Miracle-dz/UniFAR-FTUs huggingface dataset:
Center Contrastive Loss for Metric Learning

Contrastive learning is a major studied topic in metric learning. However, sampling effective contrastive pairs remains a challenge due to factors such as limited batch size, imbalanced data distribution, and the risk of overfitting. In this paper, we propose a novel metric learning function called Center Contrastive Loss, which maintains a class-wise center bank and compares the category centers with the query data points using a contrastive loss. The center bank is updated in real-time to boost model convergence without the need for well-designed sample mining. The category centers are well-optimized classification proxies to re-balance the supervisory signal of each class. Furthermore, the proposed loss combines the advantages of both contrastive and classification methods by reducing intra-class variations and enhancing inter-class differences to improve the discriminative power of embeddings. Our experimental results, as shown in Figure 1, demonstrate that a standard network (ResNet50) trained with our loss achieves state-of-the-art performance and faster convergence.
When do associated graded modules have pure resolutions?

Given a finitely generated module $M$ over a Noetherian local ring $R$, one would like to know when the corresponding associated graded module has a pure resolution over $G_{\mathfrak{m}}(R)$. In this article, we identify a complex of free $G_{\mathfrak{m}}(R)$-modules, arising from given free resolution of $M$ over $R$, which is a resolution of $G_{\mathfrak{m}}(M)$ if and only if $G_{\mathfrak{m}}(M)$ is a pure $G_{\mathfrak{m}}(R)$-module. We also give several applications of the purity of $G_{\mathfrak{m}}(M)$ in this paper.
Contact problem for wavy surfaces in the presence of an incompressible liquid and a gas in interface gaps

This paper presents a study on smooth elastic contact between two semi-infinite elastic bodies, one of which has a wavy surface, for the case when there are an incompressible liquid, not wetting the surfaces of the bodies, at the central region of each interface gap and a gas under constant pressure at the edges of each gap. Due to the surface tension of the liquid, a pressure drop occurs in the liquid and the gas, which is described by the Laplace formula. The formulated contact problem is reduced to a singular integral equation (SIE) with the Hilbert kernel, which is transformed into a SIE with the Cauchy kernel for a derivative of a height of the gaps. A system of transcendental equations for a width of each gap and a width of the gap region filled with the liquid is obtained from the condition of boundedness of the contact stresses at the gap ends and the condition of liquid amount conservation. It is solved numerically, and the dependences of the width and shape of the gaps, the width of the gap regions filled with the liquid and the contact approach of the bodies on the applied load and the surface tension of the liquid are analyzed.
Deep Image Harmonization with Learnable Augmentation

The goal of image harmonization is adjusting the foreground appearance in a composite image to make the whole image harmonious. To construct paired training images, existing datasets adopt different ways to adjust the illumination statistics of foregrounds of real images to produce synthetic composite images. However, different datasets have considerable domain gap and the performances on small-scale datasets are limited by insufficient training data. In this work, we explore learnable augmentation to enrich the illumination diversity of small-scale datasets for better harmonization performance. In particular, our designed SYthetic COmposite Network (SycoNet) takes in a real image with foreground mask and a random vector to learn suitable color transformation, which is applied to the foreground of this real image to produce a synthetic composite image. Comprehensive experiments demonstrate the effectiveness of our proposed learnable augmentation for image harmonization. The code of SycoNet is released at https://github.com/bcmi/SycoNet-Adaptive-Image-Harmonization.
Advancing Frame-Dropping in Multi-Object Tracking-by-Detection Systems Through Event-Based Detection Triggering

With rising computational requirements modern automated vehicles (AVs) often consider trade-offs between energy consumption and perception performance, potentially jeopardizing their safe operation. Frame-dropping in tracking-by-detection perception systems presents a promising approach, although late traffic participant detection might be induced. In this paper, we extend our previous work on frame-dropping in tracking-by-detection perception systems. We introduce an additional event-based triggering mechanism using camera object detections to increase both the system's efficiency, as well as its safety. Evaluating both single and multi-modal tracking methods we show that late object detections are mitigated while the potential for reduced energy consumption is significantly increased, reaching nearly 60 Watt per reduced point in HOTA score.
Towards Semantically Enriched Embeddings for Knowledge Graph Completion

Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions.
Physical-Layer Authentication of Commodity Wi-Fi Devices via Micro-Signals on CSI Curves

