Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory of the in-plane photoelectric effect in quasi-one-dimensional electron systems

The in-plane photoelectric (IPPE) effect is a recently discovered [Sci. Adv. \textbf{8}, eabi8398 (2022)] quantum phenomenon which enables efficient detection of terahertz (THz) radiation in semiconductor structures with a two-dimensional (2D) electron gas. Here we develop a theory of the IPPE effect in quasi-one-dimensional electron systems in which the width of the 2D conducting channel is so small that the transverse quantization energy is larger than the thermal energy. We calculate the THz photoresponse of such a system, as a function of the THz frequency, control gate voltages, and geometrical parameters of the detector. We show that the transverse quantization of the electron motion manifests itself in oscillating gate-voltage dependences of the photocurrent, if the THz photon energy is less than the one-dimensional quantization energy. Results of the theory are applicable to any semiconductor systems with 2D electron gases, including III-V structures, silicon-based field effect transistors, and the novel 2D layered, graphene-related materials.
Image Classifiers Leak Sensitive Attributes About Their Classes

Neural network-based image classiﬁers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy. To investigate this privacy leakage, we introduce the ﬁrst C lass A ttribute I nference A ttack (C AIA ), which leverages recent advances in text-to-image synthesis to infer sensitive attributes of individual classes in a black-box setting, while remaining competitive with related white-box attacks. Our extensive experiments in the face recognition domain show that C AIA can accurately infer undisclosed sensitive attributes, such as an individual’s hair color, gender, and racial appearance, which are not part of the training labels. Interestingly, we demonstrate that adversarial robust models are even more vulnerable to such privacy leakage than standard models, indicating that a trade-off between robustness and privacy exists.
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits

We study linear contextual bandits in the misspecified setting, where the expected reward function can be approximated by a linear function class up to a bounded misspecification level $\zeta>0$. We propose an algorithm based on a novel data selection scheme, which only selects the contextual vectors with large uncertainty for online regression. We show that, when the misspecification level $\zeta$ is dominated by $\tilde O (\Delta / \sqrt{d})$ with $\Delta$ being the minimal sub-optimality gap and $d$ being the dimension of the contextual vectors, our algorithm enjoys the same gap-dependent regret bound $\tilde O (d^2/\Delta)$ as in the well-specified setting up to logarithmic factors. In addition, we show that an existing algorithm SupLinUCB (Chu et al., 2011) can also achieve a gap-dependent constant regret bound without the knowledge of sub-optimality gap $\Delta$. Together with a lower bound adapted from Lattimore et al. (2020), our result suggests an interplay between misspecification level and the sub-optimality gap: (1) the linear contextual bandit model is efficiently learnable when $\zeta \leq \tilde O(\Delta / \sqrt{d})$; and (2) it is not efficiently learnable when $\zeta \geq \tilde \Omega({\Delta} / {\sqrt{d}})$. Experiments on both synthetic and real-world datasets corroborate our theoretical results.
DeltaScore: Evaluating Story Generation with Differentiating Perturbations

Various evaluation metrics exist for natural language generation tasks, but they have limited utility for story generation since they generally do not correlate well with human judgments and do not measure ﬁne-grained story aspects, such as ﬂuency versus relatedness, as they are intended to assess overall generation quality. In this paper, we propose D ELTA S-CORE , an approach that utilizes perturbation to evaluate ﬁne-grained story aspects. Our core idea is based on the hypothesis that the better the story performs in a speciﬁc aspect (e.g., ﬂu-ency), the more it will be affected by a particular perturbation (e.g., introducing typos). To measure the impact, we calculate the likelihood difference between the pre-and post-perturbation stories using a language model. We evaluate D ELTA S CORE against state-of-the-art model-based and traditional similarity-based metrics across multiple story domains, and investigate its correlation with human judgments on ﬁve ﬁne-grained story aspects: ﬂuency, coherence, relatedness, logicality, and interestingness. Our results demonstrate that D ELTA S CORE performs impressively in evaluating ﬁne-grained story aspects, and we discovered a striking outcome where a speciﬁc perturbation appears to be highly effective in measuring most aspects.
DENOMINATORS OF SPECIAL VALUES OF ζ-FUNCTIONS COUNT KU-LOCAL HOMOTOPY GROUPS OF MOD p MOORE SPECTRA

