Miracle-dz/UniFAR-FTUs huggingface dataset:
On the changes in long term streamflow regimes in the Prairies

ABSTRACT Climate change/variability accompanied by anthropogenic activities can alter the runoff response of landscapes. In this study we investigate the integrated impacts of precipitation change/variability and landscape changes, specifically wetland drainage practices, on streamflow regimes in wetland dominated landscapes in the Assiniboine and Saskatchewan River Basins of the North American Prairies. Precipitation and streamflow metrics were examined for gradual (trend type) and abrupt (shift type) changes using the modified Mann-Kendall trend test and a Bayesian change point detection methodology. Results of statistical analyses indicate that precipitation metrics did not experience statistically significant increasing or decreasing changes and there was no statistical evidence of streamflow regime change over the study area except for one of the smaller watersheds. The absence of widespread streamflow and precipitation change suggests that wetland drainage did not lead to detectable changes in streamflow metrics over most of the Canadian portion of the Prairies from 1967 to 2007. Keywords: Prairies; wetland drainage; streamflow; trend; shift; climate change
High mass function ellipsoidal variables in the Gaia Focused Product Release: searching for black hole candidates in the binary zoo

The recent Gaia Focused Product Release contains radial velocity time-series for more than 9,000 Gaia long-period photometric variables. Here we search for binary systems with large radial velocity amplitudes to identify candidates with massive, unseen companions. Eight targets have binary mass function $f(M)>1\ M_\odot$, three of which are eclipsing binaries. The remaining five show evidence of ellipsoidal modulations. We fit spectroscopic orbit models to the Gaia radial velocities, and fit the spectral energy distributions of three targets. For the two systems most likely to host dark companions, J0946 and J1640, we use PHOEBE to fit the ASAS-SN light curves and Gaia radial velocities. The derived companion masses are $>3 M_\odot$, but the high Galactic dust extinctions towards these objects limit our ability to rule out main sequence companions or subgiants hotter than the photometric primaries. These systems are similar to other stellar-mass black hole impostors, notably the Unicorn (V723 Mon) and the Giraffe (2M04123153$+$6738486). While it is possible that J1640 and J0946 are similar examples of stripped giant star binaries, high-resolution spectra can be used to determine the nature of their companions.
Well-posedness and decay for a nonlinear propagation wave model in atmospheric flows

In this note, we provide two results concerning the global well-posedness and decay of solutions to an asymptotic model describing the nonlinear wave propagation in the troposphere, namely, the morning glory phenomenon. The proof of the first result combines a pointwise estimate together with some interpolation inequalities to close the energy estimates in Sobolev spaces. The second proof relies on suitable Wiener-like functional spaces.
Towards Generative Abstract Reasoning: Completing Raven's Progressive Matrix via Rule Abstraction and Selection

Endowing machines with abstract reasoning ability has been a long-term research topic in artificial intelligence. Raven's Progressive Matrix (RPM) is widely used to probe abstract visual reasoning in machine intelligence, where models will analyze the underlying rules and select one image from candidates to complete the image matrix. Participators of RPM tests can show powerful reasoning ability by inferring and combining attribute-changing rules and imagining the missing images at arbitrary positions of a matrix. However, existing solvers can hardly manifest such an ability in realistic RPM tests. In this paper, we propose a deep latent variable model for answer generation problems through Rule AbstractIon and SElection (RAISE). RAISE can encode image attributes into latent concepts and abstract atomic rules that act on the latent concepts. When generating answers, RAISE selects one atomic rule out of the global knowledge set for each latent concept to constitute the underlying rule of an RPM. In the experiments of bottom-right and arbitrary-position answer generation, RAISE outperforms the compared solvers in most configurations of realistic RPM datasets. In the odd-one-out task and two held-out configurations, RAISE can leverage acquired latent concepts and atomic rules to find the rule-breaking image in a matrix and handle problems with unseen combinations of rules and attributes.
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation

Nuclei instance segmentation in histopathological images is of great importance for biological analysis and cancer diagnosis but remains challenging for two reasons. (1) Similar visual presentation of intranuclear and extranuclear regions of chromophobe nuclei often causes under-segmentation, and (2) current methods lack the exploration of nuclei structure, resulting in fragmented instance predictions. To address these problems, this paper proposes a structure encoding and interaction network, termed SEINE, which develops the structure modeling scheme of nuclei and exploits the structure similarity between nuclei to improve the integrality of each segmented instance. Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure. Based on the encoding, we propose a structure-guided attention (SGA) module that takes the clear nuclei as prototypes to enhance the structure learning for the fuzzy nuclei. To strengthen the structural learning ability, a semantic feature fusion (SFF) is presented to boost the semantic consistency of semantic and structure branches. Furthermore, a position enhancement (PE) method is applied to suppress incorrect nuclei boundary predictions. Extensive experiments demonstrate the superiority of our approaches, and SEINE achieves state-of-the-art (SOTA) performance on four datasets. The code is available at https://github.com/zhangye-zoe/SEINE.
A Quantile Nelson-Siegel model

A widespread approach to modelling the interaction between macroeconomic variables and the yield curve relies on three latent factors usually interpreted as the level, slope, and curvature (Diebold et al., 2006). This approach is inherently focused on the conditional mean of the yields and postulates a dynamic linear model where the latent factors smoothly change over time. However, periods of deep crisis, such as the Great Recession and the recent pandemic, have highlighted the importance of statistical models that account for asymmetric shocks and are able to forecast the tails of a variable's distribution. A new version of the dynamic three-factor model is proposed to address this issue based on quantile regressions. The novel approach leverages the potential of quantile regression to model the entire (conditional) distribution of the yields instead of restricting to its mean. An application to US data from the 1970s shows the significant heterogeneity of the interactions between financial and macroeconomic variables across different quantiles. Moreover, an out-of-sample forecasting exercise showcases the proposed method's advantages in predicting the yield distribution tails compared to the standard conditional mean model. Finally, by inspecting the posterior distribution of the three factors during the recent major crises, new evidence is found that supports the greater and longer-lasting negative impact of the great recession on the yields compared to the COVID-19 pandemic.
Large Language Models for Scientific Information Extraction: An Empirical Study for Virology

In this paper, we champion the use of structured and semantic content representation of discourse-based scholarly communication, inspired by tools like Wikipedia infoboxes or structured Amazon product descriptions. These representations provide users with a concise overview, aiding scientists in navigating the dense academic landscape. Our novel automated approach leverages the robust text generation capabilities of LLMs to produce structured scholarly contribution summaries, offering both a practical solution and insights into LLMs’ emergent abilities.For LLMs, the prime focus is on improving their general intelligence as conversational agents. We argue that these models can also be applied effectively in information extraction (IE), specifically in complex IE tasks within terse domains like Science. This paradigm shift replaces the traditional modular, pipelined machine learning approach with a simpler objective expressed through instructions. Our results show that finetuned FLAN-T5 with 1000x fewer parameters than the state-of-the-art GPT-davinci is competitive for the task.
Les diatomées subfossiles de la mare de Thiaroye (Dakar-Sénégal) : Inventaire floristique et paléoenvironnement

L'etude de la microflore diatomique subfossile de la mare de Thiaroye situee dans l'agglomeration dakaroise, le long de deux carottes de sondage, a permis d'inventorier 87 especes et varietes appartenant a 27 genres. Parmi elles, 9 sont signalees pour la premiere fois au Senegal et une pour la premiere fois en Afrique. D'un point de vue paleoenvironnemental, la profondeur de la mare est restee faible depuis sa formation a la fin de la transgression dakarienne (2000 BP). Sa proximite avec l'Ocean Atlantique a eu une double repercussion: transfert de Diatomees marines a saumâtres par le vent et les Oiseaux, salinisation progressive de l'eau par la montee du biseau sale. La destruction progressive de la vegetation naturelle autour et dans la mare par l'homme au profit du maraichage a provoque une alcalinite croissante du milieu. D'un point de vue phytogeographique, l'espece Cyclotella alvarniensis est identifiee pour la premiere fois en Afrique. Son âge miocene superieur merite d'etre reexamine.
Cross-Modality Perturbation Synergy Attack for Person Re-identification

