Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel time-domain direct sampling approach for inverse scattering problems in acoustics

This work is concerned with an inverse scattering problem of determining unknown scatterers from time-dependent acoustic measurements. A novel time-domain direct sampling method is developed to efficiently determine both the locations and shapes of inhomogeneous media. In particular, our approach is very easy to implement since only cheap space-time integrations are involved in the evaluation of the imaging functionals. Based on the Fourier-Laplace transform, we establish an inherent connection between the time-domain and frequency-domain direct sampling method. Moreover, rigorous theoretical justifications and numerical experiments are provided to verify the validity and feasibility of the proposed method.
An machine learning model to predict quality of life subtypes of disabled stroke survivors

Abstract Objective Stroke causes serious physical disability with impaired quality of life (QoL) and heavy burden on health. The goal of this study is to explore the impaired QoL typologies and their predicting factors in physically disabled stroke survivors with machine learning approach. Methods Non‐negative matrix factorization (NMF) was applied to clustering 308 physically disabled stroke survivors in rural China based on their responses on the short form 36 (SF‐36) assessment of quality of life. Principal component analysis (PCA) was conducted to differentiate the subtypes, and the Boruta algorithm was used to identify the variables relevant to the categorization of two subtypes. A gradient boosting machine(GBM) and local interpretable model‐agnostic explanation (LIME) algorithms were used to apply to interpret the variables that drove subtype predictions. Results Two distinct subtypes emerged, characterized by short form 36 (SF‐36) domains. The feature difference between worsen QoL subtype and better QoL subtype was as follows: role‐emotion (RE), body pain (BP) and general health (GH), but not physical function (PF); the most relevant predictors of worsen QoL subtypes were help from others, followed by opportunities for community activity and rehabilitation needs, rather than disability severity or duration since stroke. Interpretation The results suggest that the rehabilitation programs should be tailored toward their QoL clustering feature; body pain and emotional‐behavioral problems are more crucial than motor deficit; stroke survivors with worsen QoL subtype are most in need of social support, return to community, and rehabilitation.
Unraveling the interplay between unicellular parasites and bacterial biofilms: Implications for disease persistence and antibiotic resistance

ABSTRACT Bacterial biofilms have attracted significant attention due to their involvement in persistent infections, food and water contamination, and infrastructure corrosion. This review delves into the intricate interactions between bacterial biofilms and unicellular parasites, shedding light on their impact on biofilm formation, structure, and function. Unicellular parasites, including protozoa, influence bacterial biofilms through grazing activities, leading to adaptive changes in bacterial communities. Moreover, parasites like Leishmania and Giardia can shape biofilm composition in a grazing independent manner, potentially influencing disease outcomes. Biofilms, acting as reservoirs, enable the survival of protozoan parasites against environmental stressors and antimicrobial agents. Furthermore, these biofilms may influence parasite virulence and stress responses, posing challenges in disease treatment. Interactions between unicellular parasites and fungal-containing biofilms is also discussed, hinting at complex microbial relationships in various ecosystems. Understanding these interactions offers insights into disease mechanisms and antibiotic resistance dissemination, paving the way for innovative therapeutic strategies and ecosystem-level implications.
DO PROVERBS MEASURE ABSTRACT OR METAPHORICAL THINKING: EVIDENCE FROM HEALTHY GREEK-SPEAKING ADULTS

The aim of the present study is to investigate whether proverbs depict abstract or metaphorical thinking. Proverbs were extensively used for the assessment of abstract thinking. However, recent research disputes the previous traditional hypothesis and claims that proverbs are related to metaphorical thinking rather than abstract thinking. Although, metaphorical thinking is part of abstract thinking is compatible with concrete/literal thinking. Factors such as chronological age of the participants, gender, years of education and hand preference found to affect the interpretation of proverbs. In the present study fifty (50) healthy adults participated. Two main tasks were administrated to the participants; the first task was tested participants’ mental (both verbal and practical) intelligence and the second task was a proverbs test, investigating the interpretation of specific Greek proverbs (N=25). Further information about participants’ profile, such as gender, years of education and participants’ chronological age were collected. The major findings suggest that intelligence and mainly lexical knowledge was exhibited as predictor variable in the accuracy scores on the proverbs test; while abstract thinking variables (i.e. reasoning, sequences, and analogy) were not found to correlate with accuracy on proverbs task. Participants’ chronological age, gender and years of education did not seem to affect participants’ performance. However, we should note that all participants were highly educated (university degree). Finally, proverb "transparency" has been found to affect participants’ interpretations.
Affine Symmetries for ABJM Partition Function and its Generalization

