Miracle-dz/UniFAR-FTUs huggingface dataset:
PERSPECTIVE TAKING AND NON-HUMAN GROUPS 1 Extending Perspective Taking to Non-Human Groups

Perspective taking, the active consideration of another individual’s mental states, such as their thoughts and feelings, can have positive effects in a range of human intergroup contexts. However, little is known about its effects in the context of human relations with non-human groups. In two experiments, we tested whether taking the perspective of non-human entities affects moral attitudes towards the entities’ groups as a whole, the mechanisms through which these effects occur, and how broadly or narrowly the effects generalize to groups that include the entities. Estimation of mediation models revealed no evidence of total effects of either taking the perspective of a farmed pig (Study 1, N = 271) or an artificial entity (Study 2, N = 273) on moral attitudes, compared to instructions to stay objective or a neutral control condition. However, in both studies, we found evidence of indirect effects of perspective taking on moral attitudes via empathic concern and selfother overlap, confirming two mechanisms well-established in the perspective taking literature. Moreover, these indirect effects generalized to all non-human animals in Study 1 and all artificial entities in Study 2. The lack of total effects was partly explained by competitive mediation: in both studies, after accounting for the significant indirect paths, we found positive effects of staying objective on moral attitudes. This is the opposite effect typically found in human intergroup contexts and suggests an important difference with perspective taking in the context of non-human groups.
Bandit Algorithms for Precision Medicine

The Oxford English Dictionary defines precision medicine as"medical care designed to optimize efficiency or therapeutic benefit for particular groups of patients, especially by using genetic or molecular profiling. "It is not an entirely new idea: physicians from ancient times have recognized that medical treatment needs to consider individual variations in patient characteristics. However, the modern precision medicine movement has been enabled by a confluence of events: scientific advances in fields such as genetics and pharmacology, technological advances in mobile devices and wearable sensors, and methodological advances in computing and data sciences. This chapter is about bandit algorithms: an area of data science of special relevance to precision medicine. With their roots in the seminal work of Bellman, Robbins, Lai and others, bandit algorithms have come to occupy a central place in modern data science ( Lattimore and Szepesvari, 2020). Bandit algorithms can be used in any situation where treatment decisions need to be made to optimize some health outcome. Since precision medicine focuses on the use of patient characteristics to guide treatment, contextual bandit algorithms are especially useful since they are designed to take such information into account. The role of bandit algorithms in areas of precision medicine such as mobile health and digital phenotyping has been reviewed before (Tewari and Murphy, 2017; Rabbi et al., 2019). Since these reviews were published, bandit algorithms have continued to find uses in mobile health and several new topics have emerged in the research on bandit algorithms. This chapter is written for quantitative researchers in fields such as statistics, machine learning, and operations research who might be interested in knowing more about the algorithmic and mathematical details of bandit algorithms that have been used in mobile health.
The Impact of Powerful Jets on the Far-infrared Emission of an Extreme Radio Quasar at z ∼ 6

The interactions between radio jets and the interstellar medium play a defining role for the coevolution of central supermassive black holes and their host galaxies, but observational constraints on these feedback processes are still very limited at redshifts z > 2. We investigate the radio-loud quasar PSO J352.4034–15.3373 at z ∼ 6 at the edge of the Epoch of Reionization. This quasar is among the most powerful radio emitters and the first one with direct evidence of extended radio jets (∼1.6 kpc) at these high redshifts. We analyze NOrthern Extended Millimeter Array and Atacama Large Millimeter/submillimeter Array millimeter data targeting the CO (6–5) and [C ii] far-infrared (FIR) emission lines, respectively, and the underlying continuum. The broad 440 ± 80 km s−1 and marginally resolved [C ii] emission line yields a systemic redshift of z = 5.832 ± 0.001. Additionally, we report a strong 215 MHz radio continuum detection, 88 ± 7 mJy, using the Giant Metrewave Radio Telescope. This measurement significantly improves the constraints at the low-frequency end of the spectral energy distribution of this quasar. In contrast to what is typically observed in high-redshift radio-quiet quasars, we show that cold dust emission alone cannot reproduce the millimeter continuum measurements. This is evidence that the strong synchrotron emission from the quasar contributes substantially to the emission even at millimeter (FIR in the rest-frame) wavelengths. This quasar is an ideal system to probe the effects of radio jets during the formation of a massive galaxy within the first gigayear of the universe.
Megadiff: A Dataset of 600k Java Source Code Changes Categorized by Diff Size

