are not well known. This paper investigates how sound objects whose localization cues indicate different azimuth direction as a function of frequency, 1) are localized, and 2) how horizontally wide they are perceived. A horizontally wide (45°) sound source was created by presenting spectrally consecutive, non-overlapping, bandlimited noise samples simultaneously from the different loudspeakers of a loudspeaker grid in an anechoic environment. The narrowband samples together formed a broadband stimulus. The order of the narrowband noise samples in the loudspeakers, as well as the total frequency range of the samples, was varied from case to case. In each test case, the subjects were asked to indicate the perceived center of gravity of the sound image, as well as the direction of all the loudspeakers that they perceived to radiate sound. Generally, the perceived center could not be predicted merely by a simple model using Raatgever's frequency weighting function for binaural salience [1]. Alternative frequency weights were calculated analytically from the listening test results. The results also indicated that the perceived width of the sound sources produced by the nine-loudspeaker setup was, in all cases, less than half of the actual width of the source. This implies that some frequency bands from different loudspeakers fused together spatially.
When do discounted-optimal policies also optimize the gain?

In this technical note, we establish an upper-bound on the threshold on the discount factor starting from which all discounted-optimal deterministic policies are gain-optimal, that we prove to be tight on an example. To address computability issues of that theoretical threshold, we provide a weaker bound which is tractable on ergodic MDPs in polynomial time.
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate

Antivirus developers are increasingly embracing machine learning as a key component of malware defense. While machine learning achieves cutting-edge outcomes in many fields, it also has weaknesses that are exploited by several adversarial attack techniques. Many authors have presented both white-box and black-box generators of adversarial malware examples capable of bypassing malware detectors with varying success. We propose to combine contemporary generators in order to increase their potential. Combining different generators can create more sophisticated adversarial examples that are more likely to evade anti-malware tools. We demonstrated this technique on five well-known generators and recorded promising results. The best-performing combination of AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9% against top-tier antivirus products. This represents an average improvement of more than 36% and 627% over using only the AMG-random and MAB-Malware generators, respectively. The generator that benefited the most from having another generator follow its procedure was the FGSM injection attack, which improved the evasion rate on average between 91.97% and 1,304.73%, depending on the second generator used. These results demonstrate that combining different generators can significantly improve their effectiveness against leading antivirus programs.
Divisibility of the Sums of the Power of Consecutive Integers

We study the divisibility of the sums of the odd power of consecutive integers, $S(m,k)=1^{mk}+2^{mk}+\cdots+k^{mk}$ and $1^k+2^k+\cdots+n^k$ for odd integers $m$ and $k$, by using the Girard-Waring identity. Faulhaber's approach for the divisibilities is discussed. Some expressions of power sums in terms of Stirling numbers of the second kind are represented.
A framework to mitigate patchy reionization contamination on the primordial gravitational wave signal

One of the major goals of future cosmic microwave background (CMB) B-mode polarization experiments is the detection of primordial gravitational waves through an unbiased measurement of the tensor-to-scalar ratio r. Robust detection of this signal will require mitigating all possible contamination to the B-mode polarization from astrophysical origins. One such extragalactic contamination arises from the patchiness in the electron density during the reionization epoch. Along with the signature on CMB polarization, the patchy reionization can source secondary anisotropies on the CMB temperature through the kinetic Sunyaev-Zeldovich (kSZ) effect. In order to study the impact of this foreground for the upcoming CMB missions, we present a self-consistent framework to compute the CMB anisotropies based on a physically motivated model of reionization. We show that the value of r can bias towards a higher value if the secondary contribution from reionization is neglected. However, combining small-scale kSZ signal, large-scale E-mode polarization, and B-mode polarization measurements, we can put constraints on the patchiness in electron density during reionization and can mitigate its impact on the value of r. CMB missions such as CMB-S4 and PICO may experience a bias of >0. 17σ which can go as high as ∼0. 73σ for extreme reionization models allowed by the Planck and SPT CMB measurements. As future experiments target to measure r at 5σ, this is likely to affect the measurement significance and hence possibly affect the claim of detection of r, if not mitigated properly by using joint estimations of different reionization observables.
Collaborative Learning in Kernel-Based Bandits for Distributed Users