Partially motivated by the fact that the grand partition function of the ABJM theory or its generalization is expressed by a spectral operator enjoying symmetries of the Weyl group, it was found that the grand partition function satisfies the q-Painleve equation, which is constructed from the affine Weyl group. In this paper we clarify the affine symmetries of the grand partition function. With the affine symmetries, we find that the grand partition function extends naturally outside the fundamental domain of duality cascades and once the Painleve equation holds in the fundamental domain, so does it outside.
Evaluation of antiparasitic effect of silver nanoparticles against Trypanosoma cruzi in vitro

American trypanosomiasis is one the globally neglected tropical disease, and is cause by the protozoan haemoflagellate Trypanosoma cruzi (Tc). Currently, the anti-Tc drugs are limited and no vaccine is available. The objective of the study was to determine the effects of silver nanoparticles (AgNPs) against trypomastigotes of the Sylvio X10/4 (TcI) and Esmeraldo (TcII) strains. The AgNPs were obtained by the reduction method and a structural characterization was carried out. The cytotoxic activity of the AgNPs (anti-Tc activity) was determined by calculating the values of half the maximum inhibitory concentration (IC 50 ) in HaCaT cells and parasites. AgNPs showed no-cytotoxicity against HaCaT cells at the IC 50 values assayed in parasites, and the calculated IC 50 trypomastigotes was 4.05 µg/ml and 6.16 µg/ml for TcI and TcII strains, respectively. AgNPs were more active against both Trypanosoma cruzi strains than nifurtimox (400 µg/ml) in vitro assays. These results lay the groundwork for further evaluation of AgNPs, to investigate its chemotherapeutic value to figh T. cruzi infection in animal models.
Quality of environmental information disclosure and enterprise characteristics

Purpose Based on several important environmental protection and information disclosure policies that have been issued in China, the purpose of this paper is to test the relationship between characteristics and the environmental information disclosure quality of sample companies. Design/methodology/approach The OLS regression analysis is selected for this research which takes China’s heavy pollution companies listed on the Shanghai Stock Exchange from 2015 to 2016 as samples. Findings The quality of these environmental information disclosures needs to be strengthened, and while the quality of the disclosures among the companies examined improved significantly in 2016 compared with 2015, there are still high variations in quality from industry to industry. In addition, the scale of company is most closely correlated to the quality of environmental information disclosure and the economic situation of the enterprises is the next. Other factors affecting the disclosure quality include in order the degree of local economic development the scale of the state-owned shares and the independent directors. Listed years and equity restriction show a positive correlation but not significant in statistics. Originality/value The research will assist administrative organizations to allocate governance sources effectively, plan governance investment as a whole, and improve the overall level of the disclosure of environmental information while strengthening the governance efficiency and effectiveness, according to the correlation and degree between the company characteristics and environmental information disclosure quality.
Psychophysiological reactions during the trauma-film paradigm and their predictive value for intrusions

ABSTRACT Background: Adequate adaptation of the autonomic nervous system (ANS) is crucial in potentially life-threatening situations. The defence cascade provides a descriptive model of progressing dominant physiological reactions in such situations, including cardiovascular parameters and body mobility. The empirical evidence for this model is scarce, and the influence of physiological reactions in this model for predicting trauma-induced intrusions is unresolved. Objectives: Using a trauma-film paradigm, we aimed to test physiological reactions to a highly stressful film as an analogue to a traumatic event along the defence cascade model. We also aimed to examine the predictive power of physiological activity for subsequent intrusive symptoms. Method: Forty-seven healthy female participants watched a stressful and a neutral film in randomized order. Heart rate (HR), heart rate variability (HRV), and body sway were measured. Participants tracked frequency, distress, and quality of subsequent intrusions in a diary for 7 consecutive days. Results: For the stressful film, we observed an initial decrease in HR, followed by an increase, before the HR stabilized at a high level, which was not found during the neutral film. No differences in HRV were observed between the two films. Body sway and trembling frequency were heightened during the stressful film. Neither HR nor HRV predicted subsequent intrusions, whereas perceived distress during the stressful film did. Conclusions: Our results suggest that the physiological trauma-analogue response is characterized by an orientation response and subsequent hyperarousal, reaching a high physiological plateau. In contrast to the assumptions of the defence cascade model, the hyperarousal was not followed by downregulation. Potential explanations are discussed. For trauma-associated intrusions in the subsequent week, psychological distress during the film seems to be more important than physiological distress. Understanding the interaction between physiological and psychological responses during threat informs the study of ANS imbalances in mental disorders such as post-traumatic stress disorder. HIGHLIGHTS We used a trauma-film analogue to examine the defence cascade model and to investigate the influence of psychophysiological response on subsequent intrusions. While we found an orientation phase, upregulation phase, and coactivation indicated by heart rate, no downregulation phase was observed. None of the physiological parameters examined predicted subsequent intrusions, whereas subjective distress during the film did.
A method for traffic network speed forecasting based on GCN-BiLSTM

