Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystallinity of the Homogenized Energy Density of Periodic Lattice Systems

We study the homogenized energy densities of periodic ferromagnetic Ising systems. We prove that, for finite range interactions, the homogenized energy density, identifying the effective limit, is crystalline, i.e. its Wulff crystal is a polytope, for which we can (exponentially) bound the number of vertices. This is achieved by deriving a dual representation of the energy density through a finite cell formula. This formula also allows easy numerical computations: we show a few experiments where we compute periodic patterns which minimize the anisotropy of the surface tension.
A B AYESIAN ADAPTIVE DESIGN FOR DUAL - AGENT PHASE I-II CANCER CLINICAL TRIALS COMBINING EFFICACY DATA ACROSS STAGES

Combination of several anti-cancer treatments has typically been presumed to have enhanced drug activity. Motivated by a real clinical trial, this paper considers an integrated phase I-II dose ﬁnding design in two stages for dual-agent combinations, where one main objective is to characterise both the toxicity and efﬁcacy proﬁles. We propose a novel Bayesian adaptive design that accommodates the change of patient population across stages. Speciﬁcally, in stage I, we escalate the dose following the escalation with overdose control (EWOC) principle. In stage II, to ﬁnd the most efﬁcacious dose combination, we implement a robust Bayesian hierarchical random-effects model to allow sharing of information on the efﬁcacy across stages, assuming that the related parameters are either exchangeable or nonexchangeable. Under the assumption of exchangeability, a random-effects distribution is speciﬁed for the main effects parameters to capture uncertainty about the between-stage differences. The inclusion of nonexchangeability assumption further enables that the stage-speciﬁc efﬁcacy parameters have their own priors. The proposed methodology is assessed with extensive simulations, which show that with conservative assumptions about the ( a priori ) exchangeability of the parameters related to efﬁcacy across stages, there is a generalized improvement of the operating characteristics in stage II.
An analytical theory of curriculum learning in teacher–student networks

In animals and humans, curriculum learning—presenting data in a curated order—is critical to rapid learning and effective pedagogy. A long history of experiments has demonstrated the impact of curricula in a variety of animals but, despite its ubiquitous presence, a theoretical understanding of the phenomenon is still lacking. Surprisingly, in contrast to animal learning, curricula strategies are not widely used in machine learning and recent simulation studies reach the conclusion that curricula are moderately effective or even ineffective in most cases. This stark difference in the importance of curriculum raises a fundamental theoretical question: when and why does curriculum learning help? In this work, we analyse a prototypical neural network model of curriculum learning in the high-dimensional limit, employing statistical physics methods. We study a task in which a sparse set of informative features are embedded amidst a large set of noisy features. We analytically derive average learning trajectories for simple neural networks on this task, which establish a clear speed benefit for curriculum learning in the online setting. However, when training experiences can be stored and replayed (for instance, during sleep), the advantage of curriculum in standard neural networks disappears, in line with observations from the deep learning literature. Inspired by synaptic consolidation techniques developed to combat catastrophic forgetting, we propose curriculum-aware algorithms that consolidate synapses at curriculum change points and investigate whether this can boost the benefits of curricula. We derive generalisation performance as a function of consolidation strength (implemented as an L 2 regularisation/elastic coupling connecting learning phases), and show that curriculum-aware algorithms can yield a large improvement in test performance. Our reduced analytical descriptions help reconcile apparently conflicting empirical results, trace regimes where curriculum learning yields the largest gains, and provide experimentally-accessible predictions for the impact of task parameters on curriculum benefits. More broadly, our results suggest that fully exploiting a curriculum may require explicit adjustments in the loss.
What Did Minorities Do Post 2008?