We study collaborative learning among distributed clients facilitated by a central server. Each client is interested in maximizing a personalized objective function that is a weighted sum of its local objective and a global objective. Each client has direct access to random bandit feedback on its local objective, but only has a partial view of the global objective and relies on information exchange with other clients for collaborative learning. We adopt the kernel-based bandit framework where the objective functions belong to a reproducing kernel Hilbert space. We propose an algorithm based on surrogate Gaussian process (GP) models and establish its order-optimal regret performance (up to polylogarithmic factors). We also show that the sparse approximations of the GP models can be employed to reduce the communication overhead across clients.
Discrimination Between Two Generalized Multivariate Modifled Bessel Populations

Most of the classical literature on discriminant analysis assumes that the underlying populations are normally distributed. In this paper, we investigate discrimination problems under the assumption that the populations originate from a generalized multivariate modifled Bessel distribution, which is a much more general model that includes both the multivariate normal and t distributions as special cases. We examine Fisher’s linear discrimination criterion and the probabilities of misclassiflcation under this model. It is shown that while the discriminant criterion remains robust, the misclassiflcation probabilities depend heavily on the underlying model.
The natural history of acetabular dysplasia and later total hip arthroplasty in late-detected DDH: 48 patients with closed reduction followed to a mean age of 62 years.

BACKGROUND AND PURPOSE The long-term prognosis of acetabular dysplasia without subluxation in developmental dislocation of the hip (DDH) is uncertain. The aim of this study was to assess the consequence of having acetabular dysplasia at skeletal maturity on the need for having a total hip arthroplasty (THA) up to the age of 60 years. PATIENTS AND METHODS Inclusion criteria were age at diagnosis 0.5-3.0 years, initial treatment with skin traction to obtain closed reduction during 1958-1962, and no acetabular procedures to improve femoral head coverage. 48 patients (57 hips) met these criteria, 41 girls and 7 boys. Hip dysplasia was defined as a center-edge (CE) angle at skeletal maturity of 10-19°, and hips with CE angles in the range 20-25° were termed borderline dysplastic. RESULTS At skeletal maturity, 18 hips had acetabular dysplasia with a mean CE angle of 15.8° (10-19), whereas 19 hips were borderline with CE angles 20-25°, and 20 hips were normal (CE angles ≥ 26°). During the follow-up period 16 hips had undergone THA, 11 of 18 hips with dysplasia, 4 of 19 with borderline dysplasia, and 1 of 20 hips without dysplasia (p < 0.001). Hip survival in the 18 dysplastic hips, with THA as the endpoint, was 100% up to patient age 40 years; thereafter survival fell to 83% at 50 years and 39% at 60 years. There was no significant correlation between CE angle at skeletal maturity and age at THA (p = 0.2). The mean age at the last follow-up in patients without THA was 62 years (60-64). CONCLUSION Acetabular dysplasia without subluxation was a risk factor for THA, but less than one-fifth of the hips had undergone THA up to patient age 50 years.
Exploring Australian emergency department clinicians' knowledge, attitudes and adherence to the national peripheral intravenous catheter clinical care standard: A cross‐sectional national survey

This study aimed to (i) capture clinicians' knowledge, attitude and adherence to the first Australian national peripheral intravenous catheter (PIVC) Clinical Care Standard, (ii) examine the instrument performance of the knowledge related questions and (iii) explore the educational needs for, and barriers to, Standard adherence among Australian ED clinicians.
Human Pose Estimation in Monocular Omnidirectional Top-View Images

