Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

Abstract:A growing number of methods aim to assess the challenging question of treatment effect variation in observational studies. This special section of Observational Studies reports the results of a workshop conducted at the 2018 Atlantic Causal Inference Conference designed to understand the similarities and differences across these methods. We invited eight groups of researchers to analyze a synthetic observational data set that was generated using a recent large-scale randomized trial in education. Overall, participants employed a diverse set of methods, ranging from matching and flexible outcome modeling to semiparametric estimation and ensemble approaches. While there was broad consensus on the topline estimate, there were also large differences in estimated treatment effect moderation. This highlights the fact that estimating varying treatment effects in observational studies is often more challenging than estimating the average treatment effect alone. We suggest several directions for future work arising from this workshop.
Multi-Adapter RGBT Tracking

The task of RGBT tracking aims to take the complementary advantages from visible spectrum and thermal infrared data to achieve robust visual tracking, and receives more and more attention in recent years. Existing works focus on modality-specific information integration by introducing modality weights to achieve adaptive fusion or learning robust feature representations of different modalities. Although these methods could effectively deploy the modality-specific properties, they ignore the potential values of modality-shared cues as well as instance-aware information, which are crucial for effective fusion of different modalities in RGBT tracking. In this paper, we propose a novel Multi-Adapter convolutional Network (MANet) to jointly perform modality-shared, modality-specific and instance-aware feature learning in an end-to-end trained deep framework for RGBT tracking. We design three kinds of adapters within our network. In a specific, the generality adapter is to extract shared object representations, the modality adapter aims at encoding modality-specific information to deploy their complementary advantages, and the instance adapter is to model the appearance properties and temporal variations of a certain object. Moreover, to reduce computational complexity for real-time demand of visual tracking, we design a parallel structure of generic adapter and modality adapter. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against other state-ofthe-art RGB and RGBT tracking algorithms.
Testing the background solar wind modelled by EUHFORIA

In order to address the growing need for the more accurate space weather predictions, a new model named EUHFORIA (EUropean Heliospheric FORecasting Information Asset) was recently developed (Pomoell and Poedts, 2018). We present the first results of the solar wind modeling with EUHFORIA and identify possible limitation of its present set up. Using the basic EUHFORIA 1.0. 4. model setup with the default input parameters, we modeled background solar wind (no coronal mass ejections) and compared obtained results with ACE, in situ observations. For the need of statistical study we developed a technique of combining daily EUHFORIA runs into continuous time series. Using the combined time series we performed statistical study of the solar wind for years 2008 (low solar activity) and 2012 (high solar activity) with the focus on the in situ speed and density. We find for the low activity phase a better match between model results and observations compared to the considered high activity time interval. The quality of the modeled solar wind parameters is found to be very variable. Therefore, to better understand obtained results we also qualitatively inspected characteristics of coronal holes, sources of the studied fast streams. We discuss how different characteristics of the coronal holes and input parameters to EUHFORIA influence the modeled fast solar wind, and suggest the possibilities for the improvements of the model. SOLA_keyword_list. txt.
Testing for Sample Selection

This paper provides a unified approach for detecting sample selection in nonparametric conditional quantile \textit{and} mean functions. Our testing strategy consists of a two-step procedure: the first test is an omitted predictor test with the propensity score as omitted variable. This test has power against $\sqrt{n}-$alternatives. While failure to reject the null implies no selection, we cannot, as any omnibus test, distinguish between rejection due to genuine selection or to misspecification. Since differentiation of the latter has implications for nonparametric (point) identification and estimation of the conditional quantile function, our second test is designed to detect misspecification. Using only individuals with propensity score close to one, this test relies on an `identification at infinity' argument, but accommodates cases of irregular identification. Finally, our testing procedure does not require any parametric assumptions on the selection equation, and all our results in the quantile case hold uniformly across quantile ranks in a compact set. We apply our procedure to test for selection in log hourly wages using UK Family Expenditure Survey data.
Hubble Space Telescope Imaging of Antlia B: Star Formation History and a New Tip of the Red Giant Branch Distance

