Miracle-dz/UniFAR-FTUs huggingface dataset:
Lopsidedness in WHISP galaxies - I. Rotation curves and kinematic lopsidedness

The frequently observed lopsidedness of the distribution of stars and gas in disc galaxies is still considered as a major problem in galaxy dynamics. It is even discussed as an imprint of the formation history of discs and the evolution of baryons in dark matter haloes. Here, we analyse a selected sample of 70 galaxies from the Westerbork Hi Survey of Spiral and Irregular Galaxies. The Hi data allow us to follow the morphology and the kinematics out to very large radii. In the present paper, we present the rotation curves and study the kinematic asymmetry. We extract the rotation curves of the receding and approaching sides separately and show that the kinematic behaviour of disc galaxies can be classified into five different types: symmetric velocity fields where the rotation curves of the receding and approaching sides are almost identical; global distortions where the rotation velocities of the receding and approaching sides have an offset that is constant with radius; local distortions leading to large deviations in the inner and negligible deviations in the outer parts (and vice versa); and distortions that divide the galaxies into two kinematic systems that are visible in terms of the different behaviour of the rotation curves of the receding and approaching sides, which leads to a crossing and a change in side. The kinematic lopsidedness is measured from the maximum rotation velocities, averaged over the plateau of the rotation curves. This gives a good estimate of the global lopsidedness in the outer parts of the sample galaxies. We find that the mean value of the perturbation parameter denoting the lopsided potential as obtained from the kinematic data is 0.056. Altogether, 36% of the sample galaxies are globally lopsided, which can be interpreted as the disc responding to a halo that was distorted by a tidal encounter. In Paper II, we study the morphological lopsidedness of the same sample of galaxies.
Putting them under microscope: a fine-grained approach for detecting redundant test cases in natural language

Natural language (NL) documentation is the bridge between software managers and testers, and NL test cases are prevalent in system-level testing and other quality assurance activities. Due to reasons such as requirements redundancy, parallel testing, tester turn-over within long evolving history, there are inevitably lots of redundant test cases, which significantly increase the cost. Previous redundancy detection approaches typically treat the textual descriptions as a whole to compare their similarity and suffer from low precision. Our observation reveals that a test case can have explicit test-oriented entities, such as tested function Components, Constraints, etc; and there are also specific relations between these entities. This inspires us with a potential opportunity for accurate redundancy detection. In this paper, we first define five test-oriented entity categories and four associated relation categories, and re-formulate the NL test case redundancy detection problem as the comparison of detailed testing content guided by the test-oriented entities and relations. Following that, we propose Tscope, a fine-grained approach for redundant NL test case detection by dissecting test cases into atomic test tuple(s) with the entities restricted by associated relations. To serve as the test case dissection, Tscope designs a context-aware model for the automatic entity and relation extraction. Evaluation on 3,467 test cases from ten projects shows Tscope could achieve 91.8% precision, 74.8% recall and 82.4% F1, significantly outperforming state-of-the-art approaches and commonly-used classifiers. This new formulation of the NL test case redundant detection problem can motivate the follow-up studies in further improving this task and other related tasks involving NL descriptions.
The effect of method of handling and season on weight loss, water holding capacity and colour quality of beef sold in Imo State, Nigeria