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on two widely used cross-modality datasets, namely RegDB and SYSU, which not only demonstrated the effectiveness of our method but also provided insights for future enhancements in the robustness of cross-modality ReID systems.
Temporal Insight Enhancement: Mitigating Temporal Hallucination in Multimodal Large Language Models

Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced the comprehension of multimedia content, bringing together diverse modalities such as text, images, and videos. However, a critical challenge faced by these models, especially when processing video inputs, is the occurrence of hallucinations - erroneous perceptions or interpretations, particularly at the event level. This study introduces an innovative method to address event-level hallucinations in MLLMs, focusing on specific temporal understanding in video content. Our approach leverages a novel framework that extracts and utilizes event-specific information from both the event query and the provided video to refine MLLMs' response. We propose a unique mechanism that decomposes on-demand event queries into iconic actions. Subsequently, we employ models like CLIP and BLIP2 to predict specific timestamps for event occurrences. Our evaluation, conducted using the Charades-STA dataset, demonstrates a significant reduction in temporal hallucinations and an improvement in the quality of event-related responses. This research not only provides a new perspective in addressing a critical limitation of MLLMs but also contributes a quantitatively measurable method for evaluating MLLMs in the context of temporal-related questions.
Business and ethical concerns in domestic Conversational Generative AI-empowered multi-robot systems

Business and technology are intricately connected through logic and design. They are equally sensitive to societal changes and may be devastated by scandal. Cooperative multi-robot systems (MRSs) are on the rise, allowing robots of different types and brands to work together in diverse contexts. Generative artificial intelligence has been a dominant topic in recent artificial intelligence (AI) discussions due to its capacity to mimic humans through the use of natural language and the production of media, including deep fakes. In this article, we focus specifically on the conversational aspects of generative AI, and hence use the term Conversational Generative artificial intelligence (CGI). Like MRSs, CGIs have enormous potential for revolutionizing processes across sectors and transforming the way humans conduct business. From a business perspective, cooperative MRSs alone, with potential conflicts of interest, privacy practices, and safety concerns, require ethical examination. MRSs empowered by CGIs demand multi-dimensional and sophisticated methods to uncover imminent ethical pitfalls. This study focuses on ethics in CGI-empowered MRSs while reporting the stages of developing the MORUL model.
Editorial note to Jean-Marie Souriau's"On the motion of spinning particles in general relativity"

The gravitational interaction of (classical and quantum) spinning bodies is currently the focus of many works using a variety of approaches. This note is a comment on a short paper by Jean-Marie Souriau, now reprinted in the GRG Golden Oldies collection. Souriau's short 1970 note was a pioneering contribution to a symplectic description of the dynamics of spinning particles in general relativity which remained somewhat unnoticed. We explain the specificity of Souriau's approach and emphasize its potential interest within the current flurry of activity on the gravitational interaction of spinning particles.
Enhancing Surveillance Camera FOV Quality via Semantic Line Detection and Classification with Deep Hough Transform

The quality of recorded videos and images is significantly influenced by the camera's field of view (FOV). In critical applications like surveillance systems and self-driving cars, an inadequate FOV can give rise to severe safety and security concerns, including car accidents and thefts due to the failure to detect individuals and objects. The conventional methods for establishing the correct FOV heavily rely on human judgment and lack automated mechanisms to assess video and image quality based on FOV. In this paper, we introduce an innovative approach that harnesses semantic line detection and classification alongside deep Hough transform to identify semantic lines, thus ensuring a suitable FOV by understanding 3D view through parallel lines. Our approach yields an effective F1 score of 0.729 on the public EgoCart dataset, coupled with a notably high median score in the line placement metric. We illustrate that our method offers a straightforward means of assessing the quality of the camera's field of view, achieving a classification accuracy of 83.8\%. This metric can serve as a proxy for evaluating the potential performance of video and image quality applications.
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction

