Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum sampling algorithms, phase transitions, and computational complexity

Dominik S. Wild, Dries Sels, 3 Hannes Pichler, 5 Cristian Zanoci, and Mikhail D. Lukin Max Planck Institute of Quantum Optics, Hans-Kopfermann-Straße 1, D-85748 Garching, Germany Center for Computational Quantum Physics, Flatiron Institute, New York, New York 10010, USA Department of Physics, New York University, New York, New York 10003, USA Institute for Theoretical Physics, University of Innsbruck, Innsbruck A-6020, Austria Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Innsbruck A-6020, Austria Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA (Dated: September 8, 2021)
Differentially-Private Fingerprinting of Relational Databases

When sharing sensitive databases with other parties, a database owner aims to (i) have privacy guarantees for its shared database, (ii) have liability guarantees (e.g., via ﬁnger-printing) in case of unauthorized sharing of its database by the recipients, and (iii) provide a high quality (utility) database to the recipients. We observe that sharing a database under differential privacy and database ﬁngerprinting are orthogonal objectives. The former aims to inject noise into a database to prevent inference of the original data values, whereas, the latter aims to hide unique marks inside a database to trace malicious parties (data recipients) who leak the data without the authorization. In this paper, we achieve these two objectives simultaneously by proposing a novel differentially-private ﬁngerprinting mechanism for relational databases. Speciﬁcally, we ﬁrst devise a bit-level random response scheme to achieve differential privacy for sharing the entire database, and then, based on this, we develop an ε - differentially private ﬁngerprinting mechanism. Next, we theoretically analyze the relationships among differential privacy guarantee, ﬁngerprint robustness, and database utility by deriving closed form expressions to characterize the privacy-utility coupling and privacy-ﬁngerprint robustness coupling. Furthermore, we propose a sparse vector technique (SVT)-based solu-tion to control the cumulative privacy loss when ﬁngerprinted copies of a database are shared with multiple recipients. We experimentally show that our proposed mechanism achieves stronger ﬁngerprint robustness than the state-of-the-art mechanisms (e.g., the ﬁngerprint cannot be compromised even if a malicious recipient changes 80% of the entries in the ﬁnger-printed database), and higher database utility than the simple composition of database perturbation under differential privacy
Integrating Regular Expressions with Neural Networks via DFA

Human-designed rules are widely used to build industry applications. However, it is infeasible to maintain thousands of such hand-crafted rules. So it is very important to integrate the rule knowledge into neural networks to build a hybrid model that achieves better performance. Specifically, the human-designed rules are formulated as Regular Expressions (REs), from which the equivalent Minimal Deterministic Finite Automatons (MDFAs) are constructed. We propose to use the MDFA as an intermediate model to capture the matched RE patterns as rule-based features for each input sentence and introduce these additional features into neural networks. We evaluate the proposed method on the ATIS intent classification task. The experiment results show that the proposed method achieves the best performance compared to neural networks and four other methods that combine REs and neural networks when the training dataset is relatively small.
Grammar-Based Concept Alignment for Domain-Specific Machine Translation

Grammar-based domain-speciﬁc MT systems are a common use case for CNLs. High-quality translation lexica are a crucial part of such systems, but involve time consuming work and signiﬁcant linguistic knowledge. With parallel example sentences available, statistical alignment tools can help automate part of the process, but they are not suitable for small datasets and do not always perform well with complex multiword expressions. In addition, the correspondences between word forms obtained in this way cannot be used directly. Addressing these problems, we propose a grammar-based approach to this task and put it to test in a simple translation pipeline.
When numbers fail: do researchers agree on operationalization of published research?

Current discussions on improving the reproducibility of science often revolve around statistical innovations. However, equally important for improving methodological rigour is a valid operationalization of phenomena. Operationalization is the process of translating theoretical constructs into measurable laboratory quantities. Thus, the validity of operationalization is central for the quality of empirical studies. But do differences in the validity of operationalization affect the way scientists evaluate scientific literature? To investigate this, we manipulated the strength of operationalization of three published studies and sent them to researchers via email. In the first task, researchers were presented with a summary of the Method and Result section from one of the studies and were asked to guess the hypothesis that was investigated via a multiple-choice questionnaire. In a second task, researchers were asked to rate the perceived quality of the study. Our results show that (1) researchers are better at inferring the underlying research question from empirical results if the operationalization is more valid, but (2) the different validity is only to some extent reflected in a judgement of the study's quality. These results combined give partial corroboration to the notion that researchers’ evaluations of research results are not affected by operationalization validity.
CIA_A_313827 1241..1249

1Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China; 2Global Health Research Center, Duke Kunshan University, Kunshan, 215300, Jiangsu, People’s Republic of China Objective: Previous studies have explored the association between malnutrition and frailty, but no study has investigated whether the Geriatric Nutritional Risk Index (GNRI), a simple and objective nutritional risk screening tool, is associated with the frailty of older adults. The study aimed to examine the relationship between nutrition-related risk, as assessed by the GNRI, and frailty among older hospitalized patients. Methods: A cross-sectional study was conducted in the West China Hospital of Sichuan University with 740 patients aged ≥70 years between March 2016 and Jan 2017. Nutritional and frailty status was evaluated with the GNRI and FRAIL scale, respectively. The adjusted and unadjusted ordinal logistic regression analyses were used to examine the relationship between nutritional risk and frailty. The ability of GNRI in detecting frailty was assessed by receiver operating characteristic (ROC) curve analysis. Results: The prevalence of low, moderate, and severe nutritional risk among frail patients were 30.1%, 27.6%, and 12.5%, respectively. Ordinal logistic regression analysis showed that malnutrition assessed by the GNRI had a significant association with frailty after adjustment of age, sex, polypharmacy, comorbidity, vision impairment, hearing impairment, cognitive impairment, and depression. In the ROC analysis, the area under the curve for GNRI identifying frailty was 0.698 (95% CI: 0.66–0.74; P<0.001), and the optimal cut-point value was 97.16 (sensitivity: 64.3%; specificity: 66.9%). Conclusion: Nutrition-related risk screened by the GNRI was independently associated with frailty. The GNRI could be used as a simple tool in detecting nutritional risk and frailty status of older patients.
Human-understandable and Machine-processable Explanations for Sub-symbolic Predictions

HESIP is a hybrid machine learning system in which a sub-symbolic machine learning component makes a prediction for an image clas-siﬁcation and afterwards a symbolic machine learning component learns probabilistic rules that are used to explain that prediction. In this paper, we present an extension to HESIP that generates human-understandable and machine-processable explanations in a controlled natu-ral language for the learned probabilistic rules. In order to achieve this, the literals of the probabilistic rules are ﬁrst reordered, and then aggregated and disambiguated according to linguistic principles so that the rules can be verbalised with a bi-directional grammar. A human-in-the-loop can modify incorrect explanations and the same bi-directional grammar can be used to process these explanations to improve the decision process of the machine.
Comparison of first‐line treatments of peripheral T‐cell lymphoma according to regimen: A systematic review and meta‐analysis

Peripheral T‐cell lymphomas (PTCLs) are known to have an aggressive clinical course and grave prognosis. Several recommended first‐line treatment regimens are available, but identification of the superior treatment remain elusive. We conducted a systematic review and meta‐analysis to determine which study‐level factors and group of regimens affect survival outcomes. The MEDLINE, Embase, and Cochrane databases were searched from inception to January 2021, and phase II or III clinical studies evaluating the efficacy of chemotherapy regimens were included. Random effects models were used to estimate 3‐year overall survival rate, complete remission rate, and subgroup differences. Meta‐regressions were carried out with adjustments for relevant covariates. Overall, 34 cohorts from 28 studies comprising 1424 PTCL patients were included in the pooled analysis. Chemotherapy regimens were divided into four groups: cyclophosphamide, doxorubicin, vincristine, prednisone (CHOP), CHOP plus etoposide, gemcitabine‐based, and others. The pooled 3‐year overall survival rate was 0.49 (95% confidence interval [CI] 0.43–0.54) for CHOP, 0.61 (95% CI 0.52–0.70) for CHOP plus etoposide, 0.39 (95% CI 0.30–0.47) for gemcitabine‐based, and 0.61 (95% CI 0.44–0.78) for others. CHOP plus etoposide was significantly better than CHOP, with the latter used as a reference (coefficient of 0.11; p = 0.035), with adjustment for the proportion of International Prognostic Index score 4‐5 in meta‐regression analysis. Although grossly divided groups were pooled and analyzed, among four regimen groups for frontline PTCL treatment CHOP plus etoposide showed better survival than CHOP.
Estimating the effects of trait knowledge on social perception

