Miracle-dz/UniFAR-FTUs huggingface dataset:
AI Does Not Alter Perceptions of Text Messages

For many people, anxiety, depression, and other social and mental factors can make composing text messages an active challenge. To remedy this problem, large language models (LLMs) may yet prove to be the perfect tool to assist users that would otherwise find texting difficult or stressful. However, despite rapid uptake in LLM usage, considerations for their assistive usage in text message composition have not been explored. A primary concern regarding LLM usage is that poor public sentiment regarding AI introduces the possibility that its usage may harm perceptions of AI-assisted text messages, making usage counter-productive. To (in)validate this possibility, we explore how the belief that a text message did or did not receive AI assistance in composition alters its perceived tone, clarity, and ability to convey intent. In this study, we survey the perceptions of 26 participants on 18 randomly labeled pre-composed text messages. In analyzing the participants' ratings of message tone, clarity, and ability to convey intent, we find that there is no statistically significant evidence that the belief that AI is utilized alters recipient perceptions. This provides hopeful evidence that LLM-based text message composition assistance can be implemented without the risk of counter-productive outcomes.
Rendering Graphs for Graph Reasoning in Multimodal Large Language Models

Large Language Models (LLMs) are increasingly used for various tasks with graph structures, such as robotic planning, knowledge graph completion, and common-sense reasoning. Though LLMs can comprehend graph information in a textual format, they overlook the rich visual modality, which is an intuitive way for humans to comprehend structural information and conduct graph reasoning. The potential benefits and capabilities of representing graph structures as visual images (i.e., visual graph) is still unexplored. In this paper, we take the first step in incorporating visual information into graph reasoning tasks and propose a new benchmark GITQA, where each sample is a tuple (graph, image, textual description). We conduct extensive experiments on the GITQA benchmark using state-of-the-art multimodal LLMs. Results on graph reasoning tasks show that combining textual and visual information together performs better than using one modality alone. Moreover, the LLaVA-7B/13B models finetuned on the training set (referred to as GITA), achieve higher accuracy than the closed-source model GPT-4(V). We also study the effects of augmentations in graph reasoning.
CoFiNet: Unveiling Camouflaged Objects with Multi-Scale Finesse

Camouflaged Object Detection (COD) is a critical aspect of computer vision aimed at identifying concealed objects, with applications spanning military, industrial, medical and monitoring domains. To address the problem of poor detail segmentation effect, we introduce a novel method for camouflage object detection, named CoFiNet. Our approach primarily focuses on multi-scale feature fusion and extraction, with special attention to the model's segmentation effectiveness for detailed features, enhancing its ability to effectively detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A multi-scale feature integration module is laveraged to enhance the model's capability of fusing context feature. A multi-activation selective kernel module is leveraged to grant the model the ability to autonomously alter its receptive field, enabling it to selectively choose an appropriate receptive field for camouflaged objects of different sizes. During mask generation, we employ the dual-mask strategy for image segmentation, separating the reconstruction of coarse and fine masks, which significantly enhances the model's learning capacity for details. Comprehensive experiments were conducted on four different datasets, demonstrating that CoFiNet achieves state-of-the-art performance across all datasets. The experiment results of CoFiNet underscore its effectiveness in camouflage object detection and highlight its potential in various practical application scenarios.
From Partial to Strictly Incremental Constituent Parsing

We study incremental constituent parsers to assess their capacity to output trees based on prefix representations alone. Guided by strictly left-to-right generative language models and tree-decoding modules, we build parsers that adhere to a strong definition of incrementality across languages. This builds upon work that asserted incrementality, but that mostly only enforced it on either the encoder or the decoder. Finally, we conduct an analysis against non-incremental and partially incremental models.
Quantum Normalizing Flows for Anomaly Detection

A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g. anomaly detection, once such a mapping has been learned. In this work we introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and evaluate our method on example datasets. Our proposed models show competitive performance for anomaly detection compared to classical methods, e.g. based on isolation forests, the local outlier factor (LOF) or single-class SVMs, while being fully executable on a quantum computer.
Transfer Learning in ECG Diagnosis: Is It Effective?