A rational design of new therapeutic drugs aims to find a molecular structure with desired biological functionality, e.g., an ability to activate or suppress a specific protein via binding to it. Molecular docking is a common technique for evaluating protein-molecule interactions. Recently, Reinforcement Learning (RL) has emerged as a promising approach to generating molecules with the docking score (DS) as a reward. In this work, we reproduce, scrutinize and improve the recent RL model for molecule generation called FREED (arXiv:2110.01219). Extensive evaluation of the proposed method reveals several limitations and challenges despite the outstanding results reported for three target proteins. Our contributions include fixing numerous implementation bugs and simplifying the model while increasing its quality, significantly extending experiments, and conducting an accurate comparison with current state-of-the-art methods for protein-conditioned molecule generation. We show that the resulting fixed model is capable of producing molecules with superior docking scores compared to alternative approaches.
Eye Motion Matters for 3D Face Reconstruction

Recent advances in single-image 3D face reconstruction have shown remarkable progress in various applications. Nevertheless, prevailing techniques tend to prioritize the global facial contour and expression, often neglecting the nuanced dynamics of the eye region. In response, we introduce an Eye Landmark Adjustment Module, complemented by a Local Dynamic Loss, designed to capture the dynamic features of the eyes area. Our module allows for flexible adjustment of landmarks, resulting in accurate recreation of various eye states. In this paper, we present a comprehensive evaluation of our approach, conducting extensive experiments on two datasets. The results underscore the superior performance of our approach, highlighting its significant contributions in addressing this particular challenge.
A mode-multiplexed photonic integrated vector dot-product core from inverse design

Photonic computing has the potential of harnessing the full degrees of freedom (DOFs) of the light field, including wavelength, spatial mode, spatial location, phase quadrature, and polarization, to achieve higher level of computation parallelization and scalability than digital electronic processors. While multiplexing using wavelength and other DOFs can be readily integrated on silicon photonics platforms with compact footprints, conventional mode-division multiplexed (MDM) photonic designs occupy areas exceeding tens to hundreds of microns for a few spatial modes, significantly limiting their scalability. Here we utilize inverse design to demonstrate an ultracompact photonic computing core that calculates vector dot-products based on MDM coherent mixing within a nominal footprint of 5 um x 3 um. Our dot-product core integrates the functionalities of 2 mode multiplexers and 1 multi-mode coherent mixers, all within the footprint, and could be applied to various computation and computer vision tasks, with high computing throughput density. We experimentally demonstrate computing examples on the fabricated core, including complex number multiplication and motion estimation using optical flow.
Transfer Learning in Human Activity Recognition: A Survey

Sensor-based human activity recognition (HAR) has been an active research area, owing to its applications in smart environments, assisted living, fitness, healthcare, etc. Recently, deep learning based end-to-end training has resulted in state-of-the-art performance in domains such as computer vision and natural language, where large amounts of annotated data are available. However, large quantities of annotated data are not available for sensor-based HAR. Moreover, the real-world settings on which the HAR is performed differ in terms of sensor modalities, classification tasks, and target users. To address this problem, transfer learning has been employed extensively. In this survey, we focus on these transfer learning methods in the application domains of smart home and wearables-based HAR. In particular, we provide a problem-solution perspective by categorizing and presenting the works in terms of their contributions and the challenges they address. We also present an updated view of the state-of-the-art for both application domains. Based on our analysis of 205 papers, we highlight the gaps in the literature and provide a roadmap for addressing them. This survey provides a reference to the HAR community, by summarizing the existing works and providing a promising research agenda.
Design of Initial Guess Low Thrust Trajectories Using Clohessy-Wiltshire Equations

The commercial interest in producing low-cost space missions by exploiting the superior propellant management of low-thrust propulsion technology has become increasingly popular. Typical to such missions is the design of transfer trajectories between desired targets. This is a complex and computationally expensive process. Additionally, the optimal solvers used to generate these trajectories are extremely sensitive to initial guesses. One way to overcome this challenge is to use a reasonably approximate trajectory as an initial guess on optimal solvers. This paper presents a flexible approach to generating very low thrust trajectories. The initial guess is obtained from a flexible semi-analytic approach that can provide both planar and three-dimensional initial guess trajectories for various design scenarios like orbit raising, orbit insertion, phasing, and rendezvous. NASA's Evolutionary Mission Trajectory Generator (EMTG) and General Mission Analysis Tool (GMAT) are used as optimal solvers in this analysis. Numerical case studies are presented in this paper.
On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter-Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulnerability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.