The objective of this study was to determine the influence of handling method and season on water holding capacity, weight loss, cooking loss and colour of retail beef. Four handling methods which corresponded to the commercial methods of handling retail meat were selected. They were beef sample kept uncovered on the butchers table (Treatment A); beef sample kept uncovered and under room temperature but not on the butcher’s table in order to prevent customers from handling it (Treatment B); beef sample exposed to sunlight (Treatment C); and beef sample kept on top of ice in a covered cooler (Treatment D). Samples were kept from 09.00 hrs till 18.00 hrs on sampling days. The experiment was replicated 3 times in each of the two seasons (rainy and dry seasons), with an interval of 7 days between sampling. Percentage water holding capacity (% WHC) was neither affected by season nor handling method (P>0.05). However, the mean % WHC for the rainy season beef samples was slightly higher than that for the dry season counterparts. Percentage weight loss (% WL) showed significant differences (P<0.05) between the four handling methods within and between the seasons. Similarly, the handling methods and season had significant effects (P<0.05) in percentage cooking loss (% CL) and subjective colour score (SCC). Significant and positive linear relationship was observed between atmospheric humidity and WHC (R=0.807; P<0.05). There was also significant and positive linear correlation between atmospheric temperature and WL (R=0.857; P<0.05). Keywords : Beef, Handling method, Season, Humidity, Temperature and Meat quality
HapticLever: Kinematic Force Feedback using a 3D Pantograph

HapticLever is a new kinematic approach for VR haptics which uses a 3D pantograph to stiffly render large-scale surfaces using small-scale proxies. The HapticLever approach does not consume power to render forces, but rather puts a mechanical constraint on the end effector using a small-scale proxy surface. The HapticLever approach provides stiff force feedback when the user interacts with a static virtual surface, but allows the user to move their arm freely when moving through free virtual space. We present the problem space, the related work, and the HapticLever design approach.
Data-driven spatio-temporal modelling of glioblastoma

Mathematical oncology provides unique and invaluable insights into tumour growth on both the microscopic and macroscopic levels. This review presents state-of-the-art modelling techniques and focuses on their role in understanding glioblastoma, a malignant form of brain cancer. For each approach, we summarize the scope, drawbacks and assets. We highlight the potential clinical applications of each modelling technique and discuss the connections between the mathematical models and the molecular and imaging data used to inform them. By doing so, we aim to prime cancer researchers with current and emerging computational tools for understanding tumour progression. By providing an in-depth picture of the different modelling techniques, we also aim to assist researchers who seek to build and develop their own models and the associated inference frameworks. Our article thus strikes a unique balance. On the one hand, we provide a comprehensive overview of the available modelling techniques and their applications, including key mathematical expressions. On the other hand, the content is accessible to mathematicians and biomedical scientists alike to accommodate the interdisciplinary nature of cancer research.
Open-source High-precision Autonomous Suturing Framework With Visual Guidance

Autonomous surgery has attracted increasing attention for revolutionizing robotic patient care, yet remains a distant and challenging goal. In this paper, we propose an image-based framework for high-precision autonomous suturing operation. We first build an algebraic geometric algorithm to achieve accurate needle pose estimation, then design the corresponding keypoint-based calibration network for joint-offset compensation, and further plan and control suture trajectory. Our solution ranked first among all competitors in the AccelNet Surgical Robotics Challenge. The source code is opened here to accelerate future autonomous surgery research.
Type theory in human-like learning and inference

Humans can generate reasonable answers to novel queries (Schulz, 2012): if I asked you what kind of food you want to eat for lunch, you would respond with a food, not a time. The thought that one would respond"After 4pm"to"What would you like to eat"is either a joke or a mistake, and seriously entertaining it as a lunch option would likely never happen in the first place. While understanding how people come up with new ideas, thoughts, explanations, and hypotheses that obey the basic constraints of a novel search space is of central importance to cognitive science, there is no agreed-on formal model for this kind of reasoning. We propose that a core component of any such reasoning system is a type theory: a formal imposition of structure on the kinds of computations an agent can perform, and how they're performed. We motivate this proposal with three empirical observations: adaptive constraints on learning and inference (i.e. generating reasonable hypotheses), how people draw distinctions between improbability and impossibility, and people's ability to reason about things at varying levels of abstraction.
Less is More: Task-aware Layer-wise Distillation for Language Model Compression