This paper presents Megadiff, a dataset of source code diffs. It focuses on Java, with strict inclusion criteria based on commit message and diff size. Megadiff contains 663 029 Java diffs that can be used for research on commit comprehension, fault localization, automated program repair, and machine learning on code changes.
CPNet: Cross-Parallel Network for Efficient Anomaly Detection

Anomaly detection in video streams is a challenging problem because of the scarcity of abnormal events and the difficulty of accurately annotating them. To alleviate these issues, unsupervised learning-based prediction methods have been previously applied. These approaches train the model with only normal events and predict a future frame from a sequence of preceding frames by use of encoder-decoder architectures so that they result in small prediction errors on normal events but large errors on abnormal events. The architecture, however, comes with the computational burden as some anomaly detection tasks require low computational cost without sacrificing performance. In this paper, Cross-Parallel Network (CPNet) for efficient anomaly detection is proposed here to minimize computations without performance drops. It consists of N smaller parallel U-Net, each of which is designed to handle a single input frame, to make the calculations significantly more efficient. Additionally, an inter-network shift module is incorporated to capture temporal relationships among sequential frames to enable more accurate future predictions. The quantitative results show that our model requires less computational cost than the baseline U-Net while delivering equivalent performance in anomaly detection.
Knowledge Enhanced Multi-modal Fake News Detection

Recent years have witnessed the significant damage caused by various types of fake news. Although considerable effort has been applied to address this issue and much progress has been made on detecting fake news, most existing approaches mainly rely on the textual content and/or social context, while knowledge-level information---entities extracted from the news content and the relations between them---is much less explored. Within the limited work on knowledge-based fake news detection, an external knowledge graph is often required, which may introduce additional problems: it is quite common for entities and relations, especially with respect to new concepts, to be missing in existing knowledge graphs, and both entity prediction and link prediction are open research questions themselves. Therefore, in this work, we investigate \textbf{knowledge-based fake news detection that does not require any external knowledge graph. } Specifically, our contributions include: (1) transforming the problem of detecting fake news into a subgraph classification task---entities and relations are extracted from each news item to form a single knowledge graph, where a news item is represented by a subgraph. Then a graph neural network (GNN) model is trained to classify each subgraph/news item. (2) Further improving the performance of this model through a simple but effective multi-modal technique that combines extracted knowledge, textual content and social context. Experiments on multiple datasets with thousands of labelled news items demonstrate that our knowledge-based algorithm outperforms existing counterpart methods, and its performance can be further boosted by the multi-modal approach.
Why do People Participate in Small Online Communities?

Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a "successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.
Evaluation of Load Prediction Techniques for Distributed Stream Processing

Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at which events arrive at DSP systems can vary considerably over time, which may be due to trends, cyclic, and seasonal patterns within the data streams. A priori knowledge of incoming workloads enables proactive approaches to resource management and optimization tasks such as dynamic scaling, live migration of resources, and the tuning of configuration parameters during run-times, thus leading to a potentially better Quality of Service. In this paper we conduct a comprehensive evaluation of different load prediction techniques for DSP jobs. We identify three use-cases and formulate requirements for making load predictions specific to DSP jobs. Automatically optimized classical and Deep Learning methods are being evaluated on nine different datasets from typical DSP domains, i.e. the IoT, Web 2.0, and cluster monitoring. We compare model performance with respect to overall accuracy and training duration. Our results show that the Deep Learning methods provide the most accurate load predictions for the majority of the evaluated datasets.
Robust and Dexterous Dual-arm Tele-Cooperation using Fractal Impedance Control

