Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does a Deep Learning Model Architecture Impact Its Privacy?

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from convolutional neural networks (CNNs) to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks than CNNs. Additionally, we identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures.
PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points

Traditional temporal action detection (TAD) usually handles untrimmed videos with small number of action instances from a single label (e.g., ActivityNet, THUMOS). However, this setting might be unrealistic as different classes of actions often co-occur in practice. In this paper, we focus on the task of multi-label temporal action detection that aims to localize all action instances from a multi-label untrimmed video. Multi-label TAD is more challenging as it requires for fine-grained class discrimination within a single video and precise localization of the co-occurring instances. To mitigate this issue, we extend the sparse query-based detection paradigm from the traditional TAD and propose the multi-label TAD framework of PointTAD. Specifically, our PointTAD introduces a small set of learnable query points to represent the important frames of each action instance. This point-based representation provides a flexible mechanism to localize the discriminative frames at boundaries and as well the important frames inside the action. Moreover, we perform the action decoding process with the Multi-level Interactive Module to capture both point-level and instance-level action semantics. Finally, our PointTAD employs an end-to-end trainable framework simply based on RGB input for easy deployment. We evaluate our proposed method on two popular benchmarks and introduce the new metric of detection-mAP for multi-label TAD. Our model outperforms all previous methods by a large margin under the detection-mAP metric, and also achieves promising results under the segmentation-mAP metric. Code is available at https://github.com/MCG-NJU/PointTAD.
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning

Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this vulnerability, prior work proposed both principled defenses based on differential privacy, as well as heuristic defenses based on gradient compression as countermeasures. These defenses have so far been very effective, in particular those based on gradient compression that allow the model to maintain high accuracy while greatly reducing the effectiveness of attacks. In this work, we argue that such findings underestimate the privacy risk in FL. As a counterexample, we show that existing defenses can be broken by a simple adaptive attack, where a model trained on auxiliary data is able to invert gradients on both vision and language tasks.
Exploring Causes, Effects, and Solutions to Financial Illiteracy and Exclusion among Minority Demographic Groups

Americans across demographic groups tend to have low financial literacy, with low-income people and minorities at highest risk. This opens the door to the exploitation of unbanked low-income families through high-interest alternative financial services. This paper studies the causes and effects of financial illiteracy and exclusion in the most at-risk demographic groups, and solutions proven to bring them into the financial mainstream. This paper finds that immigrants, ethnic minorities, and low-income families are most likely to be unbanked. Furthermore, the causes for being unbanked include the high fees of bank accounts, the inability of Americans to maintain bank accounts due to low financial assets or time, banking needs being met by alternative financial services, and being provided minimal help while transitioning from welfare to the workforce. The most effective solutions to financial illiteracy and exclusion include partnerships between nonprofits, banks, and businesses that use existing alternative financial service platforms to transition the unbanked into using products that meet their needs, educating the unbanked in the use of mobile banking, and providing profitable consumer credit products targeting unbanked families with features that support their needs in addition to targeted and properly implemented financial literacy programs.
Berezinskii-Kosterlitz-Thouless phase transition with Rabi-coupled bosons

We theoretically investigate the superfluid-normal-state Berezinskii-Kosterlitz-Thouless transition in a binary mixture of bosonic atoms with Rabi coupling under balanced densities. We find the nonmonotonic behavior of the transition temperature with respect to the intercomponent coupling and amplification of the transition temperature for finite values of Rabi coupling, but for small intracomponent couplings. We develop the Nelson-Kosterlitz renormalization-group equations in the two-component Bose mixture and obtain the Nelson-Kosterlitz criterion modified by a fractional parameter, which is responsible for half-integer vortices, and by Rabi coupling. Adopting the renormalization-group approach, we clarify the dependence of the Berezinskii-Kosterlitz-Thouless transition temperature on the Rabi coupling and the intercomponent coupling. Analysis of the first and second sound velocities also reveals the suppression of quasicrossing of the two sound modes with a finite Rabi coupling in the low-temperature regime. Our results for a two-dimensional binary Bose superfluid contribute to the understanding of a broad range of multicomponent quantum systems such as two-dimensional multiband superconductors.
A generalized expansion method for computing Laplace-Beltrami eigenfunctions on manifolds

Eigendecomposition of the Laplace–Beltrami operator is instrumental for a variety of applications from physics to data science. We develop a numerical method of computation of the eigenvalues and eigenfunctions of the Laplace–Beltrami operator on a smooth bounded domain based on the relaxation to the Schr¨odinger operator with ﬁnite potential on a Riemannian manifold and projection in a special basis. We prove spectral exactness of the method and provide examples of calculated results and applications, particularly, in quantum billiards on manifolds.
Distributionally Robust Gaussian Process Regression and Bayesian Inverse Problems