Layer-wise distillation is a powerful tool to compress large models (i.e. teacher models) into small ones (i.e., student models). The student distills knowledge from the teacher by mimicking the hidden representations of the teacher at every intermediate layer. However, layer-wise distillation is difficult. Since the student has a smaller model capacity than the teacher, it is often under-fitted. Furthermore, the hidden representations of the teacher contain redundant information that the student does not necessarily need for the target task's learning. To address these challenges, we propose a novel Task-aware layEr-wise Distillation (TED). TED designs task-aware filters to align the hidden representations of the student and the teacher at each layer. The filters select the knowledge that is useful for the target task from the hidden representations. As such, TED reduces the knowledge gap between the two models and helps the student to fit better on the target task. We evaluate TED in two scenarios: continual pre-training and fine-tuning. TED demonstrates significant and consistent improvements over existing distillation methods in both scenarios. Code is available at https://github.com/cliang1453/task-aware-distillation.
THE ASTRALUX LARGE M-DWARF MULTIPLICITY SURVEY

We present the results of an extensive high-resolution imaging survey of M-dwarf multiplicity using the Lucky Imaging technique. The survey made use of the AstraLux Norte camera at the Calar Alto 2.2 m telescope and the AstraLux Sur camera at the ESO New Technology Telescope in order to cover nearly the full sky. In total, 761 stars were observed (701 M-type and 60 late K-type), among which 182 new and 37 previously known companions were detected in 205 systems. Most of the targets have been observed during two or more epochs, and could be confirmed as physical companions through common proper motion, often with orbital motion being confirmed in addition. After accounting for various bias effects, we find a total M-dwarf multiplicity fraction of 27% ± 3% within the AstraLux detection range of 0. ″08–6″ (semimajor axes of ∼3–227 AU at a median distance of 30 pc). We examine various statistical multiplicity properties within the sample, such as the trend of multiplicity fraction with stellar mass and the semimajor axis distribution. The results indicate that M-dwarfs are largely consistent with constituting an intermediate step in a continuous distribution from higher-mass stars down to brown dwarfs. Along with other observational results in the literature, this provides further indications that stars and brown dwarfs may share a common formation mechanism, rather than being distinct populations.
Uncertainty-Aware Lidar Place Recognition in Novel Environments

State-of-the-art lidar place recognition models exhibit unreliable performance when tested on environments different from their training dataset, which limits their use in complex and evolving environments. To address this issue, we investigate the task of uncertainty-aware lidar place recognition, where each predicted place must have an associated uncertainty that can be used to identify and reject incorrect predictions. We introduce a novel evaluation protocol and present the first comprehensive benchmark for this task, testing across five uncertainty estimation techniques and three large-scale datasets. Our results show that an Ensembles approach is the highest performing technique, consistently improving the performance of lidar place recognition and uncertainty estimation in novel environments, though it incurs a computational cost. Code is publicly available at https://github.com/csiro-robotics/Uncertainty-LPR.
On strong chains of sets and functions

Shelah has shown that there are no chains of length ω3 increasing modulo finite in ω2ω2${}^{\omega _2}\omega _2$ . We improve this result to sets. That is, we show that there are no chains of length ω3 in [ω2]ℵ2$[\omega _2]^{\aleph _2}$ increasing modulo finite. This contrasts with results of Koszmider who has shown that there are, consistently, chains of length ω2 increasing modulo finite in [ω1]ℵ1$[\omega _1]^{\aleph _1}$ as well as in ω1ω1${}^{\omega _1}\omega _1$ . More generally, we study the depth of function spaces κμ${}^\kappa \mu$ quotiented by the ideal [κ]
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks

Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (SB) of DNNs - bias towards learning only the simplest features - is a key reason for this brittleness, another recent line of work has surprisingly found that diverse/ complex features are indeed learned by the backbone, and their brittleness is due to the linear classification head relying primarily on the simplest features. To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones. Namely, simple features are replicated several times in the learned representations while complex features might not be replicated. This phenomenon, we term Feature Replication Hypothesis, coupled with the Implicit Bias of SGD to converge to maximum margin solutions in the feature space, leads the models to rely mostly on the simple features for classification. To mitigate this bias, we propose Feature Reconstruction Regularizer (FRR) to ensure that the learned features can be reconstructed back from the logits. The use of {\em FRR} in linear layer training (FRR-L) encourages the use of more diverse features for classification. We further propose to finetune the full network by freezing the weights of the linear layer trained using FRR-L, to refine the learned features, making them more suitable for classification. Using this simple solution, we demonstrate up to 15% gains in OOD accuracy on the recently introduced semi-synthetic datasets with extreme distribution shifts. Moreover, we demonstrate noteworthy gains over existing SOTA methods on the standard OOD benchmark DomainBed as well.
Rainbow spanning trees in randomly coloured Gk-out

Given a graph $G=(V,E)$ on $n$ vertices and an assignment of colours to its edges, a set of edges $S \subseteq E$ is said to be rainbow if edges from $S$ have pairwise different colours assigned to them. In this paper, we investigate rainbow spanning trees in randomly coloured random $G_{k-out}$ graphs.
What is the Price of a Skill? Revealing the Complementary Value of Skills

The global workforce is urged to constantly reskill, as technological change favours particular new skills while making others redundant. But which skills are most marketable and have a sustainable demand? We propose a model for skill evaluation that attaches a premium to a skill based on near real-time online labour market data. The model allows us to isolate the economic return of an individual skill measured as a premium on hourly wages. We demonstrate that the value of a specific skill is strongly determined by complementarity - that is with how many other high-value skills a competency can be combined. We introduce the idea of “hub skills” to the field of human capital formation, i.e., high-return skills that can be recombined with many valuable complements. Specifically, we show that the value of a skill is relative, as it depends on the capacities it is combined with. For most skills, their value is highest when used in combination with skills of the same type. In addition, we find that supply and demand and the membership in specific skill communities, such as finance and legal or software and development, determine the value of a skill. We illustrate that AI skills are hub skills, as they can be combined with other high-value skills to generate beneficial complementarities . The value of some of these in-demand skills has increased significantly over the last years. Furthermore, we contrast our skill premia with automation probabilities and find that some skills are very susceptible to automation despite their high economic value. The model and metrics of our work can inform digital re-skilling to reduce labour market mismatches. In cooperation with online platforms and education providers, researchers and policy makers should consider using this blueprint to provide learners with personalised skill recommendations that complement their existing capacities and fit their occupational background.
Understanding Older Adults' Perceptions and Challenges in Using AI-enabled Everyday Technologies

Artificial intelligence (AI)-enabled everyday technologies could help address age-related challenges like physical impairments and cognitive decline. While recent research studied older adults' experiences with specific AI-enabled products (e.g., conversational agents and assistive robots), it remains unknown how older adults perceive and experience current AI-enabled everyday technologies in general, which could impact their adoption of future AI-enabled products. We conducted a survey study (N=41) and semi-structured interviews (N=15) with older adults to understand their experiences and perceptions of AI. We found that older adults were enthusiastic about learning and using AI-enabled products, but they lacked learning avenues. Additionally, they worried when AI-enabled products outwitted their expectations, intruded on their privacy, or impacted their decision-making skills. Therefore, they held mixed views towards AI-enabled products such as AI, an aid, or an adversary. We conclude with design recommendations that make older adults feel inclusive, secure, and in control of their interactions with AI-enabled products.
Relative Entropy (RE) Based LTI System Modeling Equipped with time delay Estimation and Online Modeling