The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets. Yet the prevailing assumption that transfer learning consistently outperforms training from scratch has never been systematically validated. In this study, we conduct the first extensive empirical study on the effectiveness of transfer learning in multi-label ECG classification, by investigating comparing the fine-tuning performance with that of training from scratch, covering a variety of ECG datasets and deep neural networks. We confirm that fine-tuning is the preferable choice for small downstream datasets; however, when the dataset is sufficiently large, training from scratch can achieve comparable performance, albeit requiring a longer training time to catch up. Furthermore, we find that transfer learning exhibits better compatibility with convolutional neural networks than with recurrent neural networks, which are the two most prevalent architectures for time-series ECG applications. Our results underscore the importance of transfer learning in ECG diagnosis, yet depending on the amount of available data, researchers may opt not to use it, considering the non-negligible cost associated with pre-training.
SemPool: Simple, robust, and interpretable KG pooling for enhancing language models

Knowledge Graph (KG) powered question answering (QA) performs complex reasoning over language semantics as well as knowledge facts. Graph Neural Networks (GNNs) learn to aggregate information from the underlying KG, which is combined with Language Models (LMs) for effective reasoning with the given question. However, GNN-based methods for QA rely on the graph information of the candidate answer nodes, which limits their effectiveness in more challenging settings where critical answer information is not included in the KG. We propose a simple graph pooling approach that learns useful semantics of the KG that can aid the LM's reasoning and that its effectiveness is robust under graph perturbations. Our method, termed SemPool, represents KG facts with pre-trained LMs, learns to aggregate their semantic information, and fuses it at different layers of the LM. Our experimental results show that SemPool outperforms state-of-the-art GNN-based methods by 2.27% accuracy points on average when answer information is missing from the KG. In addition, SemPool offers interpretability on what type of graph information is fused at different LM layers.
AstroSat Observation of Recent Outburst in the Be/X-ray Binary LS V +4417/RX J0440.9+4431

A Be/X-ray binary system known as RX J0440.9+4431 (or LS V +44 17) is a potential member of the uncommon gamma-ray binary class. With an orbital period of 150 days, this system consists of a neutron star and a Be star companion. The MAXI observatory discovered an X-ray outburst from the source in December of 2022. Early in January, the outburst reached its peak, which was then followed by a decrease and a subsequent rebrightening. The X-ray flux exceeded 1 Crab in the 15-50 keV range at this second peak. AstroSat observations were conducted close to the peak of the second outburst, from January 11 to January 12, 2023. We report here the results of our search for 3-80 keV X-ray emission in the data of the AstroSat's LAXPC detector. It is found that the pulse period of the source is around 208 seconds. The source is found to be emitting more in the softer part of the X-ray energy range. The spectral characteristics can be described by employing a power-law model with an exponential cutoff, along with a blackbody component, interstellar absorption and an additional 6.4 keV iron fluorescence line.
OPSurv: Orthogonal Polynomials Quadrature Algorithm for Survival Analysis

This paper introduces the Orthogonal Polynomials Quadrature Algorithm for Survival Analysis (OPSurv), a new method providing time-continuous functional outputs for both single and competing risks scenarios in survival analysis. OPSurv utilizes the initial zero condition of the Cumulative Incidence function and a unique decomposition of probability densities using orthogonal polynomials, allowing it to learn functional approximation coefficients for each risk event and construct Cumulative Incidence Function estimates via Gauss--Legendre quadrature. This approach effectively counters overfitting, particularly in competing risks scenarios, enhancing model expressiveness and control. The paper further details empirical validations and theoretical justifications of OPSurv, highlighting its robust performance as an advancement in survival analysis with competing risks.
Jailbreaking Attack against Multimodal Large Language Model