In this note, for each odd prime $p$, we show that the orders of the $KU$-local homotopy groups of the mod $p$ Moore spectrum are equal to denominators of special values of certain quotients of Dedekind zeta-functions of totally real number fields. With this observation in hand, we give a cute topological proof of the Leopoldt conjecture for those number fields, by showing that it is a consequence of periodicity properties of $KU$-local stable homotopy groups.
Self-Supervised Visual Representation Learning on Food Images

Food image analysis is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning-based methods learn the visual representation for downstream tasks based on human annotation of each food image. However, most food images in real life are obtained without labels, and data annotation requires plenty of time and human effort, which is not feasible for real-world applications. To make use of the vast amount of unlabeled images, many existing works focus on unsupervised or self-supervised learning of visual representations directly from unlabeled data. However, none of these existing works focus on food images, which is more challenging than general objects due to its high inter-class similarity and intra-class variance. In this paper, we focus on the implementation and analysis of existing representative self-supervised learning methods on food images. Specifically, we first compare the performance of six selected self-supervised learning models on the Food-101 dataset. Then we analyze the pros and cons of each selected model when training on food data to identify the key factors that can help improve the performance. Finally, we propose several ideas for future work on self-supervised visual representation learning for food images.
SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Generative Large Language Models (LLMs) such as GPT-3 are capable of generating highly fluent responses to a wide variety of user prompts. However, LLMs are known to hallucinate facts and make non-factual statements which can undermine trust in their output. Existing fact-checking approaches either require access to the output probability distribution (which may not be available for systems such as ChatGPT) or external databases that are interfaced via separate, often complex, modules. In this work, we propose"SelfCheckGPT", a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i.e. without an external database. SelfCheckGPT leverages the simple idea that if an LLM has knowledge of a given concept, sampled responses are likely to be similar and contain consistent facts. However, for hallucinated facts, stochastically sampled responses are likely to diverge and contradict one another. We investigate this approach by using GPT-3 to generate passages about individuals from the WikiBio dataset, and manually annotate the factuality of the generated passages. We demonstrate that SelfCheckGPT can: i) detect non-factual and factual sentences; and ii) rank passages in terms of factuality. We compare our approach to several baselines and show that our approach has considerably higher AUC-PR scores in sentence-level hallucination detection and higher correlation scores in passage-level factuality assessment compared to grey-box methods.
Large deviations for triangles in scale-free random graphs

We provide large deviations estimates for the upper tail of the number of triangles in scale-free inhomogeneous random graphs where the degrees have power law tails with index $-\alpha, \alpha \in (1,2)$. We show that upper tail probabilities for triangles undergo a phase transition. For $\alpha<4/3$, the upper tail is caused by many vertices of degree of order $n$, and this probability is semi-exponential. In this regime, additional triangles consist of two hubs. For $\alpha>4/3$ on the other hand, the upper tail is caused by one hub of a specific degree, and this probability decays polynomially in $n$, leading to additional triangles with one hub. In the intermediate case $\alpha=4/3$, we show polynomial decay of the tail probability caused by multiple but finitely many hubs. In this case, the additional triangles contain either a single hub or two hubs. Our proofs are partly based on various concentration inequalities. In particular, we tailor concentration bounds for empirical processes to make them well-suited for analyzing heavy-tailed phenomena in nonlinear settings.
Block-wise Bit-Compression of Transformer-based Models