A census of the satellite population around dwarf galaxy primary hosts in environments outside the Local Group is essential to understanding Λ cold dark matter galaxy formation and evolution on the smallest scales. We present deep optical Hubble Space Telescope imaging of the gas-rich, faint dwarf galaxy Antlia B (MV = −9.4)—a likely satellite of NGC 3109 (D = 1.3 Mpc)—discovered as part of our ongoing survey of primary host galaxies similar to the Magellanic Clouds. We derive a new tip of the red giant branch distance of D = 1.35 ± 0.06 Mpc (m − M = 25.65 ± 0.10), consistent with membership in the nearby NGC 3109 dwarf association. The color–magnitude diagram (CMD) shows both a prominent old, metal-poor stellar component and confirms a small population of young, blue stars with ages ≲1 Gyr. We use the CMD fitting algorithm MATCH to derive the star formation history (SFH) and find that it is consistent with the typical dwarf irregular or transitional dwarf galaxy (dTrans) in the Local Group. Antlia B shows relatively constant stellar mass growth for the first ∼10–11 Gyr and almost no growth in the last ∼2–3 Gyr. Despite being gas-rich, Antlia B shows no evidence of active star formation (i.e., no Hα emission) and should therefore be classified as a dTrans dwarf. Both Antlia B and the Antlia dwarf (dTrans) are likely satellites of NGC 3109, suggesting that the cessation of ongoing star formation in these galaxies may be environmentally driven. Future work studying the gas kinematics and distribution in Antlia B will explore this scenario in greater detail. Our work highlights the fact that detailed studies of nearby dwarf galaxies in a variety of environments may continue to shed light on the processes that drive the SFH and evolution of dwarf galaxies more generally.
Entropy Symmetrization and High-Order Accurate Entropy Stable Numerical Schemes for Relativistic MHD Equations

This paper presents entropy symmetrization and high-order accurate entropy stable schemes for the relativistic magnetohydrodynamic (RMHD) equations. It is shown that the conservative RMHD equations are not symmetrizable and do not possess an entropy pair. To address this issue, a symmetrizable RMHD system, which admits a convex entropy pair, is proposed by adding a source term into the equations. Arbitrarily high-order accurate entropy stable finite difference schemes are then developed on Cartesian meshes based on the symmetrizable RMHD system. The crucial ingredients of these schemes include (i) affordable explicit entropy conservative fluxes which are technically derived through carefully selected parameter variables, (ii) a special high-order discretization of the source term in the symmetrizable RMHD system, and (iii) suitable high-order dissipative operators based on essentially non-oscillatory reconstruction to ensure the entropy stability. Several benchmark numerical tests demonstrate the accuracy and robustness of the proposed entropy stable schemes of the symmetrizable RMHD equations.
Photonic architecture for reinforcement learning

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.
On the Importance of News Content Representation in Hybrid Neural Session-based Recommender Systems

News recommender systems are designed to surface relevant information for online readers by personalizing their user experiences. A particular problem in that context is that online readers are often anonymous, which means that this personalization can only be based on the last few recorded interactions with the user, a setting named session-based recommendation. Another particularity of the news domain is that constantly fresh articles are published, which should be immediately considered for recommendation. To deal with this item cold-start problem, it is important to consider the actual content of items when recommending. Hybrid approaches are therefore often considered as the method of choice in such settings. In this work, we analyze the importance of considering content information in a hybrid neural news recommender system. We contrast content-aware and content-agnostic techniques and also explore the effects of using different content encodings. Experiments on two public datasets confirm the importance of adopting a hybrid approach. Furthermore, we show that the choice of the content encoding can have an impact on the resulting performance.
A Differentiable Programming System to Bridge Machine Learning and Scientific Computing

Scientific computing is increasingly incorporating the advancements in machine learning and the ability to work with large amounts of data. At the same time, machine learning models are becoming increasingly sophisticated and exhibit many features often seen in scientific computing, stressing the capabilities of machine learning frameworks. Just as the disciplines of scientific computing and machine learning have shared common underlying infrastructure in the form of numerical linear algebra, we now have the opportunity to further share new computational infrastructure, and thus ideas, in the form of Differentiable Programming. We describe Zygote, a Differentiable Programming system that is able to take gradients of general program structures. We implement this system in the Julia programming language. Our system supports almost all language constructs (control flow, recursion, mutation, etc.) and compiles high-performance code without requiring any user intervention or refactoring to stage computations. This enables an expressive programming model for deep learning, but more importantly, it enables us to incorporate a large ecosystem of libraries in our models in a straightforward way. We discuss our approach to automatic differentiation, including its support for advanced techniques such as mixed-mode, complex and checkpointed differentiation, and present several examples of differentiating programs.
Edge Detection and Geometric Methods in Computer Vision