This paper focuses on jailbreaking attacks against multi-modal large language models (MLLMs), seeking to elicit MLLMs to generate objectionable responses to harmful user queries. A maximum likelihood-based algorithm is proposed to find an \emph{image Jailbreaking Prompt} (imgJP), enabling jailbreaks against MLLMs across multiple unseen prompts and images (i.e., data-universal property). Our approach exhibits strong model-transferability, as the generated imgJP can be transferred to jailbreak various models, including MiniGPT-v2, LLaVA, InstructBLIP, and mPLUG-Owl2, in a black-box manner. Moreover, we reveal a connection between MLLM-jailbreaks and LLM-jailbreaks. As a result, we introduce a construction-based method to harness our approach for LLM-jailbreaks, demonstrating greater efficiency than current state-of-the-art methods. The code is available here. \textbf{Warning: some content generated by language models may be offensive to some readers. }
INVITED PAPER - SPECIAL SESSION ON "SIGNAL PROCESSING FOR RADAR IMAGING" OPTIMISED IMAGE AUTOFOCUSING FOR POLARIM ETRIC ISAR

The use of full polarisation enables multi-channel SAR processing for enhancing both imaging and classification capabilities. In the field of Inverse Synthetic Aperture Radar (ISAR) very little has been investigated, especially from the point of view of multi-channel ISAR image formation. In this paper, the authors want to define an optimised image autofocusing technique that exploits full polarisation information. Theory and simulation results will be provided in the paper.
Ecosystem Services and Their Economic Valuation

Ecosystem services are the conditions and processing through which natural ecosystems, and the species that make them up, sustain and fulfill human life. They not only supply to human with the production of ecosystem goods, such as food, forage, timber, biomass fuels, natural fiber, and pharmaceuticals. Ecosystems also perform fundamental life support services, which include the purification of air and water, detoxification and decomposition of wastes, regulation of climate, regeneration of soil fertility, and production and maintenance of biodiversity, mitigation of floods, droughts, the erosive forces of wind and rain. Today, escalating impacts of human activities on forests, wetlands, and other natural ecosystems imperil the delivery of such services. Now it is recognized that ecosystem services are essential to civilization and the core issue for sustainable development. In this paper, the advances in ecosystem services characteristics, their economic valuation and their relations to sustainable development are literaturally surveyed and discussed.
Influence of discharging foggy and its protection at Ertan hydropower station

The flood discharging rain and fog at Ertan hydropower station exerted harmful influence on operation of hydraulic structures which required better protection of downstream bank revetment. Test on flood discharging rain and fog was carried out and the protection analysis showed that the protection scope and method were sound and good effect was achieved, worthy reference.
VEHICULAR AD HOC NETWORKS (VANETs): ISSUES AND APPLICATIONS

Vehicular Ad-hoc Networks (VANETs) are subclass of Mobile ad-hoc Networks (MANETs) which aim to provide wireless communication among nearly vehicles and between vehicles. These systems are gaining importance in applications ranging from traffic safety, driver assistance, entrainment information services and internet access. The design of routing protocols is a key issue due to the dynamic nature of the nodes, which are moving vehicles in the network topology. VANETs are the key networking technologies for future vehicular communication even in Intelligent Transportation System (ITS). VANETs are distinguished from other networks due to their time-varying nature of vehicle density, high-mobility, and their self organizing behavior. In this paper, we investigate the current research issues pertaining to vehicular communication networks.
Targeting Hypoxia-Inducible Factor-1 (HIF-1) in Cancer: Emerging Therapeutic Strategies and Pathway Regulation

