Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Robust Misclassiﬁcations of Neural Networks to Enhance Adversarial Attacks

Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluation is often imprecise, making it difﬁcult to identify promising approaches. We analyze the classiﬁcation decisions of 19 different state-of-the-art neural networks trained to be robust against adversarial attacks. Our ﬁndings suggest that current untargeted adversarial attacks induce misclassiﬁcation towards only a limited amount of different classes. Additionally, we observe that both over-and under-conﬁdence in model predictions result in an inaccurate assessment of model robustness. Based on these observations, we propose a novel loss function for adversarial attacks that consistently improves attack success rate compared to prior loss functions for 19 out of 19 analyzed models.
Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects

I provide two asymptotic normality results. The first concerns the case when the distribution of fwi has finite support in the sense that P (||fwi−fwj ||2 = 0) = P (||pwi−pwj ||2 = 0) > 0 and there exists an ǫ > 0 such that P (0 < ||fwi−fwj ||2 < ǫ) = P (0 < ||pwi−pwj ||2 < ǫ) = 0. This assumption is satisfied by the blockmodel of Holland et al. (1983) as discussed in Section 2.2. 1 of Auerbach (2019a) (see also Bickel et al. 2013).
Single file diffusion meets Feynman path integral

The path-integral representation of the Smoluchowski equation is exploited to explore the stochastic dynamics of a tagged Brownian particle within an interacting system where hydrodynamic effects are neglected. In particular, this formalism is applied to a particle system confined to a one-dimensional infinite line aiming to investigate the single-file diffusion phenomenon in this scenario. In particular, the path-integral method is contrasted against the standard many-particle Langevin equation for a system of interacting Brownian particles in a harmonic chain model, exhibiting excellent agreement; in this case of study a formula defined on the whole time-scale for the mean-square displacement, in the thermodynamic limit, is found for the tracer particle in terms of Bessel functions, recovering also the single-file regime. Additionally, a Brownian particle system with paramagnetic interactions is considered near crystallization where the total interaction potential is roughly a harmonic potential. Taking advantage of the path-integral formalism a simple perturbation treatment is carried out to investigate the single file diffusion behaviour when temperature is increased from the crystal phase, where we also validate our findings using Brownian dynamics simulation.
Ultrafast electronic linewidth broadening in the C 
1s
 core level of graphene

Core level binding energies and absorption edges are at the heart of many experimental techniques concerned with element-specific structure, electronic structure, chemical reactivity, elementary excitations and magnetism. X-ray photoemission spectroscopy (XPS) in particular, can provide information about the electronic and vibrational many-body interactions in a solid as these are reflected in the detailed energy distribution of the photoelectrons. Ultrafast pump-probe techniques add a new dimension to such studies, introducing the ability to probe a transient state of the many-body system. Here we use a free electron laser to investigate the effect of a transiently excited electron gas on the core level spectrum of graphene, showing that it leads to a large broadening of the C 1s peak. Confirming a decade-old prediction, the broadening is found to be caused by an exchange of energy and momentum between the photoemitted core electron and the hot electron system, rather than by vibrational excitations. This interpretation is supported by a line shape analysis that accounts for the presence of the excited electrons. Fitting the spectra to this model directly yields the electronic temperature of the system, in agreement with electronic temperature values obtained from valence band data. Furthermore, making use of time- and momentum-resolved C 1s spectra, we illustrate how the momentum change of the outgoing core electrons leads to a small but detectable change in the time-resolved photoelectron diffraction pattern and to a nearly complete elimination of the core level binding energy variation associated with the narrow $\sigma$-band in the C 1s state. The results demonstrate that the XPS line shape can be used as an element-specific and local probe of the excited electron system and that X-ray photoelectron diffraction investigations remain feasible at very high electronic temperatures.
Maximum and Leaky Maximum Propagation