This paper presents a new radiometric fingerprint that is revealed by micro-signals in the channel state information (CSI) curves extracted from commodity Wi-Fi devices. We refer to this new fingerprint as "micro-CSI". Our experiments show that micro-CSI is likely to be caused by imperfections in the radio-frequency circuitry and is present in Wi-Fi 4/5/6 network interface cards (NICs). We conducted further experiments to determine the most effective CSI collection configuration to stabilize micro-CSI. To extract micro-CSI from varying CSI curves, we developed a signal space-based extraction algorithm that effectively separates distortions caused by wireless channels and hardware imperfections under line-of-sight (LoS) scenarios. Finally, we implemented a micro-CSI-based device authentication algorithm that uses the k-Nearest Neighbors (KNN) method to identify 11 COTS Wi-Fi NICs from the same manufacturer in typical indoor environments. Our experimental results demonstrate that the micro-CSI-based authentication algorithm can achieve an average attack detection rate of over 99% with a false alarm rate of 0%.
Robust Referring Video Object Segmentation with Cyclic Structural Consensus

Referring Video Object Segmentation (R-VOS) is a challenging task that aims to segment an object in a video based on a linguistic expression. Most existing R-VOS methods have a critical assumption: the object referred to must appear in the video. This assumption, which we refer to as "semantic consensus", is often violated in real-world scenarios, where the expression may be queried against false videos. In this work, we highlight the need for a robust R-VOS model that can handle semantic mismatches. Accordingly, we propose an extended task called Robust R-VOS (R2-VOS), which accepts unpaired video-text inputs. We tackle this problem by jointly modeling the primary R-VOS problem and its dual (text reconstruction). A structural text-to-text cycle constraint is introduced to discriminate semantic consensus between video-text pairs and impose it in positive pairs, thereby achieving multi-modal alignment from both positive and negative pairs. Our structural constraint effectively addresses the challenge posed by linguistic diversity, overcoming the limitations of previous methods that relied on the point-wise constraint. A new evaluation dataset, R2-Youtube-VOS is constructed to measure the model robustness. Our model achieves state-of-the-art performance on R-VOS benchmarks, Ref-DAVIS17 and Ref-Youtube-VOS, and also our R2-Youtube-VOS dataset.
Logarithmic or algebraic: Roughening of an active Kardar-Parisi-Zhang surface

The Kardar-Parisi-Zhang (KPZ) equation sets the universality class for growing and roughening of nonequilibrium surfaces without any conservation law and nonlocal effects. We argue here that the KPZ equation can be generalised by including a symmetry-permitted nonlocal nonlinear term of active origin that is of the same order as the one included in the KPZ equation. Including this term, the 2D active KPZ equation is stable in some parameter regimes, in which the interface conformation fluctuations exhibit sub-logarithmic or super-logarithmic roughness, with nonuniversal exponents, giving positional generalised quasi-long-ranged order. For other parameter choices, the model is unstable, suggesting a perturbatively inaccessible algebraically rough interface or positional short-ranged order. Our model should serve as a paradigmatic nonlocal growth equation.
STOCHASTIC ALTERNATING DIRECTION METHOD OF MULTIPLIERS FOR STRUCTURED REGULARIZATION

In this paper, we present stochastic optimization variants of the alternating direction method of multipliers (ADMM). ADMM is a useful method to solve a regularized risk minimization problem where the regularization term is complicated and not easily dealt with in an ordinary manner. For example, structured regularization is one of the typical applications of such regularization in which ADMM is eﬀective. It includes group lasso regularization, low rank tensor regularization, and fused lasso regularization. Since ADMM is a general method and has wide applications, it is intensively studied and reﬁned these days. However, ADMM is not suited to optimization problems with huge data. To resolve this problem, online stochastic optimization variants and a batch stochastic optimization variant of ADMM are presented. All the presented methods can be easily implemented and have wide applications. Moreover, the theoretical guarantees of the methods are given.
Elliptic curves with complex multiplication and abelian division fields

Let $K$ be an imaginary quadratic field, and let $\mathcal{O}_{K,f}$ be an order in $K$ of conductor $f\geq 1$. Let $E$ be an elliptic curve with CM by $\mathcal{O}_{K,f}$, such that $E$ is defined by a model over $\mathbb{Q}(j_{K,f})$, where $j_{K,f}=j(E)$. In this article, we classify the values of $N\geq 2$ and the elliptic curves $E$ such that (i) the division field $\mathbb{Q}(j_{K,f},E[N])$ is an abelian extension of $\mathbb{Q}(j_{K,f})$, and (ii) the $N$-division field coincides with the $N$-th cyclotomic extension of the base field.
An implicit Generalised Interpolation Material Point Method for large deformation and gradient elasto-plasticity