Hypoxia-inducible factor-1 (HIF-1) is a key regulator for balancing oxygen in the cells. It is a transcription factor that regulates the expression of target genes involved in oxygen homeostasis in response to hypoxia. Recently, research has demonstrated the multiple roles of HIF-1 in the pathophysiology of various diseases, including cancer. It is a crucial mediator of the hypoxic response and regulator of oxygen metabolism, thus contributing to tumor development and progression. Studies showed that the expression of the HIF-1α subunit is significantly upregulated in cancer cells and promotes tumor survival by multiple mechanisms. In addition, HIF-1 has potential contributing roles in cancer progression, including cell division, survival, proliferation, angiogenesis, and metastasis. Moreover, HIF-1 has a role in regulating cellular metabolic pathways, particularly the anaerobic metabolism of glucose. Given its significant and potential roles in cancer development and progression, it has been an intriguing therapeutic target for cancer research. Several compounds targeting HIF-1-associated processes are now being used to treat different types of cancer. This review outlines emerging therapeutic strategies that target HIF-1 as well as the relevance and regulation of the HIF-1 pathways in cancer. Moreover, it addresses the employment of nanotechnology in developing these promising strategies.
Tree height estimation in redwood/Douglas-fir stands in Mendocino County

In this study, height-diameter equations were developed for managed stands of coastal redwood/Douglas-fir stands in Mendocino County. Equations were developed by species to predict tree height as a function of diameter as well as other factors that are known to potentially explain tree height, including site class and live crown ratio. Two equation forms were compared and evaluated. Forest inventory data from Jackson Demonstration State Forest were used to fit the equations. These height-diameter equations will be useful in explaining height-diameter relationships in largely uneven-aged coastal redwood/Douglas-fir stands, and for predicting missing heights in forest inventories.
Etude isocinétique des muscles du genou chez les footballeurs de première division

Nous avons teste 11 footballeurs, evoluant en premiere division, sur dynamometre isocinetique. Trois vitesses de tests ont ete realisees sur le genou en flexion-extension : 90~ 1800 et 3000 par seconde. Nous avons chiffre les performances du quadriceps et des ischiojambiers a ces trois vitesses et calcule le ratio IJ /Q. En dernier lieu, nous avons analyse la forme des courbes et calcule l'index de fatigue a 3000 par seconde. Il en resulte que le ratio IJ /Q est particulierement bas et qu'il existe une inhibition non douloureuse des ischiojambiers a 1800 et 3000 par seconde. L'interet d'une telle evaluation est de proposer un entrainement specifique des ischio-jambiers, pour diminuer les risques d'accidents musculoligamentaires, au cours de la pratique sportive.
Community-assisted scientific assessment and management of Western Australian Marine Protected Areas. Rottnest Island Marine Reserve report for Coastwest and Caring for our Country

This report outlines key results of the Reef Life Survey Project Community-assisted scientific assessment and monitoring of Western Australian Marine Protected Areas, funded by a Coastwest grant, and run from February 2012 to February 2013 in partnership with the Department of Environment and Conservation, Rottnest Island Authority, and the Department of Fisheries, and with the support of the Perth Region NRM and Rangelands NRM.
QUANTIFICATION OF CSF VELOCITY THROUGH THE NARROWEST POINT IN AQUEDUCT OF SYLVIA FOR NORMAL AND NORMAL PRESSURE HYDROCEPHALUS PATIENT BY CFD ANALYSIS

The aim of this comprehensive study is to quantify the velocity variation of cerebrospinal fluid (CSF) for narrowest point in aqueduct of Sylvia (AqSylv) of normal patients and normal pressure hydrocephalus (NPH) patient by corresponds to its concave shapes of anteriorly and inferiorly. T1-weighted 3-T magnetic resonance images (MRI) of the head in DICOM (Digital Imaging and Communications in Medicine) format were taken from three controlled patients whose were admitted to Thammasat Hospital, Thailand. Patients were 29 to 52 y of age with two normal patients and one (NPH) patient. DICOM files were three-dimensionally reconstructed by using 3D slicer software, and geometric information of an aqueduct for all three cases was noted. Solid models of the aqueduct for both normal patient and NPH condition were developed based on the geometric information. Computational fluid dynamics (CFD) were analyzed to quantify the CSF velocity variation throughout the narrowest point of the aqueduct for both cases, i.e. normal and NPH condition. Retrospective results of “mathematical model for dynamics of CSF through the aqueduct of Sylvia based on an analogy of arterial dilation and contraction” were used as initial data for ANSYS CFX analysis. The results showed the CSF flow through the aqueduct in a pulsatile pattern in both cases. At the narrowest point of the aqueduct, amplitude of peak CSF velocity for NPH patients was significantly higher than that of normal patient. CSF velocity variation throughout the aqueduct co-relates with the pressure gradient inside the aqueduct and increased in the third ventricle direction.
Conservation in the growth and development of public aquaria