Human pose estimation (HPE) with convolutional neural networks (CNNs) for indoor monitoring is one of the major challenges in computer vision. In contrast to HPE in perspective views, an indoor monitoring system can consist of an omnidirectional camera with a field of view of 180° to detect the pose of a person with only one sensor per room. To recognize human pose, the detection of keypoints is an essential upstream step. In our work we propose a new dataset for training and evaluation of CNNs for the task of keypoint detection in omnidirectional images. The training dataset, THEODORE+, consists of 50,000 images and is created by a 3D rendering engine, where humans are randomly walking through an indoor environment. In a dynamically created 3D scene, persons move randomly with simultaneously moving omnidirectional camera to generate synthetic RGB images and 2D and 3D ground truth. For evaluation purposes, the real-world PoseFES dataset with two scenarios and 701 frames with up to eight persons per scene was captured and annotated. We propose four training paradigms to finetune or re-train two top-down models in MMPose and two bottom-up models in CenterNet on THEODORE+. Beside a qualitative evaluation we report quantitative results. Compared to a COCO pretrained baseline, we achieve significant improvements especially for top-view scenes on the PoseFES dataset. Our datasets can be found at https://www.tu-chemnitz.de/etit/dst/forschung/comp_vision/datasets/index.php. en.
MLOps Spanning Whole Machine Learning Life Cycle: A Survey

Google AlphaGos win has significantly motivated and sped up machine learning (ML) research and development, which led to tremendous ML technical advances and wider adoptions in various domains (e.g., Finance, Health, Defense, and Education). These advances have resulted in numerous new concepts and technologies, which are too many for people to catch up to and even make them confused, especially for newcomers to the ML area. This paper is aimed to present a clear picture of the state-of-the-art of the existing ML technologies with a comprehensive survey. We lay out this survey by viewing ML as a MLOps (ML Operations) process, where the key concepts and activities are collected and elaborated with representative works and surveys. We hope that this paper can serve as a quick reference manual (a survey of surveys) for newcomers (e.g., researchers, practitioners) of ML to get an overview of the MLOps process, as well as a good understanding of the key technologies used in each step of the ML process, and know where to find more details.
Formation mechanism of glycolaldehyde and ethylene glycol in astrophysical ices from HCO • and • CH 2 OH recombination: an experimental study

Among all existing complex organic molecules, glycolaldehyde HOCH2CHO and ethylene glycol HOCH2CH2OHare two of the largest detected molecules in the interstellar medium.We investigate both experimentally and theoretically the low-temperature reaction pathways leading to glycolaldehyde and ethylene glycol in interstellar grains. Using infrared spectroscopy, mass spectroscopy and quantum calculations, we investigate formation pathways of glycolaldehyde and ethylene glycol based on HCO• and •CH2OH radical–radical recombinations. We also show that •CH2OH is the main intermediate radical species in the H2CO to CH3OH hydrogenation processes.We then discuss astrophysical implications of the chemical pathway we propose on the observed gas-phase ethylene glycol and glycolaldehyde.
LED: A Dataset for Life Event Extraction from Dialogs

Lifelogging has gained more attention due to its wide applications, such as personalized recommendations or memory assistance. The issues of collecting and extracting personal life events have emerged. People often share their life experiences with others through conversations. However, extracting life events from conversations is rarely explored. In this paper, we present Life Event Dialog, a dataset containing fine-grained life event annotations on conversational data. In addition, we initiate a novel Conversational Life Event Extraction task and differentiate the task from the public event extraction or the life event extraction from other sources like microblogs. We explore three information extraction (IE) frameworks to address the Conversational Life Event Extraction task: OpenIE, relation extraction, and event extraction. A comprehensive empirical analysis of the three baselines is established. The results suggest that the current event extraction model still struggles with extracting life events from human daily conversations. Our proposed Life Event Dialog dataset and in-depth analysis of IE frameworks will facilitate future research on life event extraction from conversations.