—Deploying robots from isolated operations to shared environments has been an increasing trend in robotics for the last decades. However, the requirement of robust interaction in highly variable environments is still beyond the capability of most robots. We proposed to achieve robustness of various interactions by using the Fractal Impedance Control (FIC) and exploiting its non-linear stiﬀness to adapt to multiple cooperative scenarios, which is applicable to both manipulation and teleoperation applications. The proposed method was evaluated by a wide range of experiments: drilling, moving objects with unknown dynamics, and interacting with humans. The extensive validations demonstrated that the proposed method is very robust in presence of delays and reduced bandwidth in the communication link between master and follower. The results conﬁrmed that the proposed method can enhance the robustness signiﬁcantly and allow switching tasks freely between manipulation, human-robot cooperation and teleoperation without the need of extensive re-tuning of the controllers.
Deep Reinforcement Learning for Demand Driven Services in Logistics and Transportation Systems: A Survey

Recent technology development brings the booming of numerous new Demand-Driven Services (DDS) into urban lives, including ridesharing, on-demand delivery, express systems and warehousing. In DDS, a service loop is an elemental structure, including its service worker, the service providers and corresponding service targets. The service workers should transport either humans or parcels from the providers to the target locations. Various planning tasks within DDS can thus be classified into two individual stages: 1) Dispatching, which is to form service loops from demand/supply distributions, and 2)Routing, which is to decide specific serving orders within the constructed loops. Generating high-quality strategies in both stages is important to develop DDS but faces several challenging. Meanwhile, deep reinforcement learning (DRL) has been developed rapidly in recent years. It is a powerful tool to solve these problems since DRL can learn a parametric model without relying on too many problem-based assumptions and optimize long-term effect by learning sequential decisions. In this survey, we first define DDS, then highlight common applications and important decision/control problems within. For each problem, we comprehensively introduce the existing DRL solutions. We also introduce open simulation environments for development and evaluation of DDS applications. Finally, we analyze remaining challenges and discuss further research opportunities in DRL solutions for DDS.
Fast Optimization of Impulsive Perturbed Orbit Rendezvous with Finite Iterations

An-yi Huang* Xi’an Satellite Control Center, 710043 Xi’an, People’s Republic of China National University of Defense Technology, 410073 Changsha, People’s Republic of China Ya-zhong Luo† National University of Defense Technology, 410073 Changsha, People’s Republic of China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions, 410073 Changsha, People’s Republic of China and Heng-nian Li Xi’an Satellite Control Center, 710043 Xi’an, People’s Republic of China
The Multi-Objective Polynomial Optimization

The multi-objective optimization is to optimize several objective functions over a common feasible set. Because the objectives usually do not share a common optimizer, people often consider (weakly) Pareto points. This paper studies multi-objective optimization problems that are given by polynomial functions. First, we study the geometry for (weakly) Pareto values and represent Pareto front as the boundary of a convex set. Linear scalarization problems (LSPs) and Chebyshev scalarization problems (CSPs) are typical approaches for getting (weakly) Pareto points. For LSPs, we show how to use tight relaxations to solve them and how to detect existence or nonexistence of proper weights. For CSPs, we show how to solve them by moment relaxations. Moreover, we show how to check whether a given point is a (weakly) Pareto point or not and how to detect existence or nonexistence of (weakly) Pareto points. We also study how to detect unboundedness of polynomial optimization, which is used to detect nonexistence of proper weights or (weakly) Pareto points. Funding: J. Nie is partially supported by the National Science Foundation [Grant DMS-2110780].
Headed-Span-Based Projective Dependency Parsing

We propose a new method for projective dependency parsing based on headed spans. In a projective dependency tree, the largest subtree rooted at each word covers a contiguous sequence (i.e., a span) in the surface order. We call such a span marked by a root word headed span. A projective dependency tree can be represented as a collection of headed spans. We decompose the score of a dependency tree into the scores of the headed spans and design a novel O(n^3) dynamic programming algorithm to enable global training and exact inference. Our model achieves state-of-the-art or competitive results on PTB, CTB, and UD
Dynamics and Morphology of Cold Gas in Fast, Radiatively Cooling Outflows: Constraining AGN Energetics with Horseshoes