With the popularity of the recent Transformer-based models represented by BERT, GPT-3 and ChatGPT, there has been state-of-the-art performance in a range of natural language processing tasks. However, the massive computations, huge memory footprint, and thus high latency of Transformer-based models is an inevitable challenge for the cloud with high real-time requirement. To tackle the issue, we propose BBCT, a method of block-wise bit-compression for transformer without retraining. Our method achieves more fine-grained compression of the whole transformer, including embedding, matrix multiplication, GELU, softmax, layer normalization, and all the intermediate results. As a case, we compress an efficient BERT with the method of BBCT. Our benchmark test results on General Language Understanding Evaluation (GLUE) show that BBCT can achieve less than 1% accuracy drop in most tasks.
Characterizing microstructural changes of skeletal muscle tissues using spectral transformed Mueller matrix polarization parameters

Polarization imaging techniques are recognized as potentially powerful tools to detect the structural changes of biological tissues. Meanwhile, spectral features of the scattered light can also provide abundant microstructural information, therefore can be applied in biomedical studies. In this paper, we adopt the polarization reflectance spectral imaging to analyze the microstructural changes of hydrolyzing skeletal muscle tissues. We measure the Mueller matrix, which is a comprehensive description of the polarization properties, of the bovine skeletal muscle samples in different periods of time, and analyze its behavior using the multispectral Mueller matrix transformation (MMT) technique. The experimental results show that for bovine skeletal muscle tissues, the backscattered spectral MMT parameters have different values and variation features at different stages. We can also find the experimental results indicate that the stages of hydrolysis for bovine skeletal muscle samples can be judged by the spectral MMT parameters. The results presented in this work show that combining with the spectral technique, the MMT parameters have the potential to be used as tools for meat quality detection and monitoring.
Prevalence and risk factors of diabetic retinopathy in Basrah, Iraq

This study aimed to measure the prevalence and risk factors of diabetic retinopathy (DR) among patients with diabetes mellitus aged 20 to 82 years attending the Faiha Diabetes, Endocrine, and Metabolism Center (FDEMC) in Basrah. A cross-sectional study was conducted at FDEMC, including 1542 participants aged 20 to 82 from January 2019 to December 2019. Both eyes were examined for evidence of DR by a mobile nonmydriatic camera, and statistical analysis was performed to measure the prevalence rates (95% CI) for patients with different characteristics. The mean age of participants was 35.9, with 689 males (44.7%; 95% CI: 42.2–47.2%) and 853 females (55.3%; 95% CI: 52.8–57.8%). The prevalence rate of DR was 30.5% (95% CI: 28.1–32.8%), and 11.27% of cases were proliferative retinopathy. DR significantly increased with age (p-value=0.000), it was higher in females (p-value=0.005), and significantly increased with a longer duration of diabetes (p-value<0.001), hyperglycemia (p-value<0.001), hypertension (p-value=0.004), dyslipidemia (p-value<0.001), nephropathy (p-value<0.001) and smoking (p-value<0.001). There was no statistical association between DR and the type of diabetes or obesity. One-third of the participants in this study had DR. Screening and early detection of DR using a simple tool such as a digital camera should be a priority to improve a person’s health status.
Depressive Features in Individuals with First Episode Psychosis: Psychopathological and Treatment Considerations from A 2-Year Follow-Up Study

Objective Comorbid depression is quite common in early psychosis and specifically related to suicidal behavior and poor long-term outcomes. However, Depressive Symptoms (DS) are often neglected in both research and treatment, especially at the psychosis onset. The goals of this investigation were: (a) to longitudinally explore DS levels in patients with First Episode Psychosis (FEP) during 24 months of follow-up, and (b) to investigate the associations of DS with psychopathology and intervention components of an “Early Intervention in Psychosis” (EIP) program across the follow-up period. Method The Global Assessment of Functioning (GAF) and the Positive And Negative Syndrome Scale (PANSS) were completed by 266 FEP subjects. A linear regression analysis with DS as the dependent parameter and psychopathological and treatment characteristics as independent variables was performed (both at baseline and across the follow-up period). Results DS had enduring associations with PANSS “Positive Symptoms” and “Negative Symptoms” subscores. During the investigation, FEP subjects significantly improved their DS severity levels. This was related to the number of individual psychotherapy meetings supplied within the EIP protocol, as well as to a higher antidepressant dose and a lower antipsychotic dose prescribed during the follow-up. Conclusions DS are quite prominent in FEP, even at the recruitment time in EIP services. Nevertheless, DS severity tends to diminish overtime, especially with the provision of specialized EIP treatments.
Long-QT mutations in KCNE1 modulate the 17β-estradiol response of Kv7.1/KCNE1

