Miracle-dz/UniFAR-FTUs huggingface dataset:
A Design-Based Perspective on Synthetic Control Methods

Since their introduction in Abadie and Gardeazabal (2003), Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data. Formal discussions often motivate SC methods by the assumption that the potential outcomes were generated by a factor model. Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s). We show that the standard SC estimator is generally biased under random assignment. We propose a Modified Unbiased Synthetic Control (MUSC) estimator that guarantees unbiasedness under random assignment and derive its exact, randomization-based, finite-sample variance. We also propose an unbiased estimator for this variance. We document in settings with real data that under random assignment, SC-type estimators can have root mean-squared errors that are substantially lower than that of other common estimators. We show that such an improvement is weakly guaranteed if the treated period is similar to the other periods, for example, if the treated period was randomly selected. While our results only directly apply in settings where treatment is assigned randomly, we believe that they can complement model-based approaches even for observational studies.
Direct identification of the six polytypes of chlorite characterized by semi-random stacking

Abstract This paper demonstrates that the six standard polytypes of chlorite, whose definitions are based on the orientation of the interlayer sheet and the position of the interlayer sheet on the 2:1 layer, can be discriminated by atomic-resolution images recorded down [010], using a transmission electron microscope with a ~2 Å point resolution and digital image processing. Several specimens were investigated to reveal their local stacking structures. A Ibb chlorite is highly twinned and twin boundaries consist of a IIb+Ia stacking sequence at the interlayer sheet. An interstratified chlorite/biotite formed by hydrothermal alteration from biotite in granite consists of a mixture of several chlorite polytypic sequences, including Iab, Ibb, IIab, and IIbb. These polytypic details of chlorite and other sheet silicates provide important insights into mineral stability, origin, and reaction mechanisms.
High-Performance HZO/InAlN/GaN MIS-HEMT with fT/fmax of 155/250 GHz

Scaling of GaN high-electron-mobility transistors (HEMTs) usually increases gate leakage current and deteriorates breakdown characteristic, limiting the maximum drain current and output power density. These bottlenecks can be circumvented by inserting a dielectric material under the gate of HEMTs. Doped HfO2 is an excellent dielectric material but unexplored so far as the gate material of HEMTs for high-speed device application. Here we demonstrate that Zr-doped HfO2 (HZO)-gated InAlN/GaN metal-insulator-semiconductor (MIS) HEMTs exhibit remarkable properties. The device with a gate length (Lg) of 50 nm exhibits maximum drain current (Id,max) of 2.15 A/mm, a transconductance (gm) peak of 476 mS/mm, an on/off current ratio (Ion/Ioff) of 9.3 × 10, a low drain-induced barrier lowing (DIBL) of 45 mV/V. RF characterizations reveal a current gain cutoff frequency (fT) of 155 GHz and a maximum oscillation frequency (fmax) of 250 GHz, resulting in a (fT×fmax) of 197 GHz. The breakdown voltages (BV) of 35 V and 72 V is achieved on the Lg = 50 nm devices with source-drain distance (Lsd) of 0.6 and 2 μm (fT of 155 and 110 GHz), resulting in high Johnson’s figure-of-merit (JFOM = fT ×BV) of 5.4 and 7.9 THz∙V, respectively. These properties, particularly the high fT/fmax and JFOM are highly desirable for the millimeter-wave power applications, demonstrating the great technological potential of HZO/InAlN/GaN MIS-HEMTs.
Unitopatho, A Labeled Histopathological Dataset for Colorectal Polyps Classification and Adenoma Dysplasia Grading

Histopathological characterization of colorectal polyps allows to tailor patients’ management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images. We introduce UniToPatho, an annotated dataset of 9536 hematoxylin and eosin (H&E) stained patches extracted from 292 whole-slide images, meant for training deep neural networks for colorectal polyps classification and adenomas grading. We present our dataset and provide insights on how to tackle the problem of automatic colorectal polyps characterization by suggesting a multi-resolution deep learning approach.
Filling the gap between GRACE- and GRACE-FO-derived terrestrial water storage anomalies with Bayesian convolutional neural networks

There is an approximately one-year observation gap of terrestrial water storage anomalies (TWSAs) between the Gravity Recovery and Climate Experiment (GRACE) satellite and its successor GRACE Follow-On (GRACE-FO). This poses a challenge for water resources management, as discontinuity in the TWSA observations may introduce signiﬁcant biases and uncertainties in hydrological model predictions and consequently mislead decision making. To tackle this challenge, a Bayesian convolutional neural network (BCNN) is proposed in this study to bridge this gap using climatic data as inputs. Enhanced by integrating recent advances in deep learning, BCNN can eﬃciently extract important features for TWSA predictions from multi-source input data. The predicted TWSAs are compared to the hydrological model outputs and three recent TWSA prediction products. Results suggest the superior performance of BCNN in bridging the gap. The extreme dry and wet events during the gap period are also successfully identiﬁed by BCNN.
A solution to the permalloy problem