We study a distributionally robust optimization formulation (i.e., a min-max game) for two representative problems in Bayesian nonparametric estimation: Gaussian process regression and, more generally, linear inverse problems. Our formulation seeks the best mean-squared error predictor, in an infinite-dimensional space, against an adversary who chooses the worst-case model in a Wasserstein ball around a nominal infinite-dimensional Bayesian model. The transport cost is chosen to control features such as the degree of roughness of the sample paths that the adversary is allowed to inject. We show that the game has a well-defined value (i.e., strong duality holds in the sense that max-min equals min-max) and that there exists a unique Nash equilibrium which can be computed by a sequence of finite-dimensional approximations. Crucially, the worst-case distribution is itself Gaussian. We explore properties of the Nash equilibrium and the effects of hyperparameters through a set of numerical experiments, demonstrating the versatility of our modeling framework.
Asymptotic Analysis of Conditioned Stochastic Gradient Descent

In this paper, we investigate a general class of stochastic gradient descent (SGD) algorithms, called Conditioned SGD, based on a preconditioning of the gradient direction. Using a discrete-time approach with martingale tools, we establish under mild assumptions the weak convergence of the rescaled sequence of iterates for a broad class of conditioning matrices including stochastic first-order and second-order methods. Almost sure convergence results, which may be of independent interest, are also presented. Interestingly, the asymptotic normality result consists in a stochastic equicontinuity property so when the conditioning matrix is an estimate of the inverse Hessian, the algorithm is asymptotically optimal.
Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models

Current Natural Language Inference (NLI) models achieve impressive results, sometimes outperforming humans when evaluating on in-distribution test sets. However, as these models are known to learn from annotation artefacts and dataset biases, it is unclear to what extent the models are learning the task of NLI instead of learning from shallow heuristics in their training data.We address this issue by introducing a logical reasoning framework for NLI, creating highly transparent model decisions that are based on logical rules. Unlike prior work, we show that improved interpretability can be achieved without decreasing the predictive accuracy. We almost fully retain performance on SNLI, while also identifying the exact hypothesis spans that are responsible for each model prediction.Using the e-SNLI human explanations, we verify that our model makes sensible decisions at a span level, despite not using any span labels during training. We can further improve model performance and the span-level decisions by using the e-SNLI explanations during training. Finally, our model is more robust in a reduced data setting. When training with only 1,000 examples, out-of-distribution performance improves on the MNLI matched and mismatched validation sets by 13% and 16% relative to the baseline. Training with fewer observations yields further improvements, both in-distribution and out-of-distribution.
Unified Network Equation for Both Normal RLC Circuits and Josephson Junction Circuits

—Josephson junction circuits, such as superconducting quantum interference devices (SQUIDs) and single-flux-quantum (SFQ) circuits, have been successfully applied in both analog and digital electronic domains. They are considered to be distinct from the normal resistor-inductor-capacitor (RLC) circuits, due to the superconducting physics. To bridge the gap between Josephson junction circuits and conventional RLC circuits, we introduce a magnetic-flux-generator (MFG) model to unify the definitions of Josephson junctions and normal noninductive components, and transform their circuits uniformly into MFG networks. A general network equation for both superconducting and normal MFG networks is derived. It shows that, Josephson junction circuits are the superconducting RLC networks modulated by Josephson currents; their analysis and simulation are unified with that of the normal RLC circuits by solving the general network equation.
Correlation of Postvaccination Fever With Specific Antibody Response to Severe Acute Respiratory Syndrome Coronavirus 2 BNT162b2 Booster and No Significant Influence of Antipyretic Medication

Abstract Background A severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNA vaccine booster elicits sufficient antibody responses that protect against coronavirus disease 2019, whereas adverse reactions such as fever have been commonly reported. Associations between adverse reactions and antibody responses have not been fully characterized, nor has the influence of antipyretic use. Methods This is a prospective observational cohort study in Japan, following our prior investigation of BNT162b2 2-dose primary series. Spike-specific immunoglobulin G (IgG) titers were measured for SARS-CoV-2–naive hospital healthcare workers who received a BNT162b2 booster. The severity of solicited adverse reactions, including the highest body temperature, and self-medicated antipyretics were reported daily for 7 days following vaccination through a web-based self-reporting diary. Results The data of 281 healthcare workers were available. Multivariate analysis extracted fever after the booster dose (β = .305, P < .001) as being significantly correlated with the specific IgG titers. The analysis of 164 participants with data from the primary series showed that fever after the second dose was associated with the emergence of fever after the booster dose (relative risk, 3.97 [95% confidence interval, 2.48–6.35]); however, the IgG titers after the booster dose were not associated with the presence or degree of fever after the second dose. There were no significant differences in the IgG titers by the use, type, or dosage of antipyretic medication. Conclusions These results suggest an independent correlation between mRNA vaccine–induced specific IgG levels and post–booster vaccination fever, without any significant influence of fever after the primary series. Antipyretic medications for adverse reactions should not interfere with the elevation of specific IgG titers.
Surgical Management of Peripheral Ulcerative Keratitis: Update on Surgical Techniques and Their Outcome