We study the behavior of minorities over the 2007-09 period. We find that, despite the Great Recession, post-2008 and compared to white Americans, minorities have a higher propensity to increase risk tolerance, optimism, and allocations to risky assets and lower probability of exiting the stock market. These findings cannot be explained by changes in wealth or income and we ascribe them to the positive effects of the election of President Obama who is a role model for minorities. Overall, increased visibility of minority role models may encourage investing in risk assets by minorities and potentially alleviate the racial wealth gap.
Fast Decentralized Nonconvex Finite-Sum Optimization with Recursive Variance Reduction

This paper considers decentralized minimization of $N:=nm$ smooth non-convex cost functions equally divided over a directed network of $n$ nodes. Specifically, we describe a stochastic first-order gradient method, called GT-SARAH, that employs a SARAH-type variance reduction technique and gradient tracking (GT) to address the stochastic and decentralized nature of the problem. We show that GT-SARAH, with appropriate algorithmic parameters, finds an $\epsilon$-accurate first-order stationary point with $O\big(\max\big\{N^{\frac{1}{2}},n(1-\lambda)^{-2},n^{\frac{2}{3}}m^{\frac{1}{3}}(1-\lambda)^{-1}\big\}L\epsilon^{-2}\big)$ gradient complexity, where ${(1-\lambda)\in(0,1]}$ is the spectral gap of the network weight matrix and $L$ is the smoothness parameter of the cost functions. This gradient complexity outperforms that of the existing decentralized stochastic gradient methods. In particular, in a big-data regime such that ${n = O(N^{\frac{1}{2}}(1-\lambda)^{3})}$, this gradient complexity furthers reduces to ${O(N^{\frac{1}{2}}L\epsilon^{-2})}$, independent of the network topology, and matches that of the centralized near-optimal variance-reduced methods. Moreover, in this regime GT-SARAH achieves a non-asymptotic linear speedup, in that, the total number of gradient computations at each node is reduced by a factor of $1/n$ compared to the centralized near-optimal algorithms that perform all gradient computations at a single node. To the best of our knowledge, GT-SARAH is the first algorithm that achieves this property. In addition, we show that appropriate choices of local minibatch size balance the trade-offs between the gradient and communication complexity of GT-SARAH. Over infinite time horizon, we establish that all nodes in GT-SARAH asymptotically achieve consensus and converge to a first-order stationary point in the almost sure and mean-squared sense.
Estimating Welfare Effects in a Nonparametric Choice Model: The Case of School Vouchers

We develop new robust discrete choice tools to learn about the average willingness to pay for a price subsidy and its effects on demand given exogenous, discrete variation in prices. Our starting point is a nonparametric, nonseparable model of choice. We exploit the insight that our welfare parameters in this model can be expressed as functions of demand for the different alternatives. However, while the variation in the data reveals the value of demand at the observed prices, the parameters generally depend on its values beyond these prices. We show how to sharply characterize what we can learn when demand is specified to be entirely nonparametric or to be parameterized in a flexible manner, both of which imply that the parameters are not necessarily point identified. We use our tools to analyze the welfare effects of price subsidies provided by school vouchers in the DC Opportunity Scholarship Program. We robustly find that the provision of the status quo voucher and a wide range of counterfactual vouchers of different amounts have positive benefits net of costs. This positive effect can be explained by the popularity of low-tuition schools in the program; removing them from the program can result in a negative net benefit. Relative to our bounds, we also find that comparable logit estimates potentially understate the benefits for certain voucher amounts, and provide a misleading sense of robustness for alternative amounts.
Localization Dynamics from Impurities and Quantum Gauge Theories

We study the superfluid response and localization dynamics from impurities and gauge theories. The superfluidity is formed in the rung-Mott phase of a bosonic ladder model producing spinMeissner currents induced by a U(1) gauge field or a uniform magnetic field. Impurities are described through two-state systems which act as a two-peak random potential. An impurity sits either at the top or at the bottom of the ladder on each rung equally, producing a telegraph signal. The impurities-matter coupling gives rise to a Z2 symmetry for static impurities and we formulate a correspondence to Z2 gauge theories for mobile impurities. The properties of the system are studied from an effective quantum spin model including the possibility of four-body coupling in the limit of a strong interaction between bosons and impurities. Through analytical approaches and numerical exact diagonalization, we analyse the superfluid currents both in the weakly-coupled and stronglycoupled rungs limits for the bosons. In the weakly-coupled rungs situation, we find a smooth powerlaw localization whereas the strongly-coupled rungs limit produces a steep localization or insulating phase for various configurations of the two-peak random potential. In the strongly disordered situation, through entanglement and bipartite fluctuation measures, we also identify a many-body localization regime in time after a quench of the system when prepared in a Néel state.
Natural language adversarial defense through synonym encoding