In this work, we present an alternative to conventional residual connections, which is inspired by maxout nets. This means that instead of the addition in residual connections, our approach only propagates the maximum value or, in the leaky formulation, propagates a percentage of both. In our eval-uation, we show on different public data sets that the presented approaches are comparable to the residual connections and have other interesting properties, such as better generalization with a constant batch normalization, faster learning, and also the possibility to generalize without additional activation functions. In addition, the proposed approaches work very well if ensembles together with residual networks are formed. LinkToCodeBlind
Distributionally Robust Surrogate Optimal Control for High-Dimensional Systems

This article presents a novel methodology for tractably solving optimal control and offline reinforcement learning (RL) problems for high-dimensional systems. This work is motivated by the ongoing challenges of safety, computation, and optimality in high-dimensional optimal control. We address these key questions with the following approach. First, we identify a sequence-modeling surrogate methodology that takes as input the initial state and a time series of control inputs and outputs an approximation of the objective function and trajectories of constraint functions. Importantly this approach entirely absorbs the individual state transition dynamics. The sole dependence on the initial state means we can apply dimensionality reduction to compress the model input while retaining most of its information. Uncertainty in the surrogate objective will affect the resulting optimality. Critically, however, uncertainty in the surrogate constraint functions will lead to infeasibility, i.e., unsafe actions. When considering offline RL, the most significant modeling errors will be encountered on out-of-distribution (OOD) data. Therefore, we apply Wasserstein ambiguity sets to “robustify” our surrogate modeling approach subject to worst case out-of-sample modeling errors based on the distribution of test data residuals. We demonstrate the efficacy of this combined approach through a case study of safe optimal fast charging of a high-dimensional lithium-ion battery model at low temperatures.
BELT: Blockwise Missing Embedding Learning Transfomer

Matrix completion has attracted a lot of attention in many ﬁelds including statistics, applied mathematics and electrical engineering. Most of works focus on the independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices, we propose the model B lockwise missing E mbedding L earning T ransformer (BELT) to treat row-wise/column-wise missingness. Speciﬁcally, our proposed method aims at eﬃcient matrix recovery when every pair of matrices from multiple sources has an overlap. We provide theoretical justiﬁcation for the proposed BELT method. Simulation studies show that the method performs well in ﬁnite sample under a variety of conﬁgura-tions. The method is applied to integrate several PMI matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality.
Quantum metrology with one auxiliary particle in a correlated bath and its quantum simulation

In realistic metrology, entangled probes are more sensitive to noise, especially for a correlated environment. The precision of parameter estimation with entangled probes is even lower than that of the unentangled ones in a correlated environment. In this paper, we propose a measurement scheme with only one auxiliary qubit, which can selectively offset the impact of environmental noise under this situation. We analyse the estimation precision of our scheme and find out that it approaches the Heisenberg limit when prepared in a proper auxiliary state. We further discuss employing auxiliary states to improve the precision of measurement in other environment models such as a partially-correlated environment. In order to verify our scheme, we apply a recentlydeveloped quantum algorithm to simulate the quantum dynamics of our proposal and show that it outperform the other proposals with less resources.
Optimizing Neural Network Weights using Nature-Inspired Algorithms

This study aims to optimize Deep Feedforward Neural Networks (DFNNs) training using nature-inspired optimization algorithms, such as PSO, MTO, and its variant called MTOCL. We show how these algorithms efficiently update the weights of DFNNs when learning from data. We evaluate the performance of DFNN fused with optimization algorithms using three Wisconsin breast cancer datasets, Original, Diagnostic, and Prognosis, under different experimental scenarios. The empirical analysis demonstrates that MTOCL is the most performing in most scenarios across the three datasets. Also, MTOCL is comparable to past weight optimization algorithms for the original dataset, and superior for the other datasets, especially for the challenging Prognostic dataset.
Comment on Stochastic Polyak Step-Size: Performance of ALI-G