Abstract Peripheral ulcerative keratitis (PUK) is an inflammatory, necrotic condition in the peripheral cornea which may end in corneal perforation and visual morbidity if not treated adequately. PUK can occur due to infectious or non-infectious causes. Early cases need medical therapy, both locally and systemically (for some cases). However, advanced PUK may necessitate surgical removal of inciting cause of the pathology and maintaining tectonic stability. Such surgical treatment, including corneal transplantations, may be used in an emergency setting or for visual rehabilitation following preliminary stabilization of the affected cornea. The outcome of these surgeries need to be analyzed to understand the long-term visual prognosis of such eyes. This is an attempt to analyze surgical modalities in the management of PUK and their outcomes.
Does Recovered COVID-19 Patients Have Protective Reactions in Subsequent Reexposures to the Virus?

Some of the recovered COVID‐19 patients had shown re‐positive by polymerase chain reaction (PCR) test results after discharge from the hospital. There are several reasons why recovered COVID‐19 patients re‐positive for Coronavirus 2 (CoV‐2) including (1) false‐negative results of pharyngeal swabs before the patient was discharged from the hospital, due to low sensitivity, sampling procedures, temperature, transportation, and quality of kits. [2] (2) Dead viruses or gene fragments maybe send a positive signal of viral RNA without active viral replications. (3) It might be some of the COVID‐19 patients did not completely meet the actual discharge criteria. The large time interval between the viral RNA tests before discharge and the actual discharge date induces the viral RNA tests unrepeatability. [3,4] However, the antibody responses against COVID‐19 are poorly understood and the main question is the reinfection possibility of recovered patients with the subsequent reexposures.
A Real-World Study of Optimal Treatment with Anlotinib First-Line Therapy in Advanced Hepatocellular Carcinoma

Purpose To observe the efficacy and safety of anlotinib as a first-line treatment for patients with advanced hepatocellular carcinoma (aHCC) in a real-word environment, explore the optimal treatment regimen for patients with aHCC using anlotinib as a first-line treatment. Patients and Methods Data from 62 patients with aHCC who received anlotinib single-drug first-line therapy between February 2019 and November 2021. Patients received anlotinib monotherapy, which may be interrupted or discontinued or changed in the event of unacceptable or severe adverse events (AEs) or failure to inhibit tumor progression. The primary endpoint was progression-free survival (PFS) and the secondary endpoints were objective response rate(ORR), disease control rate (DCR), overall survival (OS), and safety. Results Among the 62 patients, in the best overall response assessment, there were 12 with complete response (CR; 19.4%), 17 with partial response (PR; 27.4%), 25 with stable disease (SD; 40.3%), and 8 with progressive disease (PD; 14.5%). The ORR and DCR were 46.8% and 87.1%, respectively. Among the 11 patients who received tyrosine kinase inhibitors (TKIs) combined with programmed death 1 (PD-1) inhibitors after disease progression, three (27.3%) had CR, one (9.1%) had PR, three (27.3%) had SD, and four (36.4%) had PD. Therefore, the ORR and DCR were 36.4% and 63.6%, respectively. The median PFS for anlotinib monotherapy was 7.37 months (95% confidence interval [CI]: 5.88–8.86) and the median OS did not reach. AEs occurred in 95.2% of patients during anlotinib monotherapy, with the most common being thrombocytopenia (51.6%). The incidence of grade ≥3 AEs was 38.7%. Conclusion Anlotinib is effective and well-tolerated as a first-line treatment for patients with aHCC. Treatment with TKIs and PD-1 inhibitors after disease progression has also shown preliminary efficacy and safety; therefore, sequential therapy with anlotinib-TKIs and PD-1 inhibitors may be an effective treatment for patients with aHCC.
Major Dietary Patterns in Association with the Grades of Nonalcoholic Fatty Liver Disease in Newly Diagnosed Patients Living in North of Iran

