Miracle-dz/UniFAR-FTUs huggingface dataset:
Storm Surge Heights and Damage Caused by the 2013 Typhoon Haiyan Along the Leyte Gulf Coast

Typhoon Haiyan (Yolanda) struck the islands of Leyte and Samar, the central part of the Philippines, on 8 November 2013, bringing large-scale devastation to coastal areas due to the high winds and large storm surge and waves associated with it. In order to obtain the distribution of storm surge heights and damage covering the wide stretch of affected coastline, the authors carried out a series of field surveys in the aftermath of the typhoon (in December 2013, May 2014, and October 2014). In the present paper, the authors detailed the results of these field surveys and summarized the characteristics of the storm surge and main causes of the damage, especially focusing on the Leyte Gulf coast, which is surrounded by the eastern coast of Leyte and the southern coast of Samar. Finally, the wide range of types of storm surge disasters were also discussed by comparing Typhoon Haiyan with other recent major events.
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment

In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets. Notably, the alignment mechanism in FFA endows feedback connections with emergent visual inference functions, including denoising, resolving occlusions, hallucination, and imagination. Moreover, FFA offers bio-plausibility compared to traditional back-propagation (BP) methods in implementation. By repurposing the computational graph of credit assignment into a goal-driven feedback pathway, FFA alleviates weight transport problems encountered in BP, enhancing the bio-plausibility of the learning algorithm. Our study presents FFA as a promising proof-of-concept for the mechanisms underlying how feedback connections in the visual cortex support flexible visual functions. This work also contributes to the broader field of visual inference underlying perceptual phenomena and has implications for developing more biologically inspired learning algorithms.
Triple Loss Adversarial Domain Adaptation Network for Cross-Domain Sea–Land Clutter Classification

The existing sea–land clutter classification task of sky-wave over-the-horizon-radar (OTHR) assumes that the training data and test data are drawn from the same probability distribution. However, there is a distribution discrepancy/domain shift of the collected sea–land clutter under various working conditions of OTHR, which leads to the advanced sea–land clutter classification methods being difficult to achieve effective cross-domain classification. To solve this problem, this article proposes an improved maximum classifier discrepancy (MCD) framework, namely, triple loss adversarial domain adaptation network (TLADAN) for cross-domain sea–land clutter classification, which includes a metric-based feature-level loss, an adversarial-based instance-level loss, and an adversarial-based class-level loss. The proposed TLADAN performs feature-, instance-, and class-level alignments of the sea–land clutter from different domains, so as to learn the domain-invariant features to improve the classification performance in the cross-domain scenario. Our method is evaluated in six sea–land clutter domain adaptation (DA) scenarios. Meanwhile, state-of-the-art DA methods are selected for comparison. The experimental results validate the effectiveness and superiority of TLADAN.
A Risk-Averse Framework for Non-Stationary Stochastic Multi-Armed Bandits

In a typical stochastic multi-armed bandit problem, the objective is often to maximize the expected sum of rewards over some time horizon T. While the choice of a strategy that accomplishes that is optimal with no additional information, it is no longer the case when provided additional environment-specific knowledge. In particular, in areas of high volatility like healthcare or finance, a naive reward maximization approach often does not accurately capture the complexity of the learning problem and results in unreliable solutions. To tackle problems of this nature, we propose a framework of adaptive risk-aware strategies that operate in non-stationary environments. Our framework incorporates various risk measures prevalent in the literature to map multiple families of multi-armed bandit algorithms into a risk-sensitive setting. In addition, we equip the resulting algorithms with the Restarted Bayesian Online Change-Point Detection (R-BOCPD) algorithm and impose a (tunable) forced exploration strategy to detect local (per-arm) switches. We provide finite-time theoretical guarantees and an asymptotic regret bound of $\widetilde {\mathcal{O}}\left( {\sqrt {{K_T}T} } \right)$ up to time horizon T with KT the total number of change-points. In practice, our framework compares favorably to the state-of-the-art in both synthetic and real-world environments and manages to perform efficiently with respect to both risk-sensitivity and non-stationarity.
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics

Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies. However, many existing calibration methods are specialized for post-hoc recalibration, which can worsen the sharpness of forecasts. Drawing on the insight that calibration can be viewed as a distribution matching task, we introduce kernel-based calibration metrics that unify and generalize popular forms of calibration for both classification and regression. These metrics admit differentiable sample estimates, making it easy to incorporate a calibration objective into empirical risk minimization. Furthermore, we provide intuitive mechanisms to tailor calibration metrics to a decision task, and enforce accurate loss estimation and no regret decisions. Our empirical evaluation demonstrates that employing these metrics as regularizers enhances calibration, sharpness, and decision-making across a range of regression and classification tasks, outperforming methods relying solely on post-hoc recalibration.
Automatic Recognition of Emotions in Speech With Large Self-Supervised Learning Transformer Models