Estradiol (17$β$-E2) is implicated in higher arrhythmia risk of women with congenital or acquired long-QT syndrome (LQTS) compared to men. However, the underlying mechanisms remain poorly understood, and little is known about the impact of LQTS-associated mutations. We show that 17$β$-E2 inhibits the human cardiac Kv7.1/KCNE1 channel expressed in Xenopus oocytes. We find that the 17$β$-E2 effect depends on the Kv7.1 to KCNE1 stoichiometry, and we reveal a critical function of the KCNE1 carboxyl terminus for the effect. LQTS-associated mutations in the KCNE1 carboxyl terminus show a range of responses to 17$β$-E2, from a wild-type like response to impaired or abolished response. Together, this study increases our understanding of the mechanistic basis for 17$β$-E2 inhibition of Kv7.1/KCNE1 and demonstrates mutation-dependent responses to 17$β$-E2. These findings suggest that the 17$β$-E2 effect on Kv7.1/KCNE1 might contribute to the higher arrhythmia risk of women, particularly in carriers with specific LQTS-associated mutations.
A DNA barcode reference library for CITES listed Malagasy Dalbergia species

Abstract On Madagascar, the illegal and unsustainable exploitation and illegal international trade of Dalbergia (rosewood) precious woods remain a serious conservation problem. Members of this genus are at high risk of extinction as a consequence of logging, mining, and slash and burn agriculture. Morphological identification of these Malagasy species is difficult in the absence of flowers and fruits, especially in the case of cut trees, sawn wood, and finished product. In this study, we use molecular barcoding to identify the Dalbergia species with the intent to contribute to the control of their illegal trade. Thirty‐six Dalbergia samples representing 12 Malagasy species of which 11 have high commercial value, were collected to test the efficacy of a region of the plastid genome (rbcL) and a nuclear‐transcribed ITS for barcoding. These widely used markers, as well as DNA barcoding gaps, “best match” and “best close match” approaches, and the neighbor‐joining method were employed. All samples were amplified and sequenced using the two markers. Using a single locus, the “best match” and “best close match” approaches revealed that ITS has high discriminatory power within the tested Malagasy species. The combination of rbcL + ITS revealed 100% species discrimination. This study confirms that ITS alone and in combination with chloroplast barcode rbcL allow non‐ambiguous identification for the 12 species studied. The results contribute to the development of DNA barcoding as a useful tool to identify Malagasy Dalbergia and suggest that the approach developed should be expanded to all 56 potentially exploited species in reference to international CITES requirements and the sustainable management of valuable resources.
On exponential stability for systems with state delays

This paper considers the issue of delay-dependent exponential stability for time-delay systems. Both nominal and uncertain systems are investigated. New sufficient conditions in terms of linear matrix inequalities (LMIs) are obtained. These criteria are simple owing to the use of an integral inequality. The model transformation approaches, bounding techniques for cross terms and slack matrices are all avoided in the derivation. Rigorous proof and numerical examples showed that the proposed criteria and those based on introducing slack matrices are equivalent.
A COMPOSITE TIME INTEGRATION SCHEME FOR DYNAMIC ADHESION AND ITS APPLICATION TO GECKO SPATULA PEELING