Background: Evidence suggests that dietary pattern is related to incidence and grades of nonalcoholic fatty liver disease (NAFLD). The aim of this study was to investigate the relationship between major dietary patterns and hepatic histologic features of newly diagnosed patients with NAFLD. Methods: This cross-sectional study included 260 newly diagnosed NAFLD patients. Hepatic fibrosis and steatosis were diagnosed using Fibroscan. Dietary information was obtained using a validated 168-item semiquantitative food frequency questionnaire. The association between dietary patterns and odds ratio of the grade of fibrosis and steatosis was examined by multinomial logistic regression. Results: The mean ± SD age participant was 46.53 ± 11.71 years. Fifty-seven percent of the participants were women. Two major dietary patterns were recognized: “Western dietary pattern” and “Mediterranean dietary pattern. ” After adjustment for various confounders, adherence to the two extracted dietary patterns was not associated with odds of fibrosis and steatosis (P > 0.05). However, P was not significant. Patients in the second quartile of the Mediterranean dietary pattern had a higher risk for being in the F1 grade compared to those in the reference group in the crude and adjusted model. Conclusions: The major dietary patterns of NAFLD patients living in Gilan were not related to the severity of their disease. More precise study design such as cohort or interventional studies is suggested to reveal the strength of this study findings.
The Effect of Zinc Supplementation on Circulating Levels of Brain-Derived Neurotrophic Factor (BDNF): A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: There are randomized controlled trials (RCTs) about the zinc supplementation effect on circulating levels of brain-derived neurotrophic factor (BDNF). However, the findings of these studies are inconsistent. The purpose of this systematic review and meta-analysis was to determine the zinc supplementation effect on BDNF and zinc levels in published RCTs. Methods: We searched PubMed/Medline, Cochrane, Scopus, ISI Web of Science, EMBASE, “Clinicaltrials.gov”, “Cochrane Register of Controlled Trials”, “IRCT” and also key journals up to 2019. RCTs with two intervention (zinc) and control (placebo) groups that evaluated zinc supplementation efficacy on BDNF levels were included. Study heterogeneity was assessed, and then, meta-analysis was performed using the fixed-effects model. Results: Four studies were included in the present secondary analysis. Compared with placebo, zinc supplementation significantly enhanced circulating levels of BDNF [(SMD): 0.31, 95% confidence interval (CI): (0.22, 0.61)] and zinc [(SMD): 0.88, 95% CI: (0.54, 1.22)] with no considerable heterogeneity among the studies [(Q = 3.46; P = 0.32; I2% = 13.4); (Q = 2.01; P = 0, 37; I2% = 0.5), respectively]. Conclusions: Our results propose that zinc supplementation can increase the circulating levels of BDNF and zinc. This study was registered at PROSPERO as CRD42020149513.
THE VLA NASCENT DISK AND MULTIPLICITY (VANDAM) SURVEY OF PERSEUS PROTOSTARS. RESOLVING THE SUB-ARCSECOND BINARY SYSTEM IN NGC 1333 IRAS2A

We are conducting a Jansky Very Large Array (VLA) Ka-band (8 mm and 1 cm) and C-band (4 cm and 6.4 cm) survey of all known protostars in the Perseus Molecular Cloud, providing resolution down to ∼0. ″06 and ∼0. ″35 in the Ka band and C band, respectively. Here we present first results from this survey that enable us to examine the source NGC 1333 IRAS2A in unprecedented detail and resolve it into a protobinary system separated by 0. ″621 ± 0. ″006 (∼143 AU) at 8 mm, 1 cm, and 4 cm. These two sources (IRAS2A VLA1 and VLA2) are likely driving the two orthogonal outflows known to originate from IRAS2A. The brighter source IRAS2A VLA1 is extended perpendicular to its outflow in the VLA data, with a deconvolved size of 0. ″055 (∼13 AU), possibly tracing a protostellar disk. The recently reported candidate companions (IRAS2A MM2 and MM3) are not detected in either our VLA data, Combined Array for Research in Millimeter-wave Astronomy (CARMA) 1.3 mm data, or Submillimeter Array (SMA) 850 μm data. SMA CO (J = 3 → 2), CARMA CO (J = 2 → 1), and lower-resolution CARMA CO (J = 1 → 0) observations are used to examine the outflow origins and the nature of the candidate companions to IRAS2A VLA1. The CO (J = 3 → 2) and (J = 2 → 1) data show that IRAS2A MM2 is coincident with a bright CO emission spot in the east–west outflow, and IRAS2A MM3 is within the north–south outflow. In contrast, IRAS2A VLA2 lies at the east–west outflow symmetry point. We propose that IRAS2A VLA2 is the driving source of the east–west outflow and a true companion to IRAS2A VLA1, whereas IRAS2A MM2 and MM3 may not be protostellar.