Abstract : Basic problems of vision are studied form the viewpoint of modern differential topology and geometry; primarily: edge detection, stereo matching, picture representation at multiple scales, and motion. Some mathematical background is provided for the nonexpert. Some new edge detection techniques are introduced, including a nonlinear operator based on a symmetry principle, a variational approach to global edge finding, and a least-squares localization method. A theorem is proved which shows that localizing edge position and orientation requires at least 2 orientation dependent families of convolution operators. A unifying mathematical structure is presented for vision, notably stereo, motion stereo, optic flow (apparent flow of visual space under motion), and matching. The general matching problem is analyzed, and it is proved that generically, general matching is highly degenerate for monochrome pictures, but has a unique solution for 2 or more color dimensions. The result is extended to pictures with bias and gain. Small diagrams and level set topology are introduced as invariants usefull for matching, reducing the problem to graph or tree making. The level set topology tree is also proposed as a method of multi-scale description of the picture, and shown to be and invariant generalization of the 'scale space' technique.
Research and Implementation of the Frame-based Dialogue Management Model

Dialogue management module is a very important component in the spoken dialogue system, it controls the transaction between the system and user and decides which operation should be executed by system in each step. The frame-based dialogue management model, which is applied in Shanghai Intelligent Traffic Information System, is made up of query frame and strategy of dialogue management based on query frame. This model makes transaction both consistent and flexible with mixed-initiative dialogue control, and can be easily transplanted.
Network-Aware Coordination of Residential Distributed Energy Resources

Rooftop solar and batteries, along with other distributed energy resources (DERs), add a new demand-side flexibility, which, when harnessed, will enable distribution operators to more efficiently manage their constrained networks. This paper presents network-aware coordination (NAC), an approach for coordinating DER within unbalanced distribution network constraints, which utilizes the alternating direction method of multipliers (ADMMs) to solve a distributed receding-horizon OPF. As far as we are aware, this paper is the first to report on the practical implementation and performance of an ADMM-based technique solving a significant network problem in live operations. We present real-world trial results of NAC coordinating 31 residential batteries on a constrained feeder within Tasmania’s 11-kV distribution network. The batteries are coordinated to manage the network’s constraints during periods of high feeder demand, decreasing the need for expensive conventional network management, in this case a diesel generator. We achieve a 34% reduction in diesel over seven peaks with 31 batteries capable of meeting 10% of peak demand. Supplementary simulations indicate the potential for a 74% diesel reduction if battery numbers were increased to 100. We find that compared to uncoordinated battery response, the NAC achieves 13% lower total costs over the trial period.
Chinese Word Segmentation without Using Dictionary Based on Unsupervised Learning Strategy

The study of word segmentation using neither a dictionary nor an annotated corpus will help to improve the robustness of Chinese word segmentation systems in practical applications. This paper presents a new statistical measure, md, for estimating the associative strength between two adjacent Chinese characters in terms of a linear combination of two common statistical measures, namely, mutual information and t -test, puts forward the concepts of peak and valley of md, and gives an algorithm accordingly. The experimental result on large-scale open tests shows that the proposed algorithm achieves 85 88% segmentation accuracy(defined as the ratio of the number of correctly segmented character locations to the total number of character locations in the testing texts). The most advantage of the algorithm is that it can easily adapt to new application domains because all statistical data needed is based on Bigram of Chinese characters derived from raw corpora automatically.
Digital Image Watermarking Algorithm Based on Phase Shift Keying

As an effective technique of copyright protection, digital watermarking has become a new focus of research in recent years. In this paper, a noninvertible digital image watermarking algorithm is proposed based on phase shift keying (PSK). The watermark used is a binary image. In watermark embedding, the sine signals with different phase are used to modulate watermark bits and embedded in the host image wavelet coefficients that act as signal carrier. In watermark extraction, the sine signal with same frequency is picked up from watermarked wavelet coefficients and its phase is used to extract watermark bits. The method has very good watermark security and the watermark is extracted without using original unwatermarked image. The experiments show that the quality of watermarked images is guaranteed. The algorithm is very robust against image manipulations such as JPEG compression, the image filtering and the noise addition.
Programming Scala: Tackle Multi-Core Complexity on the Java Virtual Machine