The ability to correctly capture large deformation behaviour in solids is important in many problems in geotechnical engineering such as slope failure or installation of foundations. The Material Point Method (MPM) is a computational method with particular suitability for modelling problems involving large deformations. In the MPM, a domain is modelled using a set of material points at which state variables are stored and tracked. These material points move through a fixed background grid upon which calculations take place with variables being mapped between the material points and the grid. This thesis sets out to develop the MPM as a method with potential for use in geotechnical problems. Problems are encountered with the original MPM when material points cross between grid cells, and one solution to this is the Generalised Interpolation Material Point (GIMP) method, where material points are able to influence nodes beyond the currently occupied grid cell. Most development of the GIMP method has used an explicit approach, however there are a number of advantages of an implicit approach including larger load steps and improved error control. This thesis focuses on the development of a large deformation elasto-plastic implicit GIMP method. A way of calculating the deformation gradient consistent with the MPM is introduced and convergence is demonstrated using this method which has previously been frequently omitted from MPM research. An alternative way of updating material point domains using the stretch tensor is also proposed. The MPM has a number of similarities to the FEM, and it is often suggested that FEM technologies are trivial to use with the MPM. The MPM can encounter localisations caused by shear banding and, to overcome this, a gradient plasticity approach previously implemented for the FEM is investigated with the GIMP method for the first time. The addition of gradient plasticity to the GIMP method introduces a length scale parameter which governs the width of these shear bands and removes the mesh dependency which is encountered with conventional approaches. It is shown that implementation is possible however, there are a number of problems that are present in the combination of the two methods which should not be overlooked in the future.
Reproductive and environmental traits explain the variation in egg size among Medusozoa (Cnidaria)

Medusozoa (Cnidaria) are characterized by diverse life cycles, with different semaphoronts (medusa, medusoid, fixed gonophore, polyp) representing the sexual phase and carrying the gametes. Although egg size is often considered a proxy to understand reproductive and developmental traits of medusozoans, understanding of the processes influencing egg size variation in the group under an evolutionary context is still limited. We carried out a comprehensive review of the variation of egg size in Medusozoa to test whether this variation is related to biological/sexual or environmental traits. Egg size presents a strong phylogenetic signal (λ = 0.79, K = 0.67), explaining why closely related species with different reproductive strategies and different individual sizes have similar egg sizes. However, variation in egg size is influenced by the number of eggs, depth and temperature, with larger eggs frequently present in species with few eggs (1–15), in deep-sea species and in cold-water species. Conversely, the production of small eggs among cold-water species of Staurozoa might be associated with the development of a small benthic larvae in this group. Our study reinforces that egg sizes respond to reproductive and environmental traits, although egg size is highly conserved within medusa classes.
Short wave instabilities of counter-rotating batchelor vortex pairs

Recently, investigations have considered the cooperative elliptic instability which forms by the mutual co-existence of a counter-rotating Batchelor-type vortex pair. Such vortex pairs are observed in the wake of an aircraft and pose a significant danger to trailing aircraft. An aircraft flying through the wake of a lead aircraft can exhibit significant loss of lift and control forces; several accidents and near-misses have been recorded. Our investigation has identified that short-wave Kelvin-type instabilities may prematurely destroy the vortex pair. By inducing shortwave instabilities there is the potential to significantly improve safety in the aviation industry. The work has relied heavily on CFD to validate analytical estimates and provide data for future experimental investigations.
Accuracy of a Smartphone Software Application Compared With a Handheld Goniometer for Measuring Shoulder Range of Motion in Asymptomatic Adults

Background: Typically, shoulder range of motion (ROM) measurements are performed in a clinic setting, where physicians, physical therapists (PTs), and occupational therapists make use of a goniometer. Purpose: To determine the accuracy of a smartphone-based ROM software application (app) in assessing active shoulder ROM and compare the measurements with traditional goniometry as measured by a PT. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: This prospective, nonblinded study was conducted at a single institution with adult asymptomatic participants with full active ROM of both shoulders. Participants were enrolled between June 1 and 15, 2021. Each participant self-assessed their active shoulder ROM using the PeerWell smartphone app. A single PT concurrently measured each participant’s active shoulder ROM using a handheld universal goniometer. Bilateral shoulder ROM (forward flexion, abduction [AB], external rotation, internal rotation [IR], and extension) was measured 2 times for each participant. Interrater reliability between the smartphone app and PT measurements as well as intrarater reliability for each method of measurement were assessed using the intraclass correlation coefficient (ICC), and limits of agreement were analyzed for the difference in measurement methods. Results: Data were analyzed for 60 shoulders (30 right, 30 left) from 30 participants (mean age, 31.4 ± 11.7 years; 73% female). The interrater reliability between the 2 methods was excellent for all movements (ICCs, 0.90-0.96). For all movements except shoulder AB, the mean difference in the measurements between the 2 methods was <1.3°; the mean difference in AB ROM was 2.08°. For all movements except IR, both PTs and the app showed excellent intrarater reliability (ICCs >0.90); for IR, good intrarater reliability (ICC ≥0.75) was observed. Conclusion: The PeerWell smartphone app provided measurements comparable with manual measurements taken by a PT using a goniometer. These data provide evidence that the smartphone app is a reliable and valid tool for measuring shoulder ROM and show promise for measuring and monitoring patient ROM remotely.
Clinical, Biomechanical, and Self-reported Health Status After ACL Reconstruction With Meniscal Repair in Soccer Players: Results at Minimum 1-Year Follow-up