Warm ionized and cold neutral outflows with velocities exceeding 100 km s−1 are commonly observed in galaxies and clusters. However, theoretical studies indicate that ram pressure from a hot wind, driven either by the central active galactic nucleus (AGN) or a starburst, cannot accelerate existing cold gas to such high speeds without destroying it. In this work we explore a different scenario, where cold gas forms in a fast, radiatively cooling outflow with temperature T ≲ 107 K. Using 3D hydrodynamic simulations, we demonstrate that cold gas continuously fragments out of the cooling outflow, forming elongated filamentary structures extending tens of kiloparsecs. For a range of physically relevant temperature and velocity configurations, a ring of cold gas perpendicular to the direction of motion forms in the outflow. This naturally explains the formation of transverse cold gas filaments such as the blue loop and the horseshoe filament in the Perseus cluster. Based on our results, we estimate that the AGN outburst responsible for the formation of these two features drove bipolar outflows with velocity >2000 km s−1 and total kinetic energy >8 × 1057 erg about ∼10 Myr ago. We also examine the continuous cooling in the mixing layer between hot and cold gas, and find that radiative cooling only accounts for ∼10% of the total mass cooling rate, indicating that observations of soft X-ray and far-ultraviolet emission may significantly underestimate the growth of cold gas in the cooling flow of galaxy clusters.
Gemini2: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics

Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini2, a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini2 can automatically recommend intermediate keyframes for designers to consider. The Gemini2 recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini2’s recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini2 and Gemini. We find that Gemini2’s animation recommendation ranking is well aligned with subjects’ preferences, and Gemini2 can recommend favorable animations that Gemini cannot support.
Meta-repository of screening mammography classifiers

Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable comparison between different methods, we release a meta-repository containing models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of AI models on any screening mammography data set. At its inception, our meta-repository contains five state-of-the-art models with open-source implementations and cross-platform compatibility. We compare their performance on seven international data sets. Our framework has a flexible design that can be generalized to other medical image analysis tasks. The meta-repository is available at https://www.github. com/nyukat/mammography_metarepository.
Automated Audio Captioning Using Transfer Learning and Reconstruction Latent Space Similarity Regularization

In this paper, we examine the use of Transfer Learning using Pretrained Audio Neural Networks (PANNs) [1], and propose an architecture that is able to better leverage the acoustic features provided by PANNs for the Automated Audio Captioning Task [2]. We also introduce a novel self-supervised objective, Reconstruction Latent Space Similarity Regularization (RLSSR). The RLSSR module supplements the training of the model by maximizing the similarity between the encoder and decoder embedding. The combination of both methods allows us to surpass state of the art results by a significant margin on the Clotho dataset [3] across several metrics and benchmarks.
Transients from the Cataclysmic Deaths of Cataclysmic Variables

We explore the observational appearance of the merger of a low-mass star with a white dwarf (WD) binary companion. We are motivated by recent work finding that multiple tensions between the observed properties of cataclysmic variables (CVs) and standard evolution models are resolved if a large fraction of CV binaries merge as a result of unstable mass transfer. Tidal disruption of the secondary forms a geometrically thick disk around the WD, which subsequently accretes at highly super-Eddington rates. Analytic estimates and numerical hydrodynamical simulations reveal that outflows from the accretion flow unbind a large fraction ≳90% of the secondary at velocities ∼500–1000 km s−1 within days of the merger. Hydrogen recombination in the expanding ejecta powers optical transient emission lasting about a month with a luminosity ≳1038 erg s−1, similar to slow classical novae and luminous red novae from ordinary stellar mergers. Over longer timescales the mass accreted by the WD undergoes hydrogen shell burning, inflating the remnant into a giant of luminosity ∼300–5000 L ⊙, effective temperature T eff ≈ 3000 K, and lifetime ∼104–105 yr. We predict that ∼103–104 Milky Way giants are CV merger products, potentially distinguishable by atypical surface abundances. We explore whether any Galactic historical slow classical novae are masquerading CV mergers by identifying four such post-nova systems with potential giant counterparts for which a CV merger origin cannot be ruled out. We address whether the historical transient CK Vul and its gaseous/dusty nebula resulted from a CV merger.