Accurate prediction results in the field of intelligent transportation systems are crucial. They enable commuters to make informed decisions regarding travel modes, routes, and departure times, which can significantly enhance travel efficiency. Due to the unique characteristics of road networks, accurate prediction is particularly important. This paper proposes a deep learning method combining graph convolutional neural network and bidirectional long short-term memory network. The model uses GCN to extract the topological structure features of the road network, learns the spatial correlation of the road, and uses BiLSTM to learn the temporal correlation of the road network speed data. Finally, non-traffic factors are integrated into the deep model to improve the accuracy of prediction. It is verified by real road network speed data. Experimental results show that compared with the traditional prediction model, the root mean square error of the GCN-BiLSTM combination model is 1.455, the mean absolute error is 1.283, the R2 of the coefficient of determination is 0.937, and the 95% confidence interval is [-2.866, 3.233], which are better than other models. The proposed model has higher accuracy and stability.
Modeling Boundedly Rational Agents with Latent Inference Budgets

We study the problem of modeling a population of agents pursuing unknown goals subject to unknown computational constraints. In standard models of bounded rationality, sub-optimal decision-making is simulated by adding homoscedastic noise to optimal decisions rather than explicitly simulating constrained inference. In this work, we introduce a latent inference budget model (L-IBM) that models agents' computational constraints explicitly, via a latent variable (inferred jointly with a model of agents' goals) that controls the runtime of an iterative inference algorithm. L-IBMs make it possible to learn agent models using data from diverse populations of suboptimal actors. In three modeling tasks -- inferring navigation goals from routes, inferring communicative intents from human utterances, and predicting next moves in human chess games -- we show that L-IBMs match or outperform Boltzmann models of decision-making under uncertainty. Inferred inference budgets are themselves meaningful, efficient to compute, and correlated with measures of player skill, partner skill and task difficulty.
Developmental Spelling in Fourth Grade: An Analysis of What Poor Readers Do

Since Carol Chomsky (1971a, 1971b) and Charles Read (1971) published their pioneer reports on the development of writing behaviors in young children, examinations of the developmental or invented spellings of emergent writers have contributed to changes in emphases in early literacy instruction. Before that time educators seldom advocated writing experiences for children before they learned to read (Adams, 1990). During the past twenty-five years, there have been careful descriptions and analyses of the developmental stages and strategies of young children who experiment with and work through patterns of spelling while discovering written language. As a result of this body of work, more teachers have learned to decipher and assess the development of spellings of preschoolers and primary grade students. The increased ability to understand beginning attempts with print of the youngest writers has no doubt contributed to the encouragement of story writing, journals, and other writing activities from the earliest school years. Fortunately, the increase in opportunities to write also enhances the development of phonemic awareness and word recognition, both of which are predictors of future reading success (Gill, 1992; Juel, Griffith, and Gough, 1986; Perfetti, 1985; Tunmer and Nesdale, 1985).
ImFace++: A Sophisticated Nonlinear 3D Morphable Face Model with Implicit Neural Representations

Accurate representations of 3D faces are of paramount importance in various computer vision and graphics applications. However, the challenges persist due to the limitations imposed by data discretization and model linearity, which hinder the precise capture of identity and expression clues in current studies. This paper presents a novel 3D morphable face model, named ImFace++, to learn a sophisticated and continuous space with implicit neural representations. ImFace++ first constructs two explicitly disentangled deformation fields to model complex shapes associated with identities and expressions, respectively, which simultaneously facilitate the automatic learning of correspondences across diverse facial shapes. To capture more sophisticated facial details, a refinement displacement field within the template space is further incorporated, enabling a fine-grained learning of individual-specific facial details. Furthermore, a Neural Blend-Field is designed to reinforce the representation capabilities through adaptive blending of an array of local fields. In addition to ImFace++, we have devised an improved learning strategy to extend expression embeddings, allowing for a broader range of expression variations. Comprehensive qualitative and quantitative evaluations demonstrate that ImFace++ significantly advances the state-of-the-art in terms of both face reconstruction fidelity and correspondence accuracy.
Predicting Stock Prices Based on Price/Volume with Deep Learning and System Engineering Aggregate with Dynamic Behaviors and Trading Signals

Current stock market forecasting methods encompass fundamental, technical, emotional, and bargaining factors. Predominantly, price prediction hinges on order volume and price, although correlating these two within existing models proves challenging. This study employs Cycle Generative Adversarial Network (Cycle GAN) to unravel the intricate price-volume relationship, combining it with Bollinger Bands for trading signal analysis, overcoming hurdles in short-term forecasting prevalent in numerical analysis and AI. Focusing on TSMC (2330.TW) stock price, the research leverages Cycle GAN in deep learning to master the price-volume nexus, juxtaposed with LSTM and RNN. Historical TSMC closing prices and transaction counts are model inputs, scrutinizing their interconnectedness for predictions. This innovative approach aligns stock price, volume, market value, taxes, and prior changes via system engineering. By intertwining Bollinger Bands with stock price forecasts, trading signals are distilled, factoring in extended index %b for a comprehensive market picture. In this research, under the framework of simulation system engineering, the stock price forecasted using RESNET yielded a 20.3% return, which represents a significant increase when compared to the original Bollinger Bands average return on investment of 15.5%.
Unknown Sample Discovery for Source Free Open Set Domain Adaptation