La protection des poissons et des habitats aquatiques est un concept nouveau. L'aquaculture organisee de poissons pour l'alimentation est connue depuis les temps anciens, et la culture ornementale puise ses origines dans l'Est asiatique. Les bassins d'agrement n'apparaissent dans les Jardins Zoologiques qu'aux environs de 1750 et les Aquariums pourvus de vitres, pour exposer les poissons et non les cultiver, vers 1850. La Conservation n'est pas alors un but, les poissons exposes etant remplaces systematiquement pour compenser les mortalites importantes qui se produisaient dans de tels bassins. L'hypothese etait que les ressources aquatiques naturelles etaient de toute facon infinies. Aujourd'hui, les recifs coralliens et d'autres habitats sont pollues ou aneantis, les pecheries s'effondrent et des especes de poissons sont en train de s'eteindre. L'industrie des Aquariums et les Aquariums publics beneficient d'un succes commercial manifeste, mais trop peu de ressources financieres sont encore investies dans des projets de conservation. Pour progresser, la Conservation doit etre mieux soutenue et financee.
Revisión bibliográfica TOLERANCIA A ESTRÉS POR DÉFICIT HÍDRICO EN TOMATE (Solanum lycopersicum L.)

Water is one of the most important factors for the plant growth, therefore, its deficiency is a major source of stress. This article reviews some aspects about different responses at morphological, physiological and biochemical levels in tomato plant. These responses include changes in growth, development, closure of stomata and changes in gene expression, including those encoding protective proteins, antioxidant and osmolyte synthesis enzymes and transcription factors that regulate stress induced gene expression itself, as well as new tools about functional genomics. It also included some aspects related to tomato plant breeding for drought tolerance. RESUMEN. El agua es uno de los factores mas importantes para el desarrollo de las plantas, por lo tanto, su carencia constituye una de las principales fuentes de estres. En este articulo se recogen algunos aspectos sobre las diferentes respuestas a nivel morfologico, fisiologico y bioquimico en tomate que le permite adaptarse o tolerar el estres por deficit hidrico. Estas respuestas incluyen modificaciones en el crecimiento, el desarrollo, cierre de estomas y cambios en la expresion de genes, incluyendo los que codifican proteinas protectoras, enzimas clave en la via de sintesis de osmolitos, enzimas antioxidantes y factores de transcripcion que regulan la expresion de genes inducida por el estres, asi como las nuevas herramientas de genomica funcional. Tambien se incluyen aspectos relacionados con la mejora para tolerancia a la sequia en el cultivo.
The Potential of Cyclodextrins as Inhibitors for the BM2 Protein: An In Silico Investigation

The influenza BM2 transmembrane domain (BM2TM), an acid-activated proton channel, is an attractive antiviral target due to its essential roles during influenza virus replication, whereas no effective inhibitors have been reported for BM2. In this study, we draw inspiration from the properties of cyclodextrins (CDs) and hypothesize that CDs of appropriate sizes may possess the potential to act as inhibitors of the BM2TM proton channel. To explore this possibility, molecular dynamics simulations were employed to assess their inhibitory capabilities. Our findings reveal that CD4, CD5, and CD6 are capable of binding to the BM2TM proton channel, resulting in disrupted water networks and reduced hydrogen bond occupancy between H19 and the solvent within the BM2TM channel necessary for proton conduction. Notably, CD4 completely obstructs the BM2TM water channel. Based on these observations, we propose that CD4, CD5, and CD6 individually contribute to diminishing the proton transfer efficiency of the BM2 protein, and CD4 demonstrates promising potential as an inhibitor for the BM2 proton channel.