Background: Performing meniscal repair with anterior cruciate ligament reconstruction (ACLR) has been shown to contribute to the long-term preservation of knee health and gait biomechanics. Purpose: To evaluate the role of meniscal repair in the performance of semiprofessional soccer players who returned to sport after ACLR. Study Design: Case series; Level of evidence, 4. Methods: This study included 51 male soccer players (mean ± SD age, 28.82 ± 5.33 years) who underwent ACLR at a single institution between July 2018 and July 2019. The players were divided into 3 groups according to surgery type: ACLR only (n = 30), ACLR with lateral meniscal repair (n = 9), and ACLR with medial meniscal repair (n = 12). Outcomes were evaluated through clinical examination, self-reported health questionnaires (Cincinnati Knee Rating System, Tegner activity score, Tegner Lysholm Knee Scoring Scale, Tampa Scale of Kinesiophobia, and ACL–Return to Sport After Injury), and biomechanical performance evaluations (balance, strength, coordination, and symmetry tests). Parametric and nonparametric tests were carried out for multiple comparisons. Results: The mean ± SD follow-up time was 20.75 ± 9.38 months. Although no significant differences emerged in clinical and self-reported health status, almost all the physical parameters tested resulted in lower performance in players treated with ACLR and meniscal repair. Moreover, patients with ACLR with lateral meniscal repair reported higher pain and fear of reinjury, with lower outcomes in terms of strength, symmetry, and coordination as compared with the other 2 groups. Balance abilities were significantly affected in players who underwent meniscal repair as compared with those who underwent ACLR only. Conclusion: The findings showed that biomechanical performance measures and fear of reinjury were significantly worse in soccer players with associated meniscal repair at a minimum 1-year follow-up, especially in those with a lateral meniscal tear.
Medication adherence among patients with cardiovascular diseases: a cross-sectional study

Background: Medication adherence has been recognised as one of the greatest areas to improve health outcomes and reduce health expenditure. Poor medication adherence has multifactorial causes that need to be understood. Aim: To determine the levels of medication adherence and examine the factors associated with medication adherence among patients with cardiovascular diseases (CVDs) in Jordan. Methods: A cross-sectional, descriptive correlational design was used to conduct this study. A convenience sample was used to recruit 250 patients with CVDs from outpatient clinics of Jordanian hospitals. Data were collected using the Multidimensional Scale of Perceived Social Support and Morisky Medication Adherence Scale, in addition to sociodemographic and clinical-related factors during the period from June to September 2019. Results: It was found that the total mean score of medication adherence was 2.84 (standard deviation = 1.9), which reflected low adherence. A significant positive correlation was found between medication adherence and marital status and the number of diseases. However, smoking was negatively correlated with medication adherence. Smoking was the predictor of medication adherence. Conclusions: Medication adherence among patients with CVDs needs to be improved. Thus, healthcare professionals should develop strategies and interventions based on identifying factors to enhance medication adherence among those patients.
Reduced-Order Modeling of Stratospheric Winds and Its Application in High-Altitude Balloon Trajectory Simulations

AbstractThe knowledge of weather conditions at the stratosphere is important for the planning and execution of high-altitude balloon flights, which require an accurate modeling of weather data over a period of time. Various methods based on statistical analysis, artificial neural networks, and cluster analysis have been employed to model the temporal variation of weather parameters. In the present study, a proper orthogonal decomposition (POD) method has been used to study the spatial as well as temporal variations of wind data in Singapore. The use of POD facilitates a compact representation of the weather dataset and aids in faster computation of wind profiles for use in balloon trajectory simulation. Further, the results reveal the existence of the quasi-biennial oscillation phenomenon, which is characteristic of equatorial easterly–westerly winds. This phenomenon enables the development of a Fourier prediction model, which can be used in real-time balloon trajectory simulations. The Fourier model is o.