We propose a solution to the longstanding permalloy problem$-$why the particular composition of permalloy, Fe$_{21.5}$Ni$_{78.5}$, achieves a dramatic drop in hysteresis, while its material constants show no obvious signal of this behavior. We use our recently developed coercivity tool to show that a delicate balance between local instabilities and magnetic material constants are necessary to explain the dramatic drop of hysteresis at 78.5% Ni. Our findings are in agreement with the permalloy experiments and, more broadly, provide theoretical guidance for the discovery of novel low hysteresis magnetic alloys.
On the noise generation and unsteady performance of combined heaving and pitching foils

A transient two-dimensional acoustic boundary element solver is coupled to a potential flow boundary element solver via Powell’s acoustic analogy to determine the acoustic emission of isolated hydrofoils performing biologically-inspired motions. The flow-acoustic boundary element framework is validated against experimental and asymptotic solutions for the noise produced by canonical vortex-body interactions. The numerical framework then characterizes the noise production of an oscillating foil, which is a simple representation of a fish caudal fin. A rigid NACA 0012 hydrofoil is subjected to combined heaving and pitching motions for Strouhal numbers ( 0.03<St<1 ) based on peak-to-peak amplitudes and chord-based reduced frequencies ( 0.125<f∗<1 ) that span the parameter space of many swimming fish species. A dipolar acoustic directivity is found for all motions, frequencies, and amplitudes considered, and the peak noise level increases with both the reduced frequency and the Strouhal number. A combined heaving and pitching motion produces less noise than either a purely pitching or purely heaving foil at a fixed reduced frequency and amplitude of motion. Correlations of the lift and power coefficients with the peak root-mean-square acoustic pressure levels are determined, which could be utilized to develop long-range, quiet swimmers.
Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control

This paper investigates the collision-free control problem for multi-agent systems. For such multi-agent systems, it is the typical situation where conventional methods using either the usual centralized model predictive control (MPC), or even the distributed counterpart, would suffer from substantial difficulty in balancing optimality and computational efficiency. Additionally, the non-convex characteristics that invariably arise in such collision-free control and optimization problems render it difficult to effectively derive a reliable solution (and also to thoroughly analyze the associated convergence properties). To overcome these challenging issues, this work establishes a suitably novel parallel computation framework through an innovative mathematical problem formulation; and then with this framework and formulation, a parallel algorithm based on alternating direction method of multipliers (ADMM) is presented to solve the sub-problems arising from the resulting parallel structure. Furthermore, an efficient and intuitive initialization procedure is developed to accelerate the optimization process, and the optimum is thus determined with significantly improved computational efficiency. As supported by rigorous proofs, the convergence of the proposed ADMM iterations for this non-convex optimization problem is analyzed and discussed in detail. Finally, a simulation with a group of unmanned aerial vehicles (UAVs) serves as an illustrative example here to demonstrate the effectiveness and efficiency of the proposed approach. Also, the simulation results verify significant improvements in accuracy and computational efficiency compared to other baselines, including primal quadratic mixed integer programming (PQ-MIP), non-convex quadratic mixed integer programming (NC-MIP), and non-convex quadratically constrained quadratic programming (NC-QCQP).
Local linear tie-breaker designs

Tie-breaker experimental designs are hybrids of Randomized Control Trials (RCTs) and Regression Discontinuity Designs (RDDs) in which subjects with moderate scores are placed in an RCT while subjects with extreme scores are deterministically assigned to the treatment or control group. The design maintains the benefits of randomization for causal estimation while avoiding the possibility of excluding the most deserving recipients from the treatment group. The causal effect estimator for a tie-breaker design can be estimated by fitting local linear regressions for both the treatment and control group, as is typically done for RDDs. We study the statistical efficiency of such local linear regression-based causal estimators as a function of ∆, the radius of the interval in which treatment randomization occurs. In particular, we determine the efficiency of the estimator as a function of ∆ for a fixed, arbitrary bandwidth under the assumption of a uniform assignment variable. To generalize beyond uniform assignment variables and asymptotic regimes, we also demonstrate on the Angrist and Lavy (1999) classroom size dataset that prior to conducting an experiment, an experimental designer can estimate the efficiency for various experimental radii choices by using Monte Carlo as long as they have access to the distribution of the assignment variable. For both uniform and triangular kernels, we show that increasing the radius of randomized experiment interval will increase the efficiency until the radius is the size of the local-linear regression bandwidth, after which no additional efficiency benefits are conferred.
k-Neighbor Based Curriculum Sampling for Sequence Prediction

