Miracle-dz/UniFAR-FTUs huggingface dataset:
The evolution of paternity and paternal care in birds

Paternity has been hypothesized to be related to the evolution of paternal care because (1) there should be selection for males not to invest in broods with an uncertain parentage, or (2) male extrapair activity is traded against paternal care. We used interspecific comparisons to discriminate between these alternatives. Male participation in three kinds of parental care (nest building, incubation, provisioning of offspring) increased with high paternity in their own nests. Male parental activities at some stages of the breeding cycle were significantly correlated. A multivariate analysis taking this intercorrelation between different components of care and potentially confounding variables such as precociality, polyandry, and sexual dichromatism into account revealed that paternity was significantly positively related to offspring provisioning, while male participation in the other components of parental care did not explain a significant amount of interspecific variation in paternity. Analyses of evolutionary transitions between different dichotomized states of paternity and paternal care provided no clear conclusions concerning evolutionary scenarios. However, theoretical arguments and the results of the contrast analyses suggest that male provisioning of offspring evolved in response to paternity.
Program Development and Effectiveness of Workplace Health Promotion Program for Preventing Metabolic Syndrome among Office Workers

This paper aims to develop and analyze the effects of a socio-ecological model-based intervention program for preventing metabolic syndrome (MetS) among office workers. The intervention program was developed using regular health examinations, a “health behavior and need” assessment survey among workers, and a focus group study. According to the type of intervention, subjects took part in three groups: health education via an intranet-based web magazine (Group 1), self-monitoring with the U-health system (Group 2), and the target population who received intensive intervention (Group 3). The intervention programs of Group 1 and Group 2, which relied on voluntary participation, did not show significant effects. In Group 3, which relied on targeted and proactive programs, showed a decrease in waist circumference and in fasting glucose (p < 0.001). The MetS score in both males (−0.61 ± 3.35 versus −2.32 ± 2.55, p = 0.001) and females (−3.99 ± 2.05 versus −5.50 ± 2.19, p = 0.028) also showed a statistically significant decrease. In light of the effectiveness of the intensive intervention strategy for metabolic syndrome prevention among workers used in this study, companies should establish targeted and proactive health care programs rather than providing a healthcare system that is dependent on an individual’s voluntary participation.
Neuropsychological measures in normal individuals that predict subsequent cognitive decline.

OBJECTIVE To examine neuropsychological measures among normal individuals that predict time to subsequent cognitive decline. DESIGN Cognitive performance, as measured by 6 neuropsychological tests, was examined at baseline. Participants were followed up for approximately 5 years. Cox proportional hazards models were used to evaluate the neuropsychological measures at baseline that predicted time to progression from normal cognition to mild impairment. Comparable data also examined time to progression from mild impairment to a diagnosis of Alzheimer disease. SETTING Community volunteer-based sample examined at a medical institution. PARTICIPANTS One hundred and seven individuals who were cognitively normal and 235 individuals with mild cognitive impairment at baseline. MAIN OUTCOME MEASURES Time to progression from normal cognition to mild impairment and time to progression from mild impairment to a diagnosis of Alzheimer disease. RESULTS The risk of progressing from normal to mild impairment was considerably greater among those with lower scores on tests of episodic memory (eg, hazard ratio for a 1-SD decrease in the California Verbal Learning Test, 0.55; P<.001). Normal individuals who carried at least 1 copy of the apolipoprotein E epsilon2 allele were less likely to develop cognitive impairments over time than individuals with no epsilon2 allele (hazard ratio for presence of allele, 0.13; P = .006). Measures of both episodic memory and executive function were significant predictors of time to progression from mild impairment to a clinical diagnosis of Alzheimer disease (eg, hazard ratio for a 1-SD decrease in California Verbal Learning Test score, 0.67; P = .005; hazard ratio for a 1-SD increase in the time to complete part B of the Trail Making test, 1.40; P = .007). Among individuals with mild impairments, the apolipoprotein E epsilon4 allele increased risk for Alzheimer disease in a dose-dependent manner; however, this effect was not significant within the context of multivariable models. CONCLUSIONS Episodic memory performance among normal individuals predicts time to progression to mild impairment while apolipoprotein E epsilon2 status is associated with lower risk of cognitive decline among normal individuals. Tests of both episodic memory and executive function are predictors of time to progression from mild impairment to a clinical diagnosis of Alzheimer disease.
Development of Affective Organizational Commitment: A Cross-Sequential Examination of Change with Tenure