Research in social cognition has predominantly investigated perceptual and inferential processes separately; however, real-world social interactions usually involve integration between person inferences (e.g., generous, selfish) and the perception of physical appearance (e.g., thin, tall). Therefore, in the current work, we investigated the integration of different person-relevant signals, by estimating the extent to which bias in one social information processing system influences another. Following an initial stimulus-validation experiment (Experiment 1, N = 55), two further pre-registered experiments (Experiments 2, N = 55 and 3; N = 123) employed a priming paradigm to measure the effects of extraversion-diagnostic information on subsequent health and body-size judgements of a target body. The results were consistent across both priming experiments and supported our predictions: compared to trait-neutral control statements, extraversion-diagnostic statements increased judgements of health and decreased those of body size. As such, we show that trait-based knowledge does not only influence mappings towards similar types of person judgements, such as health judgements. Rather, even a brief re-configuration of trait-space alters mappings towards non-trait judgements, which are based on body size and shape. The results complement prior neuroimaging findings that showed functional interactions between the body-selective brain regions in the ventral visual stream and the theory of mind network when forming impressions of others. Therefore, we provide a functional signature of how distinct information processing units exchange signals and integrate information to form impressions. Overall, the current study underscores the value of behavioural work in complementing neuroscience research when investigating the role and properties of functional integration during impression formation. In addition, it stresses the potential limitations of an over-reliance on studying separate systems in isolation.
Clinically oriented contour evaluation using geometric and dosimetric indices based on simple geometric transformations

Background: In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; however, the ability of geometric indices to identify the error of contouring results is limited, mainly because it does not consider any clinical background. To study the relationship between geometric indices and dosimetric indices, four different types of targets were selected to introduce the systematic and random geometric errors in the delineation process. Materials and Methods: The C-shaped target outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) and the targets of three actual cases of oropharyngeal cancer, metastatic spine cancer, and prostate cancer were selected as the test contours that needed to be modified. Python software was used to perform translation, scaling, rotating and sine function transformation to introduce systematic and random errors into the above contours. These geometrically transformed contours were regarded as reference contours corrected by systematic and random errors. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric and dosimetric indices were quantified through linear regression. The Wilcoxon signed rank test was used to compare the ability of spatial-direction discrimination between the geometric indices of different directions of transformations. Results: The correlations between the geometric and dosimetric indices were inconsistent for the targets. For systematic errors, except for the sine function transformation (R²: 0.023–0.04, p＞0.05), the geometric transformations of the C-shaped target’s planning target volume (C-PTV) had correlations with the dosimetric indices D98% and Dmean (R²: 0.689–0.988), 80% of which were strongly correlated (R² > 0.8). For the random errors, the correlation coefficients of the actual cases were also high, R2 > 0.384, p < 0.05. The results of Wilcoxon signed rank test showed that only the p-values of volumetric geometric indices of C-PTV were less than 0.05. Conclusions: Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices. Keywords: Contour evaluation, Geometric indices, Dosimetric indices, Geometric transformation
Regulating emotion following severe traumatic brain injury: a randomized controlled trial of heart-rate variability biofeedback training

ABSTRACT Background While difficulties regulating emotions are almost ubiquitous after traumatic brain injury (TBI), remediation techniques are limited. Heart-rate variability (HRV) is a physiological measure of emotion regulation and can be modified using biofeedback training. The aim of the current study was to evaluate the efficacy of repeated biofeedback training for improving emotion regulation difficulties following TBI. Design Fifty adults with severe TBI were allocated to either biofeedback or waitlist conditions. Treatment consisted of six biofeedback sessions whereby participants were taught to breathe at their resonant frequency. Outcomes included changes in physiological and subjective reactivity to anger-induction, emotional well-being, and physiology at rest, together with symptoms of psychological distress and sleep disturbances (ACTRN12618002031246). Results While biofeedback led to reduced skin conductance, it did not affect any other objective or subjective response to the mood induction procedure. Biofeedback led to fewer sleep disturbances, and reduced negative mood valence and depression during follow-up. Conclusions HRV biofeedback training is a feasible technique following TBI that transfers to improved symptoms of general emotional well-being, psychological distress, and sleep. Biofeedback does not transfer to a laboratory-based emotional provocation task. HRV biofeedback training may represent a novel adjunct for generalized emotional difficulties following injury.
Postsynthetic modification of single Pd sites into uncoordinated polypyridine groups of a MOF as the highly efficient catalyst for Heck and Suzuki reactions

