Miracle-dz/UniFAR-FTUs huggingface dataset:
F AST MNAS: U NCERTAINTY - AWARE N EURAL A RCHI - TECTURE S EARCH WITH L IFELONG L EARNING

Sampling-based neural architecture search (NAS) always guarantees better convergence yet suffers from huge computational resources compared with gradient-based approaches, due to the rollout bottleneck – exhaustive training for each sampled generation on proxy tasks. This work provides a general pipeline to accelerate the convergence of the rollout process as well as the RL learning process in sampling-based NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We ﬁrst introduce an uncertainty-aware critic (value function) in PPO to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, a life-long knowledge pool together with a block similarity function is proposed to utilize lifelong parameter knowledge and reduces the searching time by 2 times. It is the ﬁrst to introduce block-level weight sharing in RL-based NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show a simply designed off-policy correction factor that enables “replay buffer” in RL optimization and further reduces half of the searching time. Experiments on the MNAS search space show the proposed FNAS accelerates standard RL-based NAS process by ∼ 10x (e
C ERTIFIED ROBUSTNESS AGAINST PHYSICALLY - REALIZABLE PATCH ATTACKS VIA RANDOMIZED CROPPING

This paper proposes a certiﬁable defense against adversarial patch attacks on image classiﬁcation. Our approach classiﬁes random crops from the original image independently and classiﬁes the original image as the majority vote over predicted classes of the crops. This process minimizes changes to the training process, as only the crop classiﬁcation model needs to be trained, and can be trained in a standard manner without explicit adversarial training. Leveraging the fact that a patch attack can only inﬂuence a certain number of pixels in the image, we derive certiﬁed robustness bounds for the classiﬁer. Our method is particularly effective when realistic transformations are applied to the adversarial patch, such as afﬁne transformations. Such transformations occur naturally when an adversarial patch is physically introduced in a scene. Our method improves upon the current state of the art in defending against patch attacks on CIFAR10 and ImageNet, both in terms of certiﬁed accuracy and inference time.
Supplementary material to "Deep ice as a geochemical reactor: insights from iron speciation and mineralogy of dust in the Talos Dome ice core (East Antarctica)"

Abstract. Thanks to its insolubility, mineral dust is considered a stable proxy in polar ice cores. With this study we show that below an ice-depth of 1000 m, the Talos Dome ice core (Ross Sea sector of East Antarctica) presents evident and progressive signs of post-depositional processes affecting the mineral dust records. We applied a suite of established and cutting edge techniques to investigate the properties of dust present in the Talos Dome ice core, ranging from concentration and grain-size to elemental-composition and Fe-mineralogy. Results show that through acidic/oxidative weathering, the conditions of deep ice at Talos Dome promote the dissolution of specific minerals and the englacial formation of others, deeply affecting dust primitive features. The expulsion of acidic atmospheric species from ice-grains and their concentration in localized environments is likely the main process responsible for englacial reactions and is related with ice re-crystallization. Deep ice can be seen as a "geochemical reactor" capable of fostering complex reactions which involve both soluble and insoluble impurities. Fe-bearing minerals can efficiently be used to explore such transformations.
Idiopathic Ovarian Vein Thrombosis: A Rare Cause of Abdominal Pain

Ovarian vein thrombosis (OVT) is a rare entity. It is usually seen in hypercoagulable states such as pregnancy, peripartum period, active malignancy, recent pelvic surgeries, pelvic infections, and inherited or acquired thrombophilias. Idiopathic OVT is exceedingly rare. We report a case of OVT in a healthy 42-year-old post-menopausal female presenting with right lower quadrant abdominal pain for four days. The patient denied any recent pelvic surgery, pelvic infection, or any family history of thrombophilia. Right ovarian vein thrombosis was found on a computed tomography scan of the abdomen and pelvis. Laboratory workup including hypercoagulability studies was normal. The patient was treated with a therapeutic dose of lower molecular weight heparin and later transitioned to rivaroxaban for three to six months. This case emphasizes OVT as a differential diagnosis of lower abdominal pain in healthy females. Currently, there are no standard guidelines for the duration of anticoagulation in OVT, however based on literature review, deep venous thrombosis treatment guidelines can be followed.
Asset Prices and No-Dividend Stocks ∗

We incorporate stocks that pay no dividends into an otherwise standard, parsimonious dynamic asset pricing framework. We find that such a simple feature leads to profound asset price implications, which are all supported empirically. In particular, we demonstrate that no-dividend stocks command lower mean returns, but also have higher return volatilities and higher market betas than comparable stocks that pay dividends. We also show that the presence of no-dividend stocks in the stock market leads to a lower correlation between the stock market return and aggregate consumption growth rate, a non-monotonic and even a negative relation between the stock market risk premium and its volatility, and a downward sloping term structure of equity risk premia. We provide straightforward intuition for all these results and the underlying economic mechanisms at play. JEL Classifications: G12.
I NTRIGUING CLASS - WISE PROPERTIES OF ADVERSARIAL TRAINING