In the area of natural language processing, deep learning models are recently known to be vulnerable to various types of adversarial perturbations, but relatively few works are done on the defense side. Especially, there exists few effective defense method against the successful synonym substitution based attacks that preserve the syntactic structure and semantic information of the original text while fooling the deep learning models. We contribute in this direction and propose a novel adversarial defense method called Synonym Encoding Method (SEM). Specifically, SEM inserts an encoder before the input layer of the target model to map each cluster of synonyms to a unique encoding and trains the model to eliminate possible adversarial perturbations without modifying the network architecture or adding extra data. Extensive experiments demonstrate that SEM can effectively defend the current synonym substitution based attacks and block the transferability of adversarial examples. SEM is also easy and efficient to scale to large models and big datasets.
On Commonsense Cues in BERT for Solving Commonsense Tasks

BERT has been used for solving commonsense tasks such as CommonsenseQA. While prior research has found that BERT does contain commonsense information to some extent, there has been work showing that pre-trained models can rely on spurious associations (e.g., data bias) rather than key cues in solving sentiment classification and other problems. We quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsense tasks, and the importance of such cues for the model prediction. Using two different measures, we find that BERT does use relevant knowledge for solving the task, and the presence of commonsense knowledge is positively correlated to the model accuracy.
Composing Normalizing Flows for Inverse Problems

Given an inverse problem with a normalizing flow prior, we wish to estimate the distribution of the underlying signal conditioned on the observations. We approach this problem as a task of conditional inference on the pre-trained unconditional flow model. We first establish that this is computationally hard for a large class of flow models. Motivated by this, we propose a framework for approximate inference that estimates the target conditional as a composition of two flow models. This formulation leads to a stable variational inference training procedure that avoids adversarial training. Our method is evaluated on a variety of inverse problems and is shown to produce highquality samples with uncertainty quantification. We further demonstrate that our approach can be amortized for zero-shot inference.
Identity Politics and Trade Policy GENE

We characterize trade policies that result from political competition when assessments of wellbeing include both material and psychosocial components. The material component reflects, as usual, satisfaction from consumption. Borrowing from social identity theory, we take the psychosocial component as combining the pride and self-esteem an individual draws from the status of groups with which she identifies and a dissonance cost she bears from identifying with those that are different from herself. In this framework, changes in social identification patterns that may result, for example, from increased income inequality or heightened class or ethnic tensions, lead to pronounced changes in trade policy. We analyse the nature of these policy changes.
Dilational Properties of Sodium 2,5-Dialkyl Benzene Sulfonates at Air−Water and Decane−Water Interfaces

The dilational properties of adsorbed film of sodium 2,5-dialkyl benzene sulfonates at the air−water and decane−water interfaces have been investigated by a Langmuir trough method. The influences of oscillating frequency and bulk concentration on dilational modulus and phase angle were expounded. The experimental results showed the hydrophobic chains located at different positions have different effects on dilational properties: for the air−water interface, the short alkyl chain ortho to sulfonate group produces a small increase in dilational modulus, while an increase in long para-alkyl plays an important role in the surface relaxation process, which leads to a higher surface dilational modulus. In the case of the decane−water interface, the interfacial dilational moduli are obviously lower than those of the air−water interface due to the insertion of decane molecules from oil phase. Moreover, the strong interactions between para-alkyl chains were weakened by decane molecules; therefore, the interfacial .
Study of Wetting and Adhesion Interactions between Water and Various Polymer and Superhydrophobic Surfaces