Abstract Much of the theoretical and empirical research devoted to affective organizational commitment has been based on the assumption that this form of commitment develops with tenure, displaying a rapid decrease after entry, followed later by a steady increase. The present study used the cross-sequential design, advocated in the life-span development literature, to examine changes in commitment with tenure and found evidence for a developmental trend in affective organizational commitment. However, contrary to theory and past results, the developmental function indicated a steady, persistent decrease with increasing tenure (from 1 to 9 and 15 to 19 years) in a sample of 479 Australian police officers. This is the first study designed explicitly to examine how commitment changes over an extended period of tenure, therefore replications are required to test for similar or other nonconforming trends in other organizations. If persistent decreases in commitment, resulting in low levels of commitment in groups of experienced employees, are found in other organizations, managers will need to be aware that low commitment norms may impact on new employees and will need advice on ways to improve commitment levels. If different trends are found, the influence of various characteristics of the culture or structure of organizations will need to be given greater consideration in theories devised to explain the development of commitment.
Computational Models to Study Language Processing in the Human Brain: A Survey

Despite differing from the human language processing mechanism in implementation and algorithms, current language models demonstrate remarkable human-like or surpassing language capabilities. Should computational language models be employed in studying the brain, and if so, when and how? To delve into this topic, this paper reviews efforts in using computational models for brain research, highlighting emerging trends. To ensure a fair comparison, the paper evaluates various computational models using consistent metrics on the same dataset. Our analysis reveals that no single model outperforms others on all datasets, underscoring the need for rich testing datasets and rigid experimental control to draw robust conclusions in studies involving computational models.
Clinical information extraction for Low-resource languages with Few-shot learning using Pre-trained language models and Prompting

Automatic extraction of medical information from clinical documents poses several challenges: high costs of required clinical expertise, limited interpretability of model predictions, restricted computational resources and privacy regulations. Recent advances in domain-adaptation and prompting methods showed promising results with minimal training data using lightweight masked language models, which are suited for well-established interpretability methods. We are first to present a systematic evaluation of these methods in a low-resource setting, by performing multi-class section classification on German doctor's letters. We conduct extensive class-wise evaluations supported by Shapley values, to validate the quality of our small training data set and to ensure the interpretability of model predictions. We demonstrate that a lightweight, domain-adapted pretrained model, prompted with just 20 shots, outperforms a traditional classification model by 30.5% accuracy. Our results serve as a process-oriented guideline for clinical information extraction projects working with low-resource.
Robust NAS under adversarial training: benchmark, theory, and beyond

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robust architectures, especially when adversarial training is considered. In this work, we aim to address these two challenges, making twofold contributions. First, we release a comprehensive data set that encompasses both clean accuracy and robust accuracy for a vast array of adversarially trained networks from the NAS-Bench-201 search space on image datasets. Then, leveraging the neural tangent kernel (NTK) tool from deep learning theory, we establish a generalization theory for searching architecture in terms of clean accuracy and robust accuracy under multi-objective adversarial training. We firmly believe that our benchmark and theoretical insights will significantly benefit the NAS community through reliable reproducibility, efficient assessment, and theoretical foundation, particularly in the pursuit of robust architectures.
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models

Recent trends in Large Vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced, object-level referential comprehension. In this paper, we present and delve into the self-consistency capability of LVLMs, a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations, posing limitations on their practical applicability and potential. To address this gap, we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments, we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://github.com/ivattyue/SC-Tune.
Disambiguating the role of noise correlations when decoding neural populations together