A novel holmium(III) metal–organic framework (Ho-MOF), namely, [Ho(2-TriPP-COO)3] (2) has been hydrothermally obtained using 4′-(4-carboxyphenyl)-2,2′:6′,2′′-terpyridine (2-TriPP-COOH) and Ho(NO3)3·5H2O, and it is structurally characterized by single-crystal XRD, powder XRD as well as elemental analysis. The postsynthetic modification of Ho-MOF is based on the utilization of a strong coordination effect between Pd2+ ions and free polypyridine groups in the skeleton of Ho-MOF, which play a critical role to access the highly efficient Pd-HoMOF catalyst. Also, Pd-HoMOF exhibits very high activity in Heck and Suzuki–Miyaura cross-coupling reactions. Moreover, the MOF catalyst displays good thermal stability (up to 400 °C), and it can be recovered and reused for five reaction cycles. The bridging between the MOF structure and homogeneous molecular Pd catalyst represents a good example in designing highly efficient catalysts for various fine chemical transformations.
Intercultural Film Education Online

Film education is a concept that encompasses the ability to analyse the content and the context of films, to interpret and critically examine their meanings, and to produce a film of one's own. The research in film education has resulted in the development of various teaching practices and strategies, many of which are available online in the form of free and reusable open educational resources. Currently, the ongoing migration, expansion of online/distance education and internationalisation of schools and universities are influencing the way open educational resources about films are designed. This paper aims to understand pedagogic practices of the online open educational resources within the intercultural film education context. This research focuses on a single film education website IntoFilm.org and explores its open educational resources from an intercultural pedagogic perspective. The research data includes 160 Film Guides, which are analysed through qualitative content analysis. The findings of this study reveal that, although the website's open educational resources succeed in some of the aspects of intercultural education, they lack activities and discussion based on the international nature of filmmaking and cultural exchange and dialogue, which are fundamental aspects of interculturalism.
A Zone-Based Data Lake Architecture for IoT, Small and Big Data

Data lakes are supposed to enable analysts to perform more efficient and efficacious data analysis by crossing multiple existing data sources, processes and analyses. However, it is impossible to achieve that when a data lake does not have a metadata governance system that progressively capitalizes on all the performed analysis experiments. The objective of this paper is to have an easily accessible, reusable data lake that capitalizes on all user experiences. To meet this need, we propose an analysis-oriented metadata model for data lakes. This model includes the descriptive information of datasets and their attributes, as well as all metadata related to the machine learning analyzes performed on these datasets. To illustrate our metadata solution, we implemented a web application of data lake metadata management. This application allows users to find and use existing data, processes and analyses by searching relevant metadata stored in a NoSQL data store within the data lake. To demonstrate how to easily discover metadata with the application, we present two use cases, with real data, including datasets similarity detection and machine learning guidance.
Design of Microstrip Antenna for Mobile Communication Based on Substrate Integrated Waveguide

In the 5G mobile communication system, the performance of the antenna is of vital importance to the performance of the entire system. Compared with the traditional metal waveguide, the SIW structure microstrip antenna also has the electromagnetic performance of the traditional metal waveguide, high Q value, low loss, etc. And its structure is simple, the size is small, and it is easy to integrate with the planar circuit. However, the traditional SIW structure microstrip antenna has a narrow bandwidth, which limits its application in 5G systems. The bandwidth can be increased to a certain extent by slotting on the back of the antenna. Therefore, a cavity-backed microstrip antenna with SIW structure loaded with short-circuit array is designed. Its performance is excellent, it can realize three modes of common vibration, the bandwidth is up to 15.8%, the input impedance is small, the loss is small, the gain is stable in the working frequency band, and the radiation signal is stable.
Overview of Few-shot Learning

Discussion. A straight-forward way is to train the classifier on the support set. However, due to lack of labeled samples in each class, the performance is not satisfactory. One effective way to exploit training set is to mimic few-shot learning via episode-based training. In each episode, we sample C classes from training set and sample K examples from each of them. This gives us a train set. Also we sample several examples from the same C classes for a test set. These train/test sets align with support/query sets. We train the model to learn well on these train and test sets, so that it can generalize to support/query set. This is also the basic idea of meta-learning.