The wetting and adhesion characteristics of 20 different surfaces have been studied systematically by both static water contact angle (θ) and dynamic contact angle measurement techniques: sliding angle (α) and advancing (θA) and receding (θR) contact angles. These surfaces cover surfaces of all traits, from smooth and flat to rough and artificially textured. Fourteen of the surfaces are flat, and they range from molded plastic sheets to solution coated polymer films to chemical vapor deposition polymerized polymer films and to self-assembled monolayers on Si wafers. The rest of the surfaces include 4 fluorosilane coated textured Si wafer surfaces and two natural surfaces derived from the front and back side of the rose petal. Static water contact angle data suggest that these surfaces vary from hydrophilic with θ at ∼71° to superhydrophobic with θ exceeding 150°. Plots of θ of these surfaces versus α, (cos θR – cos θA), and the contact angle hysteresis (θA – θR) all yield scattered plots, indicating that .
Synthesis and Characterizations of PdNi Carbon Supported Nanomaterials: Studies of Electrocatalytic Activity for Oxygen Reduction in Alkaline Medium

Electrocatalytic materials offer numerous benefits due to their wide range of applications. In this study, a polyol technique was used to synthesize PdNi nanoparticles (NPs) with different percent atomic compositions (Pd = 50 to 90%) to explore their catalytic efficiency. The produced nanoparticles were characterized using X-ray diffraction (XRD) and electrochemical investigations. According to XRD measurements, the synthesized NPs were crystalline in nature, with crystallite sizes of about 2 nm. The electrochemical properties of the synthesized NPs were studied in alkaline solution through a rotating ring-disk electrode (RRDE) technique of cyclic voltammetry. The PdNi nanoparticles supported on carbon (PdNi/C) were used as electrocatalysts and their activity and stability were compared with the homemade Pd/C and Pt/C. In alkaline solution, PdNi/C electrocatalysts showed improved oxygen reduction catalytic activity over benchmark Pd/C and Pt/C electrocatalysts in all composition ratios. Furthermore, stability experiments revealed that PdNi 50:50 is more stable in alkaline solution than pure Pd and other PdNi compositions.
Manual Therapy in Cervical and Lumbar Radiculopathy: A Systematic Review of the Literature

The aim of this study was to describe and update current knowledge of manual therapy accuracy in treating cervical and lumbar radiculopathy, to identify the limitations in current studies, and to suggest areas for future research. The study was conducted according to PRISMA guidelines for systematic reviews. A comprehensive literature review was conducted using PubMed and Web of Science databases up to April 2020. The following inclusion criteria were used: (1) presence of radiculopathy; (2) treatment defined as manual therapy (i.e., traction, manipulation, mobilization); and (3) publication defined as a Randomized Controlled Trial. The electronic literature search resulted in 473 potentially relevant articles. Finally, 27 articles were accepted: 21 on cervical (CR) and 6 in lumbar radiculopathy (LR). The mean PEDro score for CR was 6.6 (SD 1.3), and for LR 6.7 (SD 1.6). Traction-oriented techniques are the most frequently chosen treatment form for CR and are efficient in reducing pain and improving functional outcomes. In LR, each of the included publications used a different form of manual therapy, which makes it challenging to summarize knowledge in this group. Of included publications, 93% were either of moderate or low quality, which indicates that quality improvement is necessary for this type of research.
Small Proline-Rich Protein 2A and 2D Are Regulated by the RBM38-p73 Axis and Associated with p73-Dependent Suppression of Chronic Inflammation

