Miracle-dz/UniFAR-FTUs huggingface dataset:
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators

The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on a very limited set of semi-synthetic benchmark datasets. In this paper, we show that even in arguably the simplest setting -- estimation under ignorability assumptions -- the results of such empirical evaluations can be misleading if (i) the assumptions underlying the data-generating mechanisms in benchmark datasets and (ii) their interplay with baseline algorithms are inadequately discussed. We consider two popular machine learning benchmark datasets for evaluation of heterogeneous treatment effect estimators -- the IHDP and ACIC2016 datasets -- in detail. We identify problems with their current use and highlight that the inherent characteristics of the benchmark datasets favor some algorithms over others -- a fact that is rarely acknowledged but of immense relevance for interpretation of empirical results. We close by discussing implications and possible next steps.
On Optimal Quantization in Sequential Detection

The problem of designing optimal quantization rules for sequential detectors is investigated. First, it is shown that this task can be solved within the general framework of active sequential detection. Using this approach, the optimal sequential detector and the corresponding quantizer are characterized and their properties are briefly discussed. In particular, it is shown that designing optimal quantization rules requires solving a nonconvex optimization problem, which can lead to issues in terms of computational complexity and numerical stability. Motivated by these difficulties, two performance bounds are proposed that are easier to evaluate than the true performance measures and are potentially tighter than the bounds currently available in the literature. The usefulness of the bounds and the properties of the optimal quantization rules are illustrated with two numerical examples.
Inferring Multiple Relationships between ASes using Graph Convolutional Network

Precisely understanding the business relationships between Autonomous Systems (ASes) is essential for studying the Internet structure. So far, many inference algorithms have been proposed to classify the AS relationships, which mainly focus on Peer-Peer (P2P) and Provider-Customer (P2C) binary classification and achieved excellent results. However, there are other types of AS relationships in actual scenarios, i.e., the businessbased sibling and structure-based exchange relationships, that were neglected in the previous research. These relationships are usually difficult to be inferred by existing algorithms because there is no discrimination on the designed features compared to the P2P or P2C relationships. In this paper, we focus on the multi-classification of AS relationships for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiple relationships are difficult to be inferred. We then introduce new features and propose a Graph Convolutional Network (GCN) framework, AS-GCN, to solve this multi-classification problem under complex scene. The framework takes into account the global network structure and local link features concurrently. The experiments on real Internet topological data validate the effectiveness of our method, i.e., AS-GCN achieves comparable results on the easy binary classification task, and outperforms a series of baselines on the more difficult multi-classification task, with the overall accuracy above 95%.
Goal-Oriented Script Construction

The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.
Exceeding the Limits of Visual-Linguistic Multi-Task Learning

By leveraging large amounts of product data collected across hundreds of live e-commerce websites, we construct 1000 unique classification tasks that share similarly-structured input data, comprised of both text and images. These classification tasks focus on learning the product hierarchy of different e-commerce websites, causing many of them to be correlated. Adopting a multi-modal transformer model, we solve these tasks in unison using multi-task learning (MTL). Extensive experiments are presented over an initial 100-task dataset to reveal best practices for"large-scale MTL"(i.e., MTL with more than 100 tasks). From these experiments, a final, unified methodology is derived, which is composed of both best practices and new proposals such as DyPa, a simple heuristic for automatically allocating task-specific parameters to tasks that could benefit from extra capacity. Using our large-scale MTL methodology, we successfully train a single model across all 1000 tasks in our dataset while using minimal task specific parameters, thereby showing that it is possible to extend several orders of magnitude beyond current efforts in MTL.
Neural Network Approximation of Refinable Functions

In the desire to quantify the success of neural networks in deep learning and other applications, there is a great interest in understanding which functions are efficiently approximated by the outputs of neural networks. By now, there exists a variety of results which show that a wide range of functions can be approximated with sometimes surprising accuracy by these outputs. For example, it is known that the set of functions that can be approximated with exponential accuracy (in terms of the number of parameters used) includes, on one hand, very smooth functions such as polynomials and analytic functions and, on the other hand, very rough functions such as the Weierstrass function, which is nowhere differentiable. In this paper, we add to the latter class of rough functions by showing that it also includes refinable functions. Namely, we show that refinable functions are approximated by the outputs of deep ReLU neural networks with a fixed width and increasing depth with accuracy exponential in terms of their number of parameters. Our results apply to functions used in the standard construction of wavelets as well as to functions constructed via subdivision algorithms in Computer Aided Geometric Design.
Retinal Microvasculature as Biomarker for Diabetes and Cardiovascular Diseases

Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to color fundus images for semantic segmentation of the blood vessels and severity classification on both vascular and full images. Vessel reconstruction through harmonic descriptors is also used as a smoothing and de-noising tool. The mathematical background of the theory is also outlined. Results: For diabetic patients, at least 93.8% of DR No-Refer vs. Refer classification can be related to vasculature defects. As for the Non-Sight Threatening vs. Sight Threatening case, the ratio is as high as 96.7%. Conclusion: In the case of DR, most of the disease biomarkers are related topologically to the vasculature. Translational Relevance: Experiments conducted on eye blood vasculature reconstruction as a biomarker shows a strong correlation between vasculature shape and later stages of DR.
Heat transfer in dropwise condensation of steam. Effect of falling drops.

水蒸気の滴状凝縮における落下滴の清掃効果を調べるため, 別途水蒸気により調製した凝縮液を, 滴状凝縮している垂直伝熱面の上端に, ノズルより液滴として加え, 伝熱量の測定と凝縮面の写真撮影を行った. この結果, 熱伝達係数は, 加える液滴を増すに応じて, はじめ増大し, つづいて最大に達し, その後徐々に減少していくことがわかった. しかし, 通常の場合, その熱伝達係数は液滴を加えない場合の値まで下がることはなかった. 滴の動くことが熱伝達に対してもたらす効果を調べるため, 表面状態が時間とともに変化する時の熱伝達を二次元モデルで計算した. その結果, 熱伝達は表面状態の変化する速さとともに増加することがわかった. 滴状凝縮では滴の動くこと自体伝熱増進に寄与していることが考えられる.
Electronic Properties and Performance upon Lithium Intercalation of MoO3 Thin Films Grown by PLD

Over a wide range of growth temperature, 25-500 °C, structural and optical characteristics of a series of MoO 3 films prepared by PLD technique are studied. It is found that the growth temperature strongly influences the surface morphology, local atomic structure, and optical properties of PLD MoO 3 thin films. The compositional stability was found to be well maintained up to a temperature on the order of 400 °C, at which point the formation of compositional defects introduces structural disorder and changes in the optical characteristics. The electrochemical features of the insertion of Li ion into the MoO 3 host lattice are also considered for various deposition temperature. Performance of Li//MoO 3 thin film batteries are evaluated.
Exact long-range dielectric screening and interatomic force constants in quasi-2D crystals

We develop a fundamental theory of the long-range electrostatic interactions in two-dimensional crystals by performing a rigorous study of the nonanalyticities of the Coulomb kernel. We find that the dielectric functions are best represented by 2× 2 matrices, with nonuniform macroscopic potentials that are two-component hyperbolic functions of the out-of-plane coordinate, z. We demonstrate our arguments by deriving the long-range interatomic forces in the adiabatic regime, where we identify a formerly overlooked dipolar coupling involving the out-of-plane components of the dynamical charges. The resulting formula is exact up to an arbitrary multipolar order, which we illustrate in practice via the explicit inclusion of dynamical quadrupoles. By performing numerical tests on monolayer BN, SnS2 and BaTiO3 membranes, we show that our method allows for a drastic improvement in the description of the long-range electrostatic interactions, with comparable benefits to the quality of the interpolated phonon band structure.
A No-Go guide for the Hubble tension

Rong-Gen Cai1,2,∗ Zong-Kuan Guo1,2,3,† Shao-Jiang Wang1,‡ Wang-Wei Yu1,3,§ and Yong Zhou1¶ CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China School of Fundamental Physics and Mathematical Sciences, Hangzhou Institute for Advanced Study (HIAS), University of Chinese Academy of Sciences (UCAS), Hangzhou 310024, China and School of Physical Sciences, University of Chinese Academy of Sciences (UCAS), Beijing 100049, China
Experimenting with Self-Supervision using Rotation Prediction for Image Captioning