This is a short note on the performance of the ALI-G algorithm (Berrada et al., 2020) as reported in (Loizou et al., 2021). ALI-G (Berrada et al., 2020) and SPS (Loizou et al., 2021) are both adaptations of the Polyak step-size to optimize machine learning models that can interpolate the training data. The main algorithmic differences are that (1) SPS employs a multiplicative constant in the denominator of the learning-rate while ALI-G uses an additive constant, and (2) SPS uses an iteration-dependent maximal learning-rate while ALI-G uses a constant one. There are also differences in the analysis provided by the two works, with less restrictive assumptions proposed in (Loizou et al., 2021). In their experiments, (Loizou et al., 2021) did not use momentum for ALI-G (which is a standard part of the algorithm) or standard hyper-parameter tuning (for e.g. learning-rate and regularization). Hence this note as a reference for the improved performance that ALI-G can obtain with well-chosen hyper-parameters. In particular, we show that when training a ResNet-34 on CIFAR-10 and CIFAR-100, the performance of ALI-G can reach respectively 93.5% (+6%) and 76% (+8%) with a very small amount of tuning. Thus ALI-G remains a very competitive method for training interpolating neural networks. 1 Context For context, we provide a summary on the Adaptive Learning-Rate for Interpolation with Gradients (ALI-G) algorithm. More details can be found in (Berrada et al., 2020). Loss Function. We consider a supervised learning task where the model is parameterized by w ∈ R. The objective function is expressed as an expectation over z ∈ Z, a random variable indexing the samples of the training set: f(w) , Ez∈Z [lz(w)], (1) where each lz is the loss function associated with the sample z and is assumed to be non-negative: ∀w ∈ R , lz(w) ≥ 0. Regularization. We incorporate regularization (if any) as a constraint on the feasible domain: Ω = {
Fact-driven Logical Reasoning

Logical reasoning, which is closely related to human cognition, is of vital importance in human’s understanding of texts. Recent years have witnessed increasing attentions on machine’s logical reasoning abilities. However, previous studies commonly apply ad-hoc methods to model pre-deﬁned relation patterns, such as linking named entities, which only considers global knowledge components that are related to commonsense, without local perception of complete facts or events. Such methodology is obviously insufﬁcient to deal with complicated logical structures. Therefore, we argue that the natural logic units would be the group of backbone constituents of the sentence such as the subject-verb-object formed "facts", covering both global and local knowledge pieces that are necessary as the basis for logical reasoning. Beyond building the ad-hoc graphs, we propose a more general and convenient fact-driven approach to construct a supergraph on top of our newly deﬁned fact units, and enhance the supergraph with further explicit guidance of local question and option interactions. Experiments on two challenging logical reasoning benchmark datasets, ReClor and LogiQA, show that our proposed model, F OCAL R EASONER , outperforms the baseline models dramatically. It can also be smoothly applied to other downstream tasks such as MuTual, a dialogue reasoning dataset, achieving competitive results.
Nickel Complexes for Robust Light-Driven and Electrocatalytic Hydrogen Production from Water

A series of nickel bis(chelate) complexes having square planar coordination are studied for light-driven and electrocatalytic hydrogen production from water. The complexes Ni(abt)2 (abt = 2-aminobenzenethiolate), Ni(mp)2 (mp = 2-mercaptophenolate) and Ni(mpo)2 (mpo = 2-mercaptopyridyl-N-oxide) are found to be active catalysts under light-driven conditions, using fluorescein (Fl) as the photosensitizer (PS) and triethanolamine (TEOA) as the sacrificial electron donor in water under basic pH (pH = 9.8). These molecular systems achieve a turnover number (TON) of ∼6000 (relative to catalyst) and are stable for more than 100 h under H2-generating conditions. When water-soluble CdSe quantum dots with tripodal S-donor capping agents are employed as PS and ascorbic acid (AA) is used as the sacrificial electron donor at pH 4.5, an active and robust system is obtained for the light-driven generation of H2 from aqueous protons. A TON of over 280 000 is achieved for the three active catalysts. These complexes are als.
Rising to the challenges of the pandemic: Telehealth innovations in U.S. emergency departments