Multi-step ahead prediction in language models is challenging due to the discrepancy between training and test time processes. At test time, a sequence predictor is required to make predictions given past predictions as the input, instead of the past targets that are provided during training. This difference, known as exposure bias, can lead to the compounding of errors along a generated sequence at test time. To improve generalization in neural language models and address compounding errors, we propose \textit{Nearest-Neighbor Replacement Sampling} -- a curriculum learning-based method that gradually changes an initially deterministic teacher policy to a stochastic policy. A token at a given time-step is replaced with a sampled nearest neighbor of the past target with a truncated probability proportional to the cosine similarity between the original word and its top $k$ most similar words. This allows the learner to explore alternatives when the current policy provided by the teacher is sub-optimal or difficult to learn from. The proposed method is straightforward, online and requires little additional memory requirements. We report our findings on two language modelling benchmarks and find that the proposed method further improves performance when used in conjunction with scheduled sampling.
Measuring Dependence with Matrix-based Entropy Functional

Measuring the dependence of data plays a central role in statistics and machine learning. In this work, we summarize and generalize the main idea of existing information-theoretic dependence measures into a higher-level perspective by the Shearer's inequality. Based on our generalization, we then propose two measures, namely the matrix-based normalized total correlation and the matrix-based normalized dual total correlation, to quantify the dependence of multiple variables in arbitrary dimensional space, without explicit estimation of the underlying data distributions. We show that our measures are differentiable and statistically more powerful than prevalent ones. We also show the impact of our measures in four different machine learning problems, namely the gene regulatory network inference, the robust machine learning under covariate shift and non-Gaussian noises, the subspace outlier detection, and the understanding of the learning dynamics of convolutional neural networks, to demonstrate their utilities, advantages, as well as implications to those problems.
Sustaining Star Formation in the Galactic Star Cluster M 36?

We present comprehensive characterization of the Galactic open cluster M 36. Some 200 member candidates, with an estimated contamination rate of ∼8%, have been identified on the basis of proper motion and parallax measured by the Gaia DR2. The cluster has a proper motion grouping around ( mas yr−1, and μ δ = − 3.35 ± 0.02 mas yr−1), distinctly separated from the field population. Most member candidates have parallax values 0.7–0.9 mas, with a median value of 0.82 ± 0.07 mas (distance ∼1.20 ± 0.13 kpc). The angular diameter of determined from the radial density profile then corresponds to a linear extent of 9.42 ± 0.14 pc. With an estimated age of ∼15 Myr, M 36 is free of nebulosity. To the southwest of the cluster, we discover a highly obscured (A V up to ∼23 mag), compact (∼ ) dense cloud, within which three young stellar objects in their infancy (ages ≲0.2 Myr) are identified. The molecular gas, 3.6 pc in extent, contains a total mass of (2–3) × 102 M ⊙, and has a uniform velocity continuity across the cloud, with a velocity range of −20 to −22 km s−1, consistent with the radial velocities of known star members. In addition, the cloud has a derived kinematic distance marginally in agreement with that of the star cluster. If physical association between M 36 and the young stellar population can be unambiguously established, this manifests a convincing example of prolonged star formation activity spanning up to tens of Myr in molecular clouds.
Channel Estimation for RIS Assisted Wireless Communications—Part II: An Improved Solution Based on Double-Structured Sparsity

Reconfigurable intelligent surface (RIS) can manipulate the wireless communication environment by controlling the coefficients of RIS elements. However, due to the large number of passive RIS elements without signal processing capability, channel estimation in RIS assisted wireless communication system requires high pilot overhead. In the second part of this invited paper, we propose to exploit the double-structured sparsity of the angular cascaded channels among users to reduce the pilot overhead. Specifically, we first reveal the double-structured sparsity, i.e., different angular cascaded channels for different users enjoy the completely common non-zero rows and the partially common non-zero columns. By exploiting this double-structured sparsity, we further propose the double-structured orthogonal matching pursuit (DS-OMP) algorithm, where the completely common non-zero rows and the partially common non-zero columns are jointly estimated for all users. Simulation results show that the pilot overhead required by the proposed scheme is lower than existing schemes.
Contrastive Prototype Learning with Augmented Embeddings for Few-Shot Learning

Most recent few-shot learning (FSL) methods are based on meta-learning with episodic training. In each meta-training episode, a discriminative feature embedding and/or classifier are first constructed from a support set in an inner loop, and then evaluated in an outer loop using a query set for model updating. This query set sample centered learning objective is however intrinsically limited in addressing the lack of training data problem in the support set. In this paper, a novel contrastive prototype learning with augmented embeddings (CPLAE) model is proposed to overcome this limitation. First, data augmentations are introduced to both the support and query sets with each sample now being represented as an augmented embedding (AE) composed of concatenated embeddings of both the original and augmented versions. Second, a novel support set class prototype centered contrastive loss is proposed for contrastive prototype learning (CPL). With a class prototype as an anchor, CPL aims to pull the query samples of the same class closer and those of different classes further away. This support set sample centered loss is highly complementary to the existing query centered loss, fully exploiting the limited training data in each episode. Extensive experiments on several benchmarks demonstrate that our proposed CPLAE achieves new state-of-the-art.
Cognitive Perspectives on Context-based Decisions and Explanations