Image captioning is a task in the field of Artificial Intelligence that merges between computer vision and natural language processing. It is responsible for generating legends that describe images, and has various applications like descriptions used by assistive technology or indexing images (for search engines for instance). This makes it a crucial topic in AI that is undergoing a lot of research. This task however, like many others, is trained on large images labeled via human annotation, which can be very cumbersome: it needs manual effort, both financial and temporal costs, it is error-prone and potentially difficult to execute in some cases (e.g. medical images). To mitigate the need for labels, we attempt to use self-supervised learning, a type of learning where models use the data contained within the images themselves as labels. It is challenging to accomplish though, since the task is two-fold: the images and captions come from two different modalities and usually handled by different types of networks. It is thus not obvious what a completely self-supervised solution would look like. How it would achieve captioning in a comparable way to how self-supervision is applied today on image recognition tasks is still an ongoing research topic. In this project, we are using an encoder-decoder architecture where the encoder is a convolutional neural network (CNN) trained on OpenImages dataset and learns image features in a self-supervised fashion using the rotation pretext task. The decoder is a Long Short-Term Memory (LSTM), and it is trained, along within the image captioning model, on MS COCO dataset and is responsible of generating captions. Our GitHub repository can be found: https://github.com/elhagry1/SSL_ImageCaptioning_RotationPrediction
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings

Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain accurate optimal value and policy estimates. While many deep reinforcement learning methods have aimed at improving sample efficiency through replay or improved exploration techniques, state of the art actor-critic and policy gradient methods still suffer from the hard exploration problem in sparse reward settings. Motivated by recent successes of value-based methods for approximating state-action values, like RBF-DQN, we explore the potential of value-based reinforcement learning for learning continuous robotic manipulation tasks in multi-task sparse reward settings. On robotic manipulation tasks, we empirically show RBF-DQN converges faster than current state of the art algorithms such as TD3, SAC, and PPO. We also perform ablation studies with RBF-DQN and have shown that some enhancement techniques for vanilla Deep Q learning such as Hindsight Experience Replay (HER) and Prioritized Experience Replay (PER) can also be applied to RBF-DQN. Our experimental analysis suggests that value-based approaches may be more sensitive to data augmentation and replay buffer sample techniques than policy-gradient methods, and that the benefits of these methods for robot manipulation are heavily dependent on the transition dynamics of generated subgoal states.
Semi-Streaming Algorithms for Submodular Function Maximization under $b$-Matching Constraint

We consider the problem of maximizing a submodular function under the b -matching constraint, in the semi-streaming model. Our main results can be summarized as follows. When the function is linear, i.e. for the maximum weight b -matching problem, we obtain a 2 + ε approximation. This improves the previous best bound of 3 + ε [12]. When the function is a non-negative monotone submodular function, we obtain a 3+2 √ 2 ≈ 5 . 828 approximation. This matches the currently best ratio [12]. When the function is a non-negative non-monotone submodular function, we obtain a 4 + 2 √ 3 ≈ 7 . 464 approximation. This ratio is also achieved in [12], but only under the simple matching constraint, while we can deal with the more general b -matching constraint. We also consider a generalized problem, where a k -uniform hypergraph is given with an extra matroid constraint imposed on the edges, with the same goal of finding a b -matching that maximizes a submodular function. We extend our technique to this case to obtain an algorithm with an approximation of 83 k + O (1). Our algorithms build on the ideas of the recent works of Levin and Wajc [12] and of Garg, Jordan, and Svensson [9]. Our main technical innovation is to introduce a data structure and associate it with each vertex and the matroid, to record the extra information of the stored edges. After the streaming phase, these data structures guide the greedy algorithm to make better choices. 2012 ACM
Discrete Lehmann representation of imaginary time Green's functions

We present an efficient basis for imaginary time Green's functions based on a low rank decomposition of the spectral Lehmann representation. The basis functions are simply a set of well-chosen exponentials, so the corresponding expansion may be thought of as a discrete form of the Lehmann representation using an effective spectral density which is a sum of $\delta$ functions. The basis is determined only by an upper bound on the product $\beta \omega_{\max}$, with $\beta$ the inverse temperature and $\omega_{\max}$ an energy cutoff, and a user-defined error tolerance $\epsilon$. The number $r$ of basis functions scales as $\mathcal{O}\left(\log(\beta \omega_{\max}) \log (1/\epsilon)\right)$. The discrete Lehmann representation of a particular imaginary time Green's function can be recovered by interpolation at a set of $r$ imaginary time nodes. Both the basis functions and the interpolation nodes can be obtained rapidly using standard numerical linear algebra routines. Due to the simple form of the basis, the discrete Lehmann representation of a Green's function can be explicitly transformed to the Matsubara frequency domain, or obtained directly by interpolation on a Matsubara frequency grid. We benchmark the efficiency of the representation on simple cases, and with a high precision solution of the Sachdev-Ye-Kitaev equation at low temperature. We compare our approach with the related intermediate representation method, and introduce an improved algorithm to build the intermediate representation basis and a corresponding sampling grid.
Filtered deformations of elliptic algebras