Abstract Objective During the first 9 months of the coronavirus disease 2019 (COVID-19) pandemic, many emergency departments (EDs) experimented with telehealth applications to reduce virus exposure, decrease visit volume, and conserve personal protective equipment. We interviewed ED leaders who implemented telehealth programs to inform responses to the ongoing COVID-19 pandemic and future emergencies. Materials and Methods From September to November 2020, we conducted semi-structured interviews with ED leaders across the United States. We identified EDs with pandemic-related telehealth programs through literature review and snowball sampling. Maximum variation sampling was used to capture a range of experiences. We used standard qualitative analysis techniques, consisting of both inductive and deductive approaches to identify and characterize themes. Results We completed 15 interviews with EDs leaders in 10 states. From March to November 2020, participants experimented with more than a dozen different types of telehealth applications including tele-isolation, tele-triage, tele-consultation, virtual postdischarge assessment, acute care in the home, and tele-palliative care. Prior experience with telehealth was key for implementation of new applications. Most new telehealth applications turned out to be temporary because they were no longer needed to support the response. The leading barriers to telehealth implementation during the pandemic included technology challenges and the need for “hands-on” implementation support in the ED. Conclusions In response to the COVID-19 pandemic, EDs rapidly implemented many telehealth innovations. Their experiences can inform future responses.
Effect of food on the single‐dose pharmacokinetics and tolerability of savolitinib in Chinese healthy volunteers

The aim of this study was to investigate the effect of a high‐fat and high‐calorie meal on the single‐dose pharmacokinetics (PK) and tolerability of savolitinib. The study included two phases: safety run‐in phase and food effect assessment phase. In the safety run‐in phase, 9 healthy male volunteers were divided into three groups to be administered a single oral dose of savolitinib tablets at 200, 400, and 600 mg. In the food effect assessment phase, 16 healthy male volunteers received a single 600 mg dose of savolitinib tablets following an overnight fast or a high‐fat and high‐calorie breakfast prior to dosing. Blood samples were collected at the designated time points for pharmacokinetic analysis. Safety and tolerability were assessed throughout the study by clinical assessments and adverse events (AEs). A total of 25 healthy male volunteers were enrolled in the study, including 9 in the safety run‐in phase and 16 in the food effect assessment phase. In the food effect assessment phase, the geometric mean ratios (90% confidence interval) for savolitinib dosed under the fed condition compared with that dosed under the fasting condition were 102.7% (84.9%, 124.2%) and 117.1% (103.9%, 131.9%) for Cmax and AUC0‐inf of savolitinib, respectively. The Tmax was delayed significantly (p = 0.023) under fed condition. The most common AEs possibly related to the study drug were dizziness, nausea, and emesis. The study indicated that a high‐fat and high‐calorie meal has no clinically relevant impact on the PK and bioavailability of savolitinib in healthy male volunteers.
The effects of psychosocial stress on item, cued‐pair and emotional memory

Physical stress, such as from the cold‐pressor test, has been robustly associated with altered memory retrieval, but it is less clear whether the same happens following psychosocial stress. Studies using psychosocial stressors report mixed effects on memory, leading to uncertainty about the common cognitive impact of both forms of stress. The current study uses a series of four carefully designed experiments, each differing by only a single critical factor to determine the effects of psychosocial stress on specific aspects of episodic memory. In three experiments, we induced psychosocial stress after participants encoded words, then assessed retrieval of those words after a prolonged delay. These experiments found no effect of post‐encoding stress on recognition of neutral words or cued recall of word‐pairs, but a small effect on recollection of semantically related words. There were, however, positive relationships within the stress group between measures of stress (cortisol in experiment 1 and self‐reported‐anxiety in experiment 3) and recollection of single word stimuli. In the fourth experiment, we found that psychosocial stress immediately before retrieval did not influence word recognition. Recollection, particularly for semantically related stimuli, may therefore be more susceptible to the effects of psychosocial stress, and future studies can assess how this relates to other forms of stress. Overall, our findings suggest that the effects of psychosocial stress on episodic memory may be more subtle than expected, warranting further exploration in larger studies.
Naxitamab combined with granulocyte‐macrophage colony‐stimulating factor as consolidation for high‐risk neuroblastoma patients in complete remission