One of the most controversial problems in neural decoding is quantifying the information loss caused by ignoring noise correlations during optimal brain computations. For more than a decade, the measure here called $ \Delta I^{DL} $ has been believed exact. However, we have recently shown that it can exceed the information loss $ \Delta I^{B} $ caused by optimal decoders constructed ignoring noise correlations. Unfortunately, the different information notions underlying $ \Delta I^{DL} $ and $ \Delta I^{B} $, and the putative rigorous information-theoretical derivation of $ \Delta I^{DL} $, both render unclear whether those findings indicate either flaws in $ \Delta I^{DL} $ or major departures from traditional relations between information and decoding. Here we resolve this paradox and prove that, under certain conditions, observing $ \Delta I^{DL} {>}\Delta I^{B} $ implies that $ \Delta I^{DL} $ is flawed. Motivated by this analysis, we test both measures using neural populations that transmit independent information. Our results show that $ \Delta I^{DL} $ may deem noise correlations more important when decoding the populations together than when decoding them in parallel, whereas the opposite may occur for $ \Delta I^{B} $. We trace these phenomena back, for $ \Delta I^{B} $, to the choice of tie-breaking rules, and for $ \Delta I^{DL} $, to unforeseen limitations within its information-theoretical foundations. Our study contributes with better estimates that potentially improve theoretical and experimental inferences currently drawn from $ \Delta I^{DL} $ without noticing that it may constitute an upper bound. On the practical side, our results promote the design of optimal decoding algorithms and neuroprosthetics without recording noise correlations, thereby saving experimental and computational resources.
Voice source and vocal tract variations as cues to emotional states perceived from expressive conversational speech

Speech parameters originating from voice source and vocal tract were analyzed to ﬁnd acoustic correlates of dimensional descriptions of emotional states. To achieve this goal best, we adopted the Utsunomiya University Spoken Dialogue Database, which was designed for studies on paralinguistic information in expressive conversational speech. Analyses for four female and two male speakers showed: (i) Prosodic parameters were highly correlated especially with the activation dimension, (ii) The aperiodicity-related voice source parameter showed that breathy phonation was mainly used in unpleasant utterances for three females, (iii) Due to smiling facial expression, formant frequencies were higher in pleasant utterances for a female.
A new peak-current injected digital control circuit for dc-dc converter

The purpose of this paper is to present a new peak-current injected digital control circuit for dc-dc converter and its dynamic characteristics. The proposed circuit is able to detect the peak point of the switch current in real time only by comparing two pulse trains. At the moment when the peak switch current is detected, the proposed circuit generates the turn off signal of the main switch. The circuit principle is very simple. It is composed of the voltage controlled oscillator (VCO) using as the V-F signal converter, the programmable delay circuit and a few flip-flops. In the experiment, it is confirmed the main switch is turned off when the peak switch current is detected. Additionally, it is shown in simulation that the proposed digital control circuit provides the good transient responses of dc-dc converter. As a result, it is seen that the proposed method has no overshoot of the output voltage and the convergence time that the output voltage is settled to steady-state is only 129μs. This result is approximately 89% smaller than the conventional voltage mode digital control dc-dc converter.
Natural Language as Polices: Reasoning for Coordinate-Level Embodied Control with LLMs

We demonstrate experimental results with LLMs that address robotics action planning problems. Recently, LLMs have been applied in robotics action planning, particularly using a code generation approach that converts complex high-level instructions into mid-level policy codes. In contrast, our approach acquires text descriptions of the task and scene objects, then formulates action planning through natural language reasoning, and outputs coordinate level control commands, thus reducing the necessity for intermediate representation code as policies. Our approach is evaluated on a multi-modal prompt simulation benchmark, demonstrating that our prompt engineering experiments with natural language reasoning significantly enhance success rates compared to its absence. Furthermore, our approach illustrates the potential for natural language descriptions to transfer robotics skills from known tasks to previously unseen tasks.
CFU : Colony Forming Units 24 EMBR : ethambutol resistant 25 MDR-TB : multi-drug-resistant tuberculosis 26 Mtb : Mycobacterium tuberculosis 27 PCR : Polymerase Chain Reaction