Simple Summary Small proline-rich protein 2A and 2D (SPRR2A and SPRR2D) are structure proteins of cornified cell envelopes and function as a protective barrier against diverse external insults. However, the role of SPRR2A/2D in chronic inflammation remains unclear. Here, we showed that SPRR2A/2D expression is controlled by a regulatory loop formed by RNA-binding protein RBM38 and tumor suppressor p73. We also found that RBM38-mediated expression of SPRR2A/2D was p73-dependent and that induction of SPRR2A/2D during keratinocyte differentiation was dependent on both p73 and Rbm38. Furthermore, We found that Rbm38−/−;Trp73+/− mice exhibited weak expression of SPRR2A/2D in multiple tissues and were susceptible to systemic chronic inflammation. Together, our data reveal that SPRR2A/2D are novel targets of the RBM38-p73 loop and contribute to p73-dependent suppression of chronic inflammation. Abstract Small proline-rich protein 2A and 2D (SPRR2A and SPRR2D) provide barrier function in terminally differentiated stratified squamous epithelia through the epidermal differentiation complex. However, little is known how SPRR2A/2D expression is controlled and their role in chronic inflammation. Here, we showed that that SPRR2A/2D expression is controlled by a regulatory loop formed by RNA-binding protein RBM38 and tumor suppressor p73. Specifically, we found that SPRR2A/2D expression was induced by ectopic expression of RBM38 or p73 but suppressed by knockout of Rbm38 or p73. We also found that RBM38-mediated expression of SPRR2A/2D was p73-dependent and that induction of SPRR2A/2D during keratinocyte differentiation was dependent on both p73 and Rbm38. Additionally, we found that SPRR2A/2D expression was closely associated with p73 expression in normal and cancerous tissues. To determine the biological function of the RBM38-p73 loop potentially via SPRR2A/2D, we generated a cohort of wild-type, Rbm38−/−, Trp73+/−, and Rbm38−/−;Trp73+/− mice. We found that Rbm38−/−;Trp73+/− mice had a much shorter lifespan than that for Rbm38−/−—and to a lesser extent for Trp73+/− mice—but were less prone to spontaneous tumors than Trp73+/− or Rbm38−/− mice. We also found that Rbm38−/−;Trp73+/− mice exhibited weak expression of SPRR2A/2D in multiple tissues and were susceptible to systemic chronic inflammation, suggesting that decreased SPRR2A/2D expression is likely responsible for chronic inflammation in Rbm38−/−;Trp73+/− mice, leading to a shortened lifespan. Together, our data reveal that SPRR2A/2D are novel targets of the RBM38-p73 loop and contribute to p73-dependent suppression of chronic inflammation.
Hyperspectral Imaging (HSI)—A New Tool to Estimate the Perfusion of Upper Abdominal Organs during Pancreatoduodenectomy

Simple Summary Novel intraoperative imaging systems may have a critical impact on intraoperative decision-making. Hyperspectral imaging (HSI) is one of the leading new imaging systems, providing color pictures of tissue characterization, such as oxygen saturation (StO2) and Near-Infrared Perfusion Index (NIR-PI). Several surgical disciplines have already used HSI in detecting tissue perfusion with a proven record of success. To the best of our knowledge, HSI has not been used in the field of pancreatic surgery yet. Abstract Hyperspectral imaging (HSI) in abdominal surgery is a new non-invasive tool for the assessment of the perfusion and oxygenation of various tissues and organs. Its benefit in pancreatic surgery is still unknown. The aim of this study was to evaluate the key impact of using HSI during pancreatoduodenectomy (PD). In total, 20 consecutive patients were included. HSI was recorded during surgery as part of a pilot study approved by the local Ethics Committee. Data were collected prospectively with the TIVITA® Tissue System. Intraoperative HS images were recorded before and after gastroduodenal artery (GDA) clamping. We detected four patients with celiac artery stenosis (CAS) caused by a median arcuate ligament (MAL). In two of these patients, a reduction in liver oxygenation (StO2) was discovered 15 and 30 min after GDA clamping. The MAL was divided in these patients. HSI showed an improvement of liver StO2 after MAL division (from 61% to 73%) in one of these two patients. There was no obvious decrease in liver StO2 in the other two patients with CAS. HSI, as a non-invasive procedure, could be helpful in evaluating liver and gastric perfusion during PD, which might assist surgeons in choosing the best surgical approach and in improving patients’ outcomes.