The increasing popularity and availability of multicore processors is creating a whole new set of challenges--although you can enjoy true concurrency, you're now faced with higher contention and synchronization issues. Deploying an existing application on a multicore processor may bring out previously hidden concurrency issues. Java's multi-threading facility by itself isn't enough---it's a very low level abstraction. Instead, you need a paradigm that provides a higher level of abstraction to deal with concurrency. It's time to embrace Functional Programming. Scala is a hybrid Object-Oriented/Functional Programming language on the JVM. Using Scala, you can create traditional imperative programs, intermix them with Java code, and at the same time take advantage of higher levels of abstraction. You can use features that lead to concise, highly expressive code that remove the pain of dealing with concurrency. Programming Scala will show you the fundamentals of functional programming using Scala. Very quickly, you'll learn how this statically typed language can give you dynamic capabilities to create concise, scalable, highly capable concurrent code. Pragmatic programmers always use the right tool for the job. For concurrent programming on the Java VM, Scala is the tool, and Programming Scala by award-winning author Venkat Subramaniam is your guide.
Principles Analysis and Implementation of Chinese Search Engine

This article presents the status and classification of current search engines,explains their working principles and mentions the development of Chinese search engine. The key techniques involved -full content retrieval and Chinese words segmentation are referred, the two ways of constructing the full content retrieval index are analyzed and the differences between them are made clear, also the commonly used methods in words segmentation are listed. The article expatiates on some schemes in developing a practical system and certain programming skills are provided. Finally authors make some suggestions about the future of Chinese search engine.
Safety analysis of children transported on bicycle-mounted seat during traffic accidents

Abstract The paper concentrates on the safety analysis of children transported in bicycle-mounted baby carriers. The biomechanics are widely researched for pedestrians and cyclists, but there is no clear understanding of children’s kinematics during accidents in commonly used baby carriers. Using a finite element vehicle model coupled with a multibody bicycle, dummies and a generic baby carrier, the authors numerically simulated various impact scenarios. The results of the simulations proved a complex motion of the bicycle. Validation of the simulative outcome was done by comparison to full-scale tests from the literature. This includes Wrap Around Distances and cyclist kinematics as well as the frontal shape parameters of the impacting vehicle. Further, the crashworthiness of the baby carrier was investigated. It was concluded that the belt system, which is attached to the baby carrier, was in several cases not able to secure the child in its position. In a result, the studied baby carrier showed poor performance as a passive safety device during the simulated accidents. In this respect, also possible injuries of the child’s head, brain and neck were discussed. Finally, the authors concluded that the kinematics of the impacted child buckled in a baby carrier are neither representing cyclist’s nor pedestrian’s kinematics.
A Wearable Dual-Band Low Profile High Gain Low SAR Antenna AMC-Backed for WBAN Applications

A dual-band, low profile, high gain, and low specific absorption rate (SAR) triangular slotted monopole antenna backed with a $4\times 4$ artificial magnetic conductor (AMC) array is presented for wireless body area network (WBAN) applications. The antenna is printed on a Rogers ULTRALAM 3850 substrate, whereas the AMC array is printed on a RO3003 substrate. The design operates at 3.5 GHz, for WiMAX wireless applications, and at 5.8 GHz for the ISM Band. The proposed antenna preserved the dual-band resonance and exhibited acceptable gain and SAR at a separation of 15 mm from the human body model. To reduce such separation and achieve enhancements to gain and SAR, an AMC array was utilized. In free space, gain enhancements by 6.8 and 3.7 dBi were achieved at both frequencies, respectively. Furthermore, over a gap of 1 mm from the human body, gain enhancements by 23.3 and 13.9 dBi were achieved at both frequencies, respectively. In addition, SAR reductions by almost 99% were attained. The antenna was fabricated and measured where a very good agreement was observed between simulated and measured results, with and without the incorporated AMC array. With such results, the proposed design can be highly recommended for wearable medical applications, specifically for diabetic patients.
Internet of Things for Smart Grid Automation

The internet of things (IoT) is applied to many areas due to wide area connectivity and massive amount of information processing capability. It can be applied to the smart grid for sensing, monitoring and controlling the grid state information. The IoT enabled sensors are used to sense the grid state information, and then H-infinity algorithm is adopted to estimate the system states. Simulation result using the micro grid illustrates that the proposed algorithm can estimate the system states within a short period of time. Consequently, this framework is valuable to design the smart energy management systems in future.