Naxitamab is a humanized anti‐disialoganglioside (GD2) monoclonal antibody approved for treatment of bone/bone marrow refractory high‐risk neuroblastoma (HR‐NB). Compassionate use (CU) expanded access program at Hospital Sant Joan de Deu permitted treatment of patients in complete remission (CR). We here report the survival, toxicity, and relapse pattern of patients in first or second CR treated with naxitamab and sargramostim (GM‐CSF).
Salivary Cortisol and Alpha-Amylase in Posttraumatic Stress Disorder and Their Potential Role in the Evaluation of Cognitive Behavioral Treatment Outcomes.

Alterations in HPA-axis and autonomic nervous system activity have been associated with posttraumatic stress disorder (PTSD) development and maintenance and are potentially associated with trauma-focused cognitive behavioral therapy (TF-CBT) outcomes. We examined the role of salivary cortisol (sCort) and alpha-amylase (sAA) in PTSD and TF-CBT outcomes in German Armed Forces service members (N = 100). Participants categorized as PTSD patients (n = 39), previously deployed healthy controls (n = 33), and nondeployed healthy controls (n = 28) provided diurnal profiles of sCort and sAA; PTSD patients provided samples before, immediately after, and 3 months after an internet-based TF-CBT intervention. No group differences emerged regarding total daily sCort and sAA output or daily slopes, ps = . 224-. 897, fs = 0.05-0.24. Participants with PTSD demonstrated a significantly attenuated sCort awakening response compared to deployed, p = . 021, d = 0.59, but not nondeployed controls, p = . 918, d = 0.08. Moreover, a significantly steeper sAA awakening response emerged in PTSD patients, p = . 034, d = 0.67, and deployed controls, p = . 014, d = 0.80, compared to nondeployed controls. From pretreatment to posttreatment (n = 21) and posttreatment to follow-up (n = 14), stable sCort, ps = . 282-. 628, fs = 0.34-0.49, and sAA concentrations, ps = . 068-. 758, fs = 0.24-1.13 paralleled a nonsignificant treatment effect. Both PTSD and trauma exposure were associated with alterations in awakening responses, but further investigation is needed to determine whether the observed correspondence remains when PTSD symptoms significantly decline.
Leaf anatomical characteristics are less important than leaf chemical properties in determining photosynthesis responses to top-dress N.

The photosynthetic capacity of leaves is dramatically influenced by nitrogen (N) available in the soil, as CO2 concentration in chloroplasts and photosynthetic biochemical capacity are related to leaf N content. The relationship between mesophyll conductance (gm) and leaf N content was expected to be shaped by leaf anatomical traits. However, the increased gm in mature leaf by N topdressing is unlikely caused by leaf anatomy changes. We assessed the impacts of N supply on leaf anatomical, biochemical, and photosynthetic features, especially, the dynamic responses of leaf anatomy, biochemistry, and photosynthesis to N topdressing in tobacco. Plant performance was substantially impacted by soil N status. In comparison with the leaves of low N (LN) treatment, the leaves with high N treatment (HN) had significantly higher photosynthesis due to the higher CO2 diffusion conductance and photosynthetic biochemical capacity. The high gm in HN leaves apparently related to leaf anatomical modification, however, the rapid response of gm to top-dressed N cannot be fully explained by leaf anatomical modification.