Speech Emotion Recognition (SER) is an important area of research in the realm of collaborative and social robotics, which aims to enhance human-robot interaction (HRI) and serves as a feedback mechanism for affective computing. Despite the recent progress in SER research area, it remains a challenging research problem due to the profound variations in the complexity, subjectivity, and contextual heterogeneity of human emotional expressions. Consequently, the inherent difficulties of modeling paralinguistic emotional information embedded in speech signals are further compounded when employing supervised learning, as it necessitates annotated labels for a large-scale dataset for satisfactory model performance. To this end, self-supervised learning (SSL) approach is widely adopted in the speech domain to addresses this problem of limited availability of annotated data.Therefore, the focus of our research is to investigate and evaluate several state-of-the-art large attention-based self-supervised learning (SSL) models for the task of automatic speech emotion recognition (SER) on the challenging RAVDESS dataset. Results of the four large SSL models on the RAVDESS dataset are promising. In particular HuBERT large model achieved highest accuracy of 88% with a much lower training time and lower model size on disk compared to the rest of the models.
Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review

Deep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to gather the approaches and summarize the existing models in this field. However, a state-of-the-art review paper is still scarce in this field that will present a bibliometric analysis as well as a critical analysis of the recent works. Therefore, this article aims to spur the researchers with a bibliometric analysis to identify the current research trend. As a research sample, in total, 281 related papers were collected from the Web of Science source, and bibliometric analysis was accomplished using VOSviewer software. Among the collection of associated works from the database, 28 papers were selected according to the defined criteria for detailed analysis. Besides this, a few research questions were generated to extract necessary information from the literature for extracting the pros and cons of the selected works. DL techniques were applied in these works and achieved results. Furthermore, the papers were also categorized based on the addressed RS application domain.
Erato: Automatizing Poetry Evaluation

We present Erato, a framework designed to facilitate the automated evaluation of poetry, including that generated by poetry generation systems. Our framework employs a diverse set of features, and we offer a brief overview of Erato's capabilities and its potential for expansion. Using Erato, we compare and contrast human-authored poetry with automatically-generated poetry, demonstrating its effectiveness in identifying key differences. Our implementation code and software are freely available under the GNU GPLv3 license.
DETERMINATION OF MACROSEISMIC EPICENTRE AND RUPTURE BEGINNING CAUSED BY WEST PASS OF KUNLUNSHAN EARTHQUAKE WITH Ms8.1

Expeditionary researches of the great earthquake with Ms 8.1 by famous schoolars are analyzed comparatively,and combined with four times investigations by the authors,the new macroseismic epicentre and the rupture beginning are determined at east-southern 3km of Bukadaban Peak.This is agreed with the microseismic epicentre determined by China digital seismic network.
Effective Invasiveness Recognition of Imbalanced Data by Semi-Automated Segmentations of Lung Nodules

Over the past few decades, recognition of early lung cancers was researched for effective treatments. In early lung cancers, the invasiveness is an important factor for expected survival rates. Hence, how to effectively identify the invasiveness by computed tomography (CT) images became a hot topic in the field of biomedical science. Although a number of previous works were shown to be effective on this topic, there remain some problems unsettled still. First, it needs a large amount of marked data for a better prediction, but the manual cost is high. Second, the accuracy is always limited in imbalance data. To alleviate these problems, in this paper, we propose an effective CT invasiveness recognizer by semi-automated segmentation. In terms of semi-automated segmentation, it is easy for doctors to mark the nodules. Just based on one clicked pixel, a nodule object in a CT image can be marked by fusing two proposed segmentation methods, including thresholding-based morphology and deep learning-based mask region-based convolutional neural network (Mask-RCNN). For thresholding-based morphology, an initial segmentation is derived by adaptive pixel connections. Then, a mathematical morphology is performed to achieve a better segmentation. For deep learning-based mask-RCNN, the anchor is fixed by the clicked pixel to reduce the computational complexity. To incorporate advantages of both, the segmentation is switched between these two sub-methods. After segmenting the nodules, a boosting ensemble classification model with feature selection is executed to identify the invasiveness by equalized down-sampling. The extensive experimental results on a real dataset reveal that the proposed segmentation method performs better than the traditional segmentation ones, which can reach an average dice improvement of 392.3%. Additionally, the proposed ensemble classification model infers better performances than the compared method, which can reach an area under curve (AUC) improvement of 5.3% and a specificity improvement of 14.3%. Moreover, in comparison with the models with imbalance data, the improvements of AUC and specificity can reach 10.4% and 33.3%, respectively.
MEASUREMENTS FOR SPATIAL ACCESSIBILITY OF NATIONAL FOREST PARKS IN CHINA