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. However, previous works mainly focus on the overall robustness of the model, and the in-depth analysis on the role of each class involved in adversarial training is still missing. In this paper, we provide the first detailed class-wise diagnosis of adversarial training on six widely used datasets, i.e., MNIST, CIFAR-10, CIFAR-100, SVHN, STL-10 and ImageNet. Surprisingly, we find that there are remarkable robustness discrepancies among classes, demonstrating the following intriguing properties: 1) Many examples from a certain class could only be maliciously attacked to some specific semantic-similar classes, and these examples will not exist adversarial counterparts in bounded -ball if we re-train the model without those specific classes; 2) The robustness of each class is positively correlated with its norm of classifier weight in deep neural networks; 3) Stronger attacks are usually more powerful for vulnerable classes. Finally, we propose an attack to better understand the defense mechanism of some state-of-the-art models from the class-wise perspective. We believe these findings can contribute to a more comprehensive understanding of adversarial training as well as further improvement of adversarial robustness.
A Confirmatory Framework PLS-SEM for Construction Waste Reduction as Part of Achieving Sustainable Development Goals of a Building

As a result of rapid population growth, an exponentially growing human population, and industrial expansion, it has become increasingly difficult to manage municipal solid wastes throughout the world. Decentralized waste management systems have created difficult situations in developing countries such as Malaysia. Wastes generated in the country, due to various cultural, social, and religious activities, organic and contributing to environmental pollution (air, water, and soil) and human health troubles. A questionnaire survey was participated by 220 construction professionals in Malaysia using structured and semi-structured methods. The framework was assessed using A partial least square structural equation modeling (PLS-SEM) to target sustainable development goals (SDG). Statistical analysis results indicate a significant effect between SCW management, since(r(270)=. 687, P&lt;0.001). Improving factors has strong relationship with SCW management, since(r(270)=. 723, P&lt;0.001). The mediation results also suggested a significant indirect positive effect of improving factors drivers on SCW management through policy-related factors sinceβ=0.688, t=8.254, P&lt;0.001, 95% CI for β=0.536,0.866. Finally, policy-related factors construct has a strong relationship with SCWM) management, since(r(270)=. 811, P&lt;0.001) With the R Square of 0.787 and 0.785. The developed framework can improve construction waste management in the construction industry and enhance construction waste management to achieve global sustainable development goals. The findings show that one of the most critical issues of enhancing profitability is using preventive policies to reduce construction waste. This study could guide construction industry stakeholders in identifying the different waste management features during a building project's construction and design stage
Exploring the Use of Neural Transformers for Psycholinguistics Exploring the Use of Neural Transformers for Psycholinguistics

Deep learning has the potential to help solve numerous problems in cognitive science and education, by providing us a way to model the cognitive profiles of individual people. If this were possible, it would allow us to design targeted tests and suggest specific remediation based on each individual’s needs. On the flip side, employing techniques from psychology can give us insight into the underlying skillsets neural networks have acquired during training, addressing the interpretability concern. This thesis explores these ideas in the context of transformer language models, which have achieved state-of-the-art results on virtually every natural language processing (NLP) task. First, we study the ability of transformers to model Age of Acquisition, an important variable in predicting word processing in humans. We then examine the broader challenge of using transformers to model the human responses to a test of linguistic competencies, this time employing measures from psychometrics as part of our evaluation. Compared to simpler models, we find that transformers can achieve superior results in both cases, suggesting they are more suitable for modeling psychological properties. The use of psychometric measures also allows us to study what linguistic skills transformers cannot learn, suggesting where future architectures can make improvements.
Learning with Plasticity Rules: Generalization and Robustness