Simulation of dynamic adhesive peeling problems at small scales has attracted little attention so far. These problems are characterized by a highly nonlinear response. Accurate and stable time integration schemes are required for simulation of dynamic peeling problems. In the present work, a composite time integration scheme is proposed for the simulation of dynamic adhesive peeling problems. It is shown through numerical examples that the proposed scheme remains stable and also has some gain in accuracy. The performance of the scheme is compared with two collocation-based schemes, i.e., Newmark scheme and Bathe composite scheme. It is shown that the proposed scheme and Bathe composite scheme perform equally. However, the proposed scheme adds very little to the computational cost of Newmark scheme. Through a numerical simulation of the peeling of a gecko spatula from a rigid substrate it is shown that the proposed scheme and the Bathe composite scheme are able to simulate the complete peeling process for given time step whereas the Newmark scheme diverges. It is also shown that the maximum pull-off force is within the range reported in the literature.
Pattern‐Matching Procedure for Automatic Talker Recognition

A pattern‐matching procedure for automatic recognition of talkers was used to study the effects of variations in patterns upon recognition performance. Several utterances of common words, excerpted from context, were spoken by ten talkers and converted to time‐frequency‐energy patterns. Some of each talker's utterances were used to form reference patterns and the remaining utterances served as test patterns. The recognition procedure consisted of cross‐correlating the test patterns with the reference patterns and selecting the talker corresponding to the reference pattern with the highest correlation as the talker of the test utterance. The same recognition procedure was used with patterns reduced to two dimensions. The recognition score for three‐dimensional patterns was 89%. Reducing the original patterns to time‐energy patterns resulted in a lower recognition score; however, when only spectral information was retained, recognition results were the same as those for three‐dimensional patterns. No errors.
Increased influenza severity in children in the wake of SARS‐CoV‐2

The SARS‐CoV‐2 pandemic and subsequent interruption of influenza circulation has lowered population immunity to influenza, especially among children with few prepandemic exposures. Using data from a prospective pediatric cohort study based in Managua, Nicaragua, we compared the incidence and severity of influenza A/H3N2 and influenza B/Victoria between 2022 and two prepandemic seasons. We found a higher incidence of A/H3N2 in older children in 2022 compared with pre‐2020 and a higher proportion of severe influenza in 2022, primarily among children aged 0–4, suggesting an influence of the SARS‐CoV‐2 pandemic on influenza incidence and severity in children.
Numerical experiments with free convection in a vertical slot

Numerical solutions of the equations of steady free convection in a vertical slot have been obtained by a procedure which relies solely on an iterative solution of Poisson's equation. With a mesh spacing of $\frac{1}{40}$ the solutions are stable up to a Rayleigh number of order 105. The numerical experiments are comparable to the author's laboratory experiments (Elder 1965) and to the experiments of Eckert & Carlson (1961). The growth of the boundary layers and the uniform vertical temperature gradient are well described. It is tentatively suggested that certain unstable motions found prior to the divergence of the numerical solution are comparable to the secondary flows found by the author.
Methods and tools to assess implementation of mental health policies and plans: A systematic review

Abstract Mental health policies and plans (MHPPs) are important policy instruments and powerful tools to facilitate development of mental health systems and services across the world. We aimed to map and analyse methods and tools used to assess the extent, process and impact of implementing MHPPs. We systematically searched peer-reviewed and grey literature across seven scientific databases. We extracted and analysed the data on a) the characteristics of included studies (e.g., policy areas, region of origin, income setting) and b) the methodology and evaluation tools applied to assess the extent and process of implementation. We included 48 studies in the analyses. Twenty-six of these studies employed only qualitative methods (e.g., semi-structured interviews, focus group discussions, desk review, stakeholder consultations); 12 studies used quantitative methods (e.g., trend analysis, survey) and 10 used mixed-methods approaches. Generally, methods and tools used for assessment were described poorly with less than half of the studies providing partial or full details about them. Only three studies provided assessment of full policies. There is a lack of rigorous research to assess implementation MHPPs. Assessments of the implementation of entire MHPPs are almost non-existent. Strategies to assess the implementation of MHPPs should be an integral part of MHPPs.