Low carbon economy,sustainable development and employment problems have been the focus of world attention.With the prosperity of ecotourism,the forest park in China gets its all-around development.Forest park has been a leading role in the development of forest tourism industry.Through the development of forest tourism in forest park,people's awareness of ecological protection was improved,and economic development in the surrounding was promoted,which has an increasingly important role in promoting regional economic development.The study of the spatial structure of forest park is receiving increasing attention but methodology so far has used qualitative rather than quantitative methods.The change of accessibility plays a prominent role of motive force to promote the regional economic development and regional spatial structure changes.Evaluation of accessibility generally includes network analysis,grid analysis and raster analysis.Based on an investigation of 713National Forest Parks and using GIS and some quantitative analysis methods,such as Nearest Neighbor Index(NNI)and Hot spot clustering,the spatial structure of National Forest Parks was investigated,and their characteristics and distribution for different strategies were discussed.Based on matrix raster data covering the whole space,this paper calculated spatial accessibility of all counties in China using cost weighted distance method and ArcGIS as platforms.Then spatial differences of county accessibility of scenic spots were discussed by using ESDA(Exploratory Spatial Data Analysis). The results show that general National Forest Parks exhibited an aggregated distribution.Considering the accessibility,we find that the human scenic spots were more centralized.The average accessibility was about 60.5min,and the area where the accessibility of scenic spots was within 2hreached 63.29%,while the area where the accessibility was within 30minutes accounted for 19.84% and the area located at central Tibetan Plateau took the longest time 595min.The average accessibility was shortened from 168.18min in 1991to 137.86min in 2010.And then,distribution of the accessibility pointed to traffic line.At county level,the estimated values of Moran's I were all positive numbers using analysis of spatial association.All the test results indicate that National Forest Park and adjacent areas showed positive correlation.Distribution of hot spots regarding the accessibility showed an obvious hot spots–sub-hotspots– sub-cold spots-cold spots zonal distribution pattern from east to west.
Design, Validation, and Reliability of a Basketball Skill and Performance Test Instrument in Adolescent Players

Study purpose. The assessment tools or skills tests, such as the AAHPERD basketball skill test and the Johnson basketball test, are all partial or separate. All these tests are old products, while the game of basketball is constantly evolving. The purpose of this study was to design and validate basketball skills and performance tests for players aged 14–16 years to shorten the test time, shorten the waiting time for results, and be comprehensive so that they can reflect real abilities and can be used as a reference when carrying out selection and evaluation. Materials and methods. A total of 7 experts assessed the 11 elements (86 items) of the basketball skill and performance test (BSPT). Three academic experts and four basketball coach experts took part in the study. Data was collected using the Delphi technique and test-retest, and data was analyzed using Aiken’s formula, intraclass correlation coefficients (ICC), and Cronbach’s alpha. Results. With a value of 0.91, the expert assessment tool shows that the content validity of the aspect is good. Cronbach’s alpha was used to measure the reliability of every aspect of the basketball skill and performance test instrument, and the results were 0.918. The result of the ICC score agreement between raters is 0.912, while for one rater, the consistency is 0.596. Conclusions. The implications of this study could prove that the BSPT can be used to assess the skill level of adolescent basketball players, benefiting not only basketball coaches but also physical education teachers.
Does GPT-4 Pass the Turing Test?

We evaluated GPT-4 in a public online Turing Test. The best-performing GPT-4 prompt passed in 41% of games, outperforming baselines set by ELIZA (27%) and GPT-3.5 (14%), but falling short of chance and the baseline set by human participants (63%). Participants' decisions were based mainly on linguistic style (35%) and socio-emotional traits (27%), supporting the idea that intelligence is not sufficient to pass the Turing Test. Participants' demographics, including education and familiarity with LLMs, did not predict detection rate, suggesting that even those who understand systems deeply and interact with them frequently may be susceptible to deception. Despite known limitations as a test of intelligence, we argue that the Turing Test continues to be relevant as an assessment of naturalistic communication and deception. AI models with the ability to masquerade as humans could have widespread societal consequences, and we analyse the effectiveness of different strategies and criteria for judging humanlikeness.
The Influence of Different Types of Switching Sequences in Space-Vector PWM on Output Characteristics of Three-Level Voltage Inverter