Brains learn robustly, and generalize effortlessly between different learning tasks; 1 in contrast, robustness and generalization across tasks are well known weaknesses 2 of artiﬁcial neural nets (ANNs). How can we use our accelerating understanding of 3 the brain to improve these and other aspects of ANNs? Here we hypothesize that (a) 4 Brains employ synaptic plasticity rules that serve as proxies for Gradient Descent 5 (GD); (b) These rules themselves can be learned by GD on the rule parameters; and 6 (c) This process may be a missing ingredient for the development of ANNs that 7 generalize well and are robust to adversarial perturbations. We provide both empiri-8 cal and theoretical evidence for this hypothesis. In our experiments, plasticity rules 9 for the synaptic weights of recurrent neural nets (RNNs) are learned through GD 10 and are found to perform reasonably well (with no backpropagation). We ﬁnd that 11 plasticity rules learned by this process generalize from one type of data/classiﬁer to 12 others (e.g., rules learned on synthetic data work well on MNIST/Fashion MNIST) 13 and converge with fewer updates. Moreover, the classiﬁers learned using plasticity 14 rules exhibit surprising levels of tolerance to adversarial perturbations. Focusing 15 on the last layer of a classiﬁcation network, we show analytically that GD on the 16 plasticity rule recovers (and can improve upon) the perceptron algorithm and the 17 multiplicative weights method; and the learned weights are provably robust to a 18 quantiﬁable extent. Finally, we argue that applying GD to learning plasticity rules 19 is biologically plausible, in the sense that they can be learned over evolutionary 20 time: we show that, within the standard population genetic framework used to 21 study evolution, natural selection of a numerical parameter over a sequence of 22 generations provably simulates a simple variant of GD. 23
Cloud Computing Adoption: Opportunities and Challenges for Small, Medium and Micro Enterprises in South Africa

The purpose of the paper is to determine the opportunities and challenges that lead to cloud computing adoption by SMMEs in South Africa by looking at the factors that influence adoption. The TOE framework is used to contextualize the factors that influence cloud computing adoption and evaluate the opportunities and challenges that are presented by cloud computing to SMMEs in South Africa. An online survey questionnaire was used to collect data from leaders of SMMEs from all geographical regions and business industries in South Africa. A quantitative research approach was adopted to investigate the objectives, and descriptive analysis was used to evaluate the relationships and present the results. The findings of the study show that relative advantage is an important factor in the consideration of cloud computing adoption by SMMEs, while government and regulatory support is perceived as a barrier. Top management support, which has been previously found by other studies to be a significant factor has been found to be insignificant in this study. The study has revealed that cloud computing presents opportunities to SMMEs and improves their competitiveness.
Distributive Injustice: Leadership Adherence to Social Norm Pressures and the Negative Impact on Organizational Commitment

The social norm theory suggests that leaders who rely on perceived norms (misperceptions) rather than actual norms may produce unfair work advantages. Furthermore, social norms alter ethical leadership behaviors. However, leadership adheres to social norms due to society's implied compliance in the absence of distributive injustice measurements. Therefore, distributive injustice may be a more salient predictor than distributive justice on affective organizational commitment. The aim of this study was to fill gaps in literature on distributive injustice and investigate negative influences on employees’ affective commitment. A distributive injustice scale was designed using employee perceptions of policies that create unfair advantages and meritless rewards. The distributive injustice scale consisted of 14 items. A survey was sent to 481 full-time employees in various industries throughout the U.S. Correlation and regression model output indicated that unfair advantages and meritless rewards had a negative relationship and influence on employees’ affective commitment. Social norm policies that create unfair advantages and meritless rewards can be perceived as a divisionary tacit that negatively impacts affective commitment.
PSR_2100008 1..16

doi:10.1017/S0003055421000083 © The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Endogeneous Mobilization and Dictators Tactics

Dictators govern in an environment characterized by domestic threats from opposition groups, and they must strategically deal with them to affirm their legitimacy. This paper analyzes the dictator’s choice between peacefully buying the opposition off, violently excluding opposition groups, or adopting a hands-off policy providing political freedom. I develop a dynamic game-theoretic model, endogenizing citizens mobilization as a response to the opposition’s corganizational capacity and their economic conditions. A dynamic of dictator tactics emerges. Dictators either use repression or co-option today, to significantly weaken the opposition if opposition parties can tap into mass support to improve their coercive capacity tomorrow. Dictators can then engage in hands-off policies later on when the opposition is weak enough. In contrast with conventional wisdom, I show that the ruler is willing to engage in co-optation when facing weak opposition groups. Because strong opposition groups and large economic inequalities are likely to ignite mass mobilization, the ruler is more inclined to use repression. This analysis suggests that NGOs must be careful when designing training programs to strengthen opposition parties in dictatorships. Keywords— Opposition Parties, Coordination, Dynamic Game, Dictatorships. Dictators frequently confront political contestations from domestic oppositions. They rely on several distinct strategies to navigate through constestations and maintain political stability: strategies of repression, co-optation or hands-off are generally used. For example, ∗PhD Candidate, Department of Politics, gnandong@princeton.edu. I am indebted to John B. Londregan, Leonard Wantchekon, Matias Iaryczower and Germán Gieczewski for many helpful discussions. I am also thankful to John Duggan, Carlo Horz, Gleason Judd, Adam Meirowitz, Kris Ramsay, Mehdi Shadmehr, Ken Shotts, Bruno Strulovici, seminar participants of the 2021 Midwest Political Science Association Conference and Graduate Research Seminar at Princeton University.
Cost of U.S. New Protectionism for Mexico