One of the difficulties in doing noncommutative projective geometry via explicitly presented graded algebras is that it is usually quite difficult to show flatness, as the Hilbert series is uncomputable in general. If the algebra has a regular central element, one can reduce to understanding the (hopefully more tractable) quotient. If the quotient is particularly nice, one can proceed in reverse and find all algebras of which it is the quotient by a regular central element (the filtered deformations of the quotient). We consider in detail the case that the quotient is an elliptic algebra (the homogeneous endomorphism ring of a vector bundle on an elliptic curve, possibly twisted by translation). We explicitly compute the family of filtered deformations in many cases and give a (conjecturally exhaustive) construction of such deformations from noncommutative del Pezzo surfaces. In the process, we also give a number of results on the classification of exceptional collections on del Pezzo surfaces, which are new even in the commutative case.
DCL: Differential Contrastive Learning for Geometry-Aware Depth Synthesis

We describe a method for unpaired realistic depth synthesis that learns diverse variations from the real-world depth scans and ensures geometric consistency between the synthetic and synthesized depth. The synthesized realistic depth can then be used to train task-specific networks facilitating label transfer from the synthetic domain. Unlike existing image synthesis pipelines, where geometries are mostly ignored, we treat geometries carried by the depth scans based on their own existence. We propose differential contrastive learning that explicitly enforces the underlying geometric properties to be invariant regarding the real variations been learned. The resulting depth synthesis method is task-agnostic, and we demonstrate the effectiveness of the proposed synthesis method by extensive evaluations on real-world geometric reasoning tasks. The networks trained with the depth synthesized by our method consistently achieve better performance across a wide range of tasks than state of the art, and can even surpass the networks supervised with full real-world annotations when slightly fine-tuned, showing good transferability. Our code and evaluation benchmarks will be publicly available.
Library and Information Science Research in Indian Universities: Growth, Core Journals, Keywords and Collaboration Patterns

This article maps Library and Information Science (LIS) research in Indian universities. As the two prominent citation databases, Web of Science and Scopus have very limited coverage of Indian LIS journals, the publications generated by the library and science departments of about 114 selected Indian universities and the two national institutions of importance in LIS research were extracted from Library, Information Science&Technology Abstracts (LISTA). The relevant publication records were analyzed using scientometrics and Social Network Analysis (SNA) tools. The study traces the growth of publications, prominent keywords, leading journals where the articles are published and the institutional collaboration patterns of Indian university publications. The results show that there is a growth in scholarly publications from Indian universities in LIS. However, the numbers of publications are limited to only a few universities and national institutes of importance. The maximum LIS research outputs are published in Indian journals. Bibliometrics related investigations are the most important research areas. Located in major cities of India, the productive institutes show healthy collaboration. The study concludes with some observations which may be useful for formulating policies in LIS research in India.
An Aggregation Scheme for Increased Power

We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Longitudinal data analyzing interventions often include multiple observations on individuals, some of which may be more likely to manifest a treatment effect than others. An intervention's theory of change provides guidance as to which of those observations are best situated to exhibit that treatment effect. Our power-maximizing weighting for repeated-measurements with delayed-effects scheme, PWRD aggregation, converts the theory of change into a test statistic with improved asymptotic relative efficiency, delivering tests with greater statistical power. We illustrate this method on an IES-funded cluster randomized trial testing the efficacy of a reading intervention designed to assist early elementary students at risk of falling behind their peers. The salient theory of change holds program benefits to be delayed and non-uniform, experienced after a student's performance stalls. In this instance, the PWRD technique's effect on power is found to be comparable to that of doubling the number of clusters in the experiment.
Heterogeneous aging trajectories within resting-state brain networks predict distinct ADHD symptoms in adults

Attention-deficit/hyperactivity disorder (ADHD) is increasingly being diagnosed in adults, but the neural mechanisms underlying its distinct clinical symptoms (hyperactivity and inattention) remain poorly understood. Here, we used a nested-spectral partition approach to study resting-state brain networks for ADHD patients and healthy adults and adopted hierarchical segregation and integration to predict clinical symptoms. Adult ADHD is typically characterized by an overintegrated interaction within default mode network. Limbic system is dominantly affected by ADHD and has an earlier aging functional pattern, but salient attention system is preferably affected by age and shows an opposite aging trajectory. More importantly, these two systems selectively and robustly predict distinct ADHD symptoms. Earlier-aging limbic system prefers to predict hyperactivity, and age-affected salient attention system better predicts inattention. Our findings provide a more comprehensive and deeper understanding of the neural basis of distinct ADHD symptoms and could contribute to the development of more objective clinical diagnoses.