This paper proposes an impulse response modeling in presence of input and noisy output of a linear time-invariant (LTI) system. The approach utilizes Relative Entropy (RE) to choose the optimum impulse response estimate, optimum time delay and optimum impulse response length. The desired RE is the Kulback-Lielber divergence of the estimated distribution from its unknown true distribution. A unique probabilistic validation approach estimates the desired relative entropy and minimizes this criterion to provide the impulse response estimate. Classical methods have approached this system modeling problem from two separate angles for the time delay estimation and for the order selection. Time delay methods focus on time delay estimate minimizing various proposed criteria, while the existing order selection approaches choose the optimum impulse response length based on their proposed criteria. The strength of the proposed RE based method is in using the RE based criterion to estimate both the time delay and impulse response length simultaneously. In addition, estimation of the noise variance, when the Signal to Noise Ratio (SNR) is unknown is also concurrent and is based on optimizing the same RE based criterion. The RE based approach is also extended for online impulse response estimations. The online method reduces the model estimation computational complexity upon the arrival of a new sample. The introduced efficient stopping criteria for this online approaches is extremely valuable in practical applications. Simulation results illustrate precision and efficiency of the proposed method compared to the conventional time delay or order selection approaches.
Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme

vi Chapter 1: Introduction and Literature Review.1 1.1 Overview of biomarker evaluation in health research.1 1.1.1 What is a biomarker? 1 1.1.2 What is the utility of evaluating biomarkers? 2 1.1.3 How are biomarkers measured? 3 1.2 Measures of accuracy for continuous biomarker.4 1.2. 1 The history of ROC curves.5 1.2. 2 Definition of ROC . 5 1.2. 3 Basic principal of the ROC curve.6 1.2. 4 Area under the ROC curve.9 1.3 Non-parametric estimation of AUC.10 1.3. 1 Non-parametric methods.11 1.3. 2 Comparison of two AUCs.13 1.4 Biomarker evaluation in longitudinal settings.15 1.4. 1 Statistical methods for repeated biomarkers.16 1.4. 2 Area under curve for repeated biomarkers.17
Submillimeter Observations of Giant Molecular Clouds in the Large Magellanic Cloud: Temperature and Density as Determined from J = 3-2 and J = 1-0 Transitions of CO

We have carried out submillimeter 12CO(J = 3–2) observations of six giant molecular clouds (GMCs) in the Large Magellanic Cloud (LMC) with the ASTE 10 m submillimeter telescope at a spatial resolution of 5 pc and very high sensitivity. We have identified 32 molecular clumps in the GMCs and revealed significant details of the warm and dense molecular gas with n(H2) ∼ 103–105 cm−3 and Tkin ∼ 60 K. These data are combined with 12CO(J = 1–0) and 13CO(J = 1–0) results and compared with LVG calculations. The results indicate that clumps that we detected are distributed continuously from cool (~10-30 K) to warm (≳30-200 K), and warm clumps are distributed from less dense (~103 cm−3) to dense (~103.5-105 cm−3). We found that the ratio of 12CO(J = 3–2) to 12CO(J = 1–0) emission is sensitive to and is well correlated with the local Hα flux. We infer that differences of clump properties represent an evolutionary sequence of GMCs in terms of density increase leading to star formation. Type I and II GMCs (starless GMCs and GMCs with H II regions only, respectively) are at the young phase of star formation where density does not yet become high enough to show active star formation, and Type III GMCs (GMCs with H II regions and young star clusters) represent the later phase where the average density is increased and the GMCs are forming massive stars. The high kinetic temperature correlated with Hα flux suggests that FUV heating is dominant in the molecular gas of the LMC.
Semantic Communication With Conceptual Spaces

Despite the fact that Shannon and Weaver’s Mathematical Theory of Communication was published over 70 years ago, all communication systems continue to operate at the first of three levels defined in this theory: the technical level. In this letter, we argue that a transition to the semantic level embodies a natural, important step in the evolution of communication technologies. Furthermore, we propose a novel approach to engineering semantic communication using conceptual spaces and functional compression. We introduce a model of semantic communication utilizing this approach, and simulate communication of image semantics demonstrating a 99.79% reduction in rate.