When human cognition is modeled in Philosophy and Cognitive Science, there is a pervasive idea that humans employ mental representations in order to navigate the world and make predictions about outcomes of future actions. By understanding how these representational structures work, we not only understand more about human cognition but also gain a better understanding for how humans rationalise and explain decisions. This has an influencing effect on explainable AI, where the goal is to provide explanations of computer decision-making for a human audience. We show that the Contextual Importance and Utility method for XAI share an overlap with the current new wave of action-oriented predictive representational structures, in ways that makes CIU a reliable tool for creating explanations that humans can relate to and trust.
κ
-Poincaré comodules, braided tensor products, and noncommutative quantum field theory

We discuss the obstruction to the construction of a multiparticle field theory on a κ-Minkowski noncommutative spacetime: the existence of multilocal functions which respect the deformed symmetries of the problem. This construction is only possible for a light-like version of the commutation relations, if one requires invariance of the tensor product algebra under the coaction of the κ-Poincaré group. This necessitates a braided tensor product. We study the representations of this product, and prove that κ-Poincaré-invariant N-point functions belong to an Abelian subalgebra, and are therefore commutative. We use this construction to define the 2-point Whightman and Pauli–Jordan functions, which turn out to be identical to the undeformed ones. We finally outline how to construct a free scalar κ-Poincaré-invariant quantum field theory, and identify some open problems. ∗fedele.lizzi@na.infn. it †flavio.mercati@gmail.com 1 ar X iv :2 10 1. 09 68 3v 1 [ he pth ] 2 4 Ja n 20 21
Surface modification of cotton fabrics using plasma technology

The present paper reports the effect of plasma treatments on the surface properties of textile fibers. In the present investigations mainly air plasma and dichlorodifluoromethane (DCFM) plasmas were used. Measurement of the contact angle, wicking properties, and dyeability of cotton fabrics subjected to various conditions of plasma treatments have been carried out. It was observed that while air plasma produced enhanced water wicking, DCFM plasma resulted in producing a highly water-repellent, hydrophobic surface. The contact angle increased dramatically for the DCFM plasma-treated fabrics up to 140°. It was also observed that plasma treatment has improved the dyeing behavior of cotton fabrics when reactive and natural dyes were used. On the other hand, the dyeability was found to have slightly decreased when direct dye was used.
Fringe News Networks: Dynamics of US News Viewership following the 2020 Presidential Election

The growing political polarization of the American electorate over the last several decades has been widely studied and documented. During the administration of President Donald Trump, charges of “fake news” made social and news media not only the means but, to an unprecedented extent, the topic of political communication. This extreme political polarization continued through the election and all through the period up to the attempted takeover of the Capitol on January 6, 2021. In this paper, we analyze this tumultuous phase in American history through the lens of news viewership. We consider the official YouTube channels of six US cable news networks across a wide political spectrum with a specific focus on three conservative fringe news networks. We analyze how the viewers reacted to the different ways the election outcome was covered by these news outlets. This paper makes two distinct types of contributions. The first is to introduce a novel methodology to analyze large social media data to study the dynamics of US news networks and their viewers. The second is to provide insights into what actually happened regarding these news networks and their viewerships during this volatile 64 day period. Our empirical evidence suggest that recent natural language processing advancements can be harnessed in a synergistic way to mine political insights from large scale social media data.
The Exact Rate Memory Tradeoff for Large Caches with Coded Placement

The idea of coded caching for content distribution networks was introduced by Maddah-Ali and Niesen, who considered the canonical $(N, K)$ cache network in which a server with $N$ files satisfy the demands of $K$ users (equipped with independent caches of size $M$ each). Among other results, their work provided a characterization of the exact rate memory tradeoff for the problem when $M\geq\frac{N}{K}(K-1)$. In this paper, we improve this result for large caches with $M\geq \frac{N}{K}(K-2)$. For the case $\big\lceil\frac{K+1}{2}\big\rceil\leq N \leq K$, we propose a new coded caching scheme, and derive a matching lower bound to show that the proposed scheme is optimal. This extends the characterization of the exact rate memory tradeoff to the case $M\geq \frac{N}{K}\Big(K-2+\frac{(K-2+1/N)}{(K-1)}\Big)$. For the case $1\leq N\leq \big\lceil\frac{K+1}{2}\big\rceil$, we derive a new lower bound, which demonstrates that the scheme proposed by Yu et al. is optimal and thus extend the characterization of the exact rate memory tradeoff to the case $M\geq \frac{N}{K}(K-2)$.