Open Set Domain Adaptation (OSDA) aims to adapt a model trained on a source domain to a target domain that undergoes distribution shift and contains samples from novel classes outside the source domain. Source-free OSDA (SF-OSDA) techniques eliminate the need to access source domain samples, but current SF-OSDA methods utilize only the known classes in the target domain for adaptation, and require access to the entire target domain even during inference after adaptation, to make the distinction between known and unknown samples. In this paper, we introduce Unknown Sample Discovery (USD) as an SF-OSDA method that utilizes a temporally ensembled teacher model to conduct known-unknown target sample separation and adapts the student model to the target domain over all classes using co-training and temporal consistency between the teacher and the student. USD promotes Jensen-Shannon distance (JSD) as an effective measure for known-unknown sample separation. Our teacher-student framework significantly reduces error accumulation resulting from imperfect known-unknown sample separation, while curriculum guidance helps to reliably learn the distinction between target known and target unknown subspaces. USD appends the target model with an unknown class node, thus readily classifying a target sample into any of the known or unknown classes in subsequent post-adaptation inference stages. Empirical results show that USD is superior to existing SF-OSDA methods and is competitive with current OSDA models that utilize both source and target domains during adaptation.
Free3D: Consistent Novel View Synthesis without 3D Representation

We introduce Free3D, a simple approach designed for open-set novel view synthesis (NVS) from a single image. Similar to Zero-1-to-3, we start from a pre-trained 2D image generator for generalization, and fine-tune it for NVS. Compared to recent and concurrent works, we obtain significant improvements without resorting to an explicit 3D representation, which is slow and memory-consuming or training an additional 3D network. We do so by encoding better the target camera pose via a new per-pixel ray conditioning normalization (RCN) layer. The latter injects pose information in the underlying 2D image generator by telling each pixel its specific viewing direction. We also improve multi-view consistency via a light-weight multi-view attention layer and multi-view noise sharing. We train Free3D on the Objaverse dataset and demonstrate excellent generalization to various new categories in several new datasets, including OminiObject3D and GSO. We hope our simple and effective approach will serve as a solid baseline and help future research in NVS with more accuracy pose. The project page is available at https://chuanxiaz.com/free3d/.
Direct data-driven control of input saturated systems: a LMI-based approach

In this paper, we revisit three intricate control challenges in the context of input-saturated systems, adopting a direct data-driven perspective. Diverging from the traditional two-stage process involving system identification and model-based control, our approach dispenses with the need for an explicit model description. By harnessing a combination of data-based closed-loop representation, Lyapunov theory, instrumental variables, and a generalized sector condition, we formulate data-driven linear matrix inequalities (LMIs). These LMIs are employed to maximize the basin of attraction, minimize the closed-loop reachable set with bounded disturbances, and propose a new data-driven $\ell_2$-gain minimization. Through demonstrations on benchmark examples, we shed light on the merits and limitations of transitioning from indirect to direct design strategies for input-saturated plants, emphasizing notable advantages in handling nonlinear dynamics.
A NEW AND OPEN COILED VAL VAT A (GASTROPODA) FROM THE PLIOCENE KOOBI FORA FORMATION OF THE TURKANA BASIN

New field collections allow the study and description of Valvata juliae new species from the Pliocene upper Burgi Member of the Koobi Fora Formation of Kenya. The shell morphology of this species varies from trochospiral to planispiral to open coiled. The species is restricted to a short stratigraphie interval. Valvata juliae is considered as an invader of the Turkana Basin during a lacustrine transgression event. The open coiling of the species is interpreted as an ecophenotypic response to a high level of environmental stress caused by lake level fluctuations and emergence of delta systems. These environmental conditions brought Valvata juliae to extinction soon after it invaded the Turkana Basin. The history of the Turkana Basin dates back to the early Pliocene since when it has existed primarily as a fluvially dominated rift basin. During relatively short time intervals, rift lakes formed in the basin. The sediments of these lakes contain most of the mollusk-bearing strata (Feibel, 1999). The Plio-Pleistocene sediments are exposed in several regions around Lake Turkana. This study analyzed the mollusk fossils of the Koobi Fora Formation which is exposed along the eastern shore of Lake Turkana, particularly in the Koobi Fora region (Fig. 1). The chronostratigraphic framework of the Koobi Fora Formation is based on more than 130 tuffs that are isochronous markers and in some cases suitable for