This article studies the impact of various switching sequence types on the operation of a three-level neutral point clamped voltage source inverter during space-vector PWM. The authors reviewed three common sequences: standard, seven-stage, and five-stage. The sequences were compared using four criteria: the quality of the inverter output curve, the balance of the neutral point voltage, the common-mode voltage, and the mitigation of the switching losses. To assess the data for three of the sequences, the authors developed a computer model using Matlab & Simulink. The analysis of the results helps select the best switching sequence taking into account the operational requirements of the inverter, while the seven-stage sequence assures acceptable results for all four of the criteria used. The results provided in this article can be used for the design of electric drive systems that require high efficiency and reliability, as well as further research on three-level voltage inverters.
The effect of fish waste and duck manure on the growth and yield of pak choi

: In 2021, pak choi production in Indonesia was 727.47 Mg, marking an increase of 8.2% compared to the 2020 production, which was 667.47 Mg. Therefore, there is a clear need for cultivation improvement, particularly through the implementation of organic fertilisers. This study aimed to investigate the impact of liquid organic fertiliser (LOF) derived from fish waste and duck manure on the growth and yield of the pak choi plant ( Brassica rapa . L. var. Nauli F1). A randomised block design factorial was used with two factors and three replications. The first factor considered was LOF from fish waste, comprising three levels (LOF 0 = control, LOF 1 = 25 cm 3 ∙dm –3 of water, and LOF 2 = 50 cm 3 ∙dm –3 of water). The second factor focused on duck manure fertiliser (DMF) and involved four levels (DMF 0 = control, DMF 1 = 3.7 kg∙plot –1 , DMF 2 = 5.55 kg∙plot –1 , and DMF 3 = 7.4 kg∙plot –1 ). The results showed that the application of LOF from fish waste positively influenced the growth and yield of pak choi, with the most effective treatment observed in LOF 1 (25 cm 3 ∙dm –3 of water). However, the application of DMF did not yield a significant difference in its effect on the growth and yield of the pak choi plant. The control treatment (DMF 0 ) reported comparable results and the combination of LOF from fish waste and DMF did not show a significant effect, with the most favourable findings observed in the LOF 2 DMF 0 treatment (50 cm 3 ∙dm –3 and control).
High Sensitivity Milk Adulteration Detector Using Fractional Order Colpitts Oscillator

Milk samples with suitable electrodes behave as fractional order (FO) elements whose order $\alpha$ change with types of adulteration present in milk and their concentrations. In this work, a milk sample is taken as the FO element in an FO Colpitts oscillator (FOCO). Therefore, with the change in adulteration, the order changes resulting in a change in output frequency of FOCO. The paper presents the design of a milk adulteration detector using an FOCO and an FV converter and studies its calibration and sensitivity. It is found that the proposed meter has 6.5 times higher sensitivity and 6 times smaller resolution than the earlier reported meter. The proposed meter achieved an LOD of 3.0% in the case of tap water, 0.2 mg in urea and 4.4% in whey.
The Role of Soluble Adenylyl Cyclase in the Regulation of Flagellar Motility in Ascidian Sperm

Flagellar motility in sperm is activated and regulated by factors related to the eggs at fertilization. In the ascidian Ciona intestinalis, a sulfated steroid called the SAAF (sperm activating and attracting factor) induces both sperm motility activation and chemotaxis. Cyclic AMP (cAMP) is one of the most important intracellular factors in the sperm signaling pathway. Adenylyl cyclase (AC) is the key enzyme that synthesizes cAMP at the onset of the signaling pathway in all cellular functions. We previously reported that both transmembrane AC (tmAC) and soluble AC (sAC) play important roles in sperm motility in Ciona. The tmAC plays a major role in the SAAF-induced activation of sperm motility. On the other hand, sAC is involved in the regulation of flagellar beat frequency and the Ca2+-dependent chemotactic movement of sperm. In this study, we focused on the role of sAC in the regulation of flagellar motility in Ciona sperm chemotaxis. The immunochemical analysis revealed that several isoforms of sAC protein were expressed in Ciona sperm, as reported in mammals and sea urchins. We demonstrated that sAC inhibition caused strong and transient asymmetrization during the chemotactic turn, and then sperm failed to turn toward the SAAF. In addition, real-time Ca2+ imaging in sperm flagella revealed that sAC inhibition induced an excessive and prolonged Ca2+ influx to flagella. These results indicate that sAC plays a key role in sperm chemotaxis by regulating the clearance of [Ca2+]i and by modulating Ca2+-dependent flagellar waveform conversion.