We investigate the short-run impact of new wave of U.S. protectionism on Mexico’s economy. First, we use event studies approach to see the impact of changes in trade policies on Mexico’s imports and exports for the period of 2016-2019. Second, we apply Zoutman et al. (2018) approach to estimate the trade elasticities for Mexico using the six-digit level of the Harmonized System product codes. Third, we modify a small open economy DSGE model developed by Christiano et al. (2011) to study the effects of U.S. protectionism on Mexico’s economy and the impacts of potential changes of trade policies on Mexico’s economy through different bilateral trade shocks. The estimated elasticities of Mexico’s imports demand and exports supply are -1.08 and 0.44 respectively. Our result shows that in the short run, the imposition of 10 percent tariffs on Mexico’s exports potentially may reduce the Mexico’s GDP to more than 4 percent, and 10 percent retaliatory tariffs on Mexico’s imported consumption goods may reduce consumption by less than 2 percent and increase CPI inflation by less than 5 percent.
Hepatic miR-122 expression correlated with IL-28B genetic polymorphisms in hepatocellular carcinoma patients with living donor liver transplantation

: Hepatocellular carcinoma (HCC) recurrence after living donor liver transplantation (LDLT) remains problematic. The genetic and molecular characteristics of patients may affect HCC recurrence. We evaluated the effects of microRNA-122 (miR-122) and interleukin-28B (IL-28B) genetic polymorphisms on patients with HCC following LDLT in 60 patients. MiR-122 and IL-28B polymorphisms were evaluated in plasma and liver tissues after LDLT. HBV, HCV, dual HBV / HCV infection, and non-B non-C were detected in 26, 22, 3, and 9 patients, respectively, over a median follow-up time of 20.5 (10–33) months. miR-122 was signiﬁcantly higher in the liver than in the plasma of patients with HBV, HCV, dual HBV / HCV, and non-B non-C. Hepatic miR-122 expression was signiﬁcantly higher for genotype TT and genotype TT plus GT ( p = 0.005) compared with genotype GG of IL-28B rs8099917 and signiﬁcantly higher in > 6% fatty liver than in < 5% or no fatty liver. In conclusion, high hepatic miR-122 was correlated with the IL-28B rs8099917 genotypes TT and GT and with > 6% fatty liver and, thus, may play a major role in HCC.
Clinical, Laboratory Data and Outcomes of 17 Iranian Citrullinemia Type1 Patients: Identification of Five Novel ASS1 Gene Mutations

Background: Citrullinemia type 1 (CTLN1) is an autosomal recessive metabolic disease caused by ASS1 gene mutations encoding argininosuccinic acid synthase enzyme which is within the pathway of arginine and nitric oxide biosynthesis. Methods: Disease confirmation was done by ASS1 gene mutation analysis using Next Generation Sequencing, DNA Sanger sequencing and bioinformatics. The study group members were17 citrullinemia type1 patients from 10 unrelated families referred to Iranian National Society for the Study of Inborn Errors of Metabolism’s clinic between 2008-2020. Clinical, laboratory and molecular data were retrospectively evaluated. Results: Eleven different ASS1 gene mutations were detected. Presentation: 3/17 (76%) neonatal, 3/17 (18%) late infantile, 1/17(6%) asymptomatic. Severe developmental delay and intractable seizures despite metabolic control was the only surviver of neonatal form outcome. Two patients with late infantile form live metabolically and seizure controlled with quite normal performance. DNA mutations: 7 missense, 1 nonsense, and 2 indel mutations in twelve, two, three patients, respectively. Five novel mutations were detected including a homozygous GG deletion in exon 11 (c.790_791delGG; G264Pfs*3) and a homozygous mutation in exon 6 (c.440C>T; p.M147T), both leading to infantile form; a homozygous mutation in exon 14 (c.1130T>C; p.M377T) leading to neonatal form; two compound heterozygote mutations in exon 14 (c.1167_1168insC& c.1186T>A; p.S396T) leading to asymptomatic form. Five patients (38%) with classic neonatal form had mutation in exon14 of ASS1 (c.1168G>A; p.G390R). Conclusions: Classic neonatal form was the most common form of disease in Iranian studied patients and exon 14: c.1168G>A; (p.G390R) was the most frequent ASS1 gene mutation. Global neonatal screening in Iran is recommended for citrullinemia type1 and certain mutations can be used for screening lethal form of disease in this population.