46 We previously reported development of a prototype antibiotic sensitivity assay to detect 47 drug-resistant Mycobacterium tuberculosis using infection by mycobacteriophage to create a 48 novel nucleic acid transcript, a surrogate marker of mycobacterial viability, detected by reverse 49 transcriptase-polymerase chain reaction (RT-PCR). (1) This assay detects antibiotic resistance 50 to all drugs, even drugs for which the resistance mechanism is unknown or complex: it is a 51 phenotypic readout using nucleic acid detection. In this report, we describe development and 52 characteristics of an optimized reporter system that directed expression of the RNA cyclase 53 ribozyme, which generated circular RNA through an intra-molecular splicing reaction and led to 54 accumulation of a new nucleic acid sequence in phage-infected bacteria. These modifications 55 simplified the assay, increased the limit of detection from 10 to <10 Mycobacterium 56 tuberculosis cells, and correctly identified the susceptibility profile of Mycobacterium 57 tuberculosis strains exposed for 16 hr to either first-line or second-line anti-tubercular drugs. In 58 addition to phenotypic drug resistance or susceptibility, the assay reported streptomycin 59 minimal inhibitory concentrations and clearly detected 10% drug-resistant cells in an otherwise 60 drug-susceptible population. 61
Revisiting shear stress tensor evolution: Non-resistive magnetohydrodynamics with momentum-dependent relaxation time

This study aims to develop second-order relativistic viscous magnetohydrodynamics (MHD) derived from kinetic theory within an extended relaxation time approximation (momentum/energy dependent) for the collision kernel. The investigation involves a detailed examination of shear stress tensor evolution equations and associated transport coefficients. The Boltzmann equation is solved using a Chapman-Enskog-like gradient expansion for a charge-conserved conformal system, incorporating a momentum-dependent relaxation time. The derived relativistic non-resistive, viscous second-order MHD equations for the shear stress tensor reveal significant modifications in the coupling with dissipative charge current and magnetic field due to the momentum dependence of the relaxation time. By utilizing a power law parametrization to quantify the momentum dependence of the relaxation time, the anisotropic magnetic field-dependent shear coefficients in the Navier-Stokes limit have been investigated. The resulting viscous coefficients are seen to be sensitive to the momentum dependence of the relaxation time.
Classbox/J: controlling the scope of change in Java

Unanticipated changes to complex software systems can introduce anomalies such as duplicated code, suboptimal inheritance relationships and a proliferation of run-time downcasts. Refactoring to eliminate these anomalies may not be an option, at least in certain stages of software evolution. Classboxes are modules that restrict the visibility of changes to selected clients only, thereby offering more freedom in the way unanticipated changes may be implemented, and thus reducing the need for convoluted design anomalies. In this paper we demonstrate how classboxes can be implemented in statically-typed languages like Java. We also present an extended case study of Swing, a Java GUI package built on top of AWT, and we document the ensuing anomalies that Swing introduces. We show how Classbox/J, a prototype implementation of classboxes for Java, is used to provide a cleaner implementation of Swing using local refinement rather than subclassing.
Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning

The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state, i.e., the building envelope, cannot be measured by the learning agent. In order to overcome these two challenges, our paper proposes an auto-encoder coupled with a batch reinforcement learning technique. The proposed approach is validated for two building types with different thermal characteristics for heating in the winter and cooling in the summer. The simulation results indicate that the proposed learning agent can reduce the energy consumption by 4%–9% during 100 winter days and by 9%–11% during 80 summer days compared to the conventional constant set-point strategy.
A metric counterpart of the Gu-Yung formula

In this note we consider a generalisation to the metric setting of the recent work [Gu-Yung, JFA 281 (2021), 109075]. In particular, we show that under relatively weak conditions on a metric measure space $(X,d,\nu)$, it holds true that \[ \bigg[ \frac{u(x)-u(y)}{d(x,y)^{\frac{s}{p}}} \bigg]_{L^p_w(X \times X, \nu \otimes \nu)} \approx \| u \|_{L^p(X,\nu)}, \] where $s$ is a generalised dimension associated to $X$ and $[\cdot]_{L^p_w}$ is the weak Lebesgue norm. We provide some counterexamples which show that our assumptions are optimal.
HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling

The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit. We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.