Miracle-dz/UniFAR-FTUs huggingface dataset:
A low Hubble constant from galaxy distribution observations

An accurate determination of the Hubble constant remains a puzzle in observational cosmology. The possibility of a new physics has emerged with a significant tension between the current expansion rate of our Universe measured from the cosmic microwave background by the Planck satellite and from local methods. In this paper, new tight estimates on this parameter are obtained by considering two data sets from galaxy distribution observations: galaxy cluster gas mass fractions and baryon acoustic oscillation measurements. Priors from the Big Bang nucleosynthesis (BBN) were also considered. By considering the flat ΛCDM and XCDM models, and the non-flat ΛCDM model, our main results are: H0=65.9+1.5−1.5 km s−1 Mpc−1, H0=65.9+4.4−4.0 km s−1 Mpc−1 and H0=64.3+ 4.5− 4.4 km s−1 Mpc−1 in 2σ c.l. , respectively. These estimates are in full agreement with the Planck satellite results. Our analyses in these cosmological scenarios also support a negative value for the deceleration parameter at least in 3σ c.l.
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) can suffer from inferior performance due to unstable training, especially for text generation. We propose a new variational GAN training framework which enjoys superior training stability. Our approach is inspired by a connection of GANs and reinforcement learning under a variational perspective. The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that stabilizes discriminator training by downplaying bad-quality fake samples. We provide theoretical analysis on the convergence of our approach. By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain significantly improved performance over a range of tasks, including text generation, text style transfer, and image generation.
DECSTR: Learning Goal-Directed Abstract Behaviors using Pre-Verbal Spatial Predicates in Intrinsically Motivated Agents

Intrinsically motivated agents freely explore their environment and set their own goals. Such goals are traditionally represented as specific states, but recent works introduced the use of language to facilitate abstraction. Language can, for example, represent goals as sets of general properties that surrounding objects should verify. However, language-conditioned agents are trained simultaneously to understand language and to act, which seems to contrast with how children learn: infants demonstrate goal-oriented behaviors and abstract spatial concepts very early in their development, before language mastery. Guided by these findings from developmental psychology, we introduce a high-level state representation based on natural semantic predicates that describe spatial relations between objects and that are known to be present early in infants. In a robotic manipulation environment, our DECSTR system explores this representation space by manipulating objects, and efficiently learns to achieve any reachable configuration within it. It does so by leveraging an object-centered modular architecture, a symmetry inductive bias, and a new form of automatic curriculum learning for goal selection and policy learning. As with children, language acquisition takes place in a second phase, independently from goal-oriented sensorimotor learning. This is done via a new goal generation module, conditioned on instructions describing expected transformations in object relations. We present ablations studies for each component and highlight several advantages of targeting abstract goals over specific ones. We further show that using this intermediate representation enables efficient language grounding by evaluating agents on sequences of language instructions and their logical combinations.
Diffuse Ionized Gas in Simulations of Multiphase, Star-forming Galactic Disks

It has been hypothesized that photons from young, massive star clusters are responsible for maintaining the ionization of diffuse warm ionized gas seen in both the Milky Way and other disk galaxies. For a theoretical investigation of the warm ionized medium (WIM), it is crucial to solve radiation-transfer equations where the interstellar medium (ISM) and clusters are modeled self-consistently. To this end, we employ a solar neighborhood model of Three-phase Interstellar Medium in Galaxies Resolving Evolution with Star Formation and Supernova Feedback (TIGRESS), a magnetohydrodynamic simulation of the multiphase, star-forming ISM, and post-process the simulation with an adaptive ray tracing method to transfer UV radiation from star clusters. We find that the WIM volume filling factor is highly variable, and sensitive to the rate of ionizing photon production and ISM structure. The mean WIM volume filling factor rises to ∼0.15 at . Approximately half of ionizing photons are absorbed by gas and half by dust; the cumulative ionizing photon escape fraction is 1.1%. Our time-averaged synthetic line profile matches Wisconsin Mapper observations on the redshifted (outflowing) side, but has insufficient intensity on the blueshifted side. Our simulation matches the Dickey–Lockman neutral density profile well, but only a small fraction of snapshots have high-altitude WIM density consistent with Reynolds Layer estimates. We compute a clumping correction factor that is remarkably constant with distance from the midplane and time; this can be used to improve estimates of ionized gas mass and mean electron density from observed surface brightness profiles in edge-on galaxies.
Anoplophora glabripennis first finding in wood packaging material in Denmark.

In 2008 a few specimens of Anoplophora glabripennis (Asian Longhorn Beetle), were found for the first time in Denmark. The finding was connected with granite imported in wooden crates, although the wood packaging material was marked as outlined in ISPM no. 15. Activities on tracing back and official actions are described. A survey was performed to detect establishment of any further A. glabripennis having possibly escaped the crates. An information campaign for public awareness and for importers of granite was organised. Finally lessons learned are described.
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed Environments

Recent investigations into sum-product-max networks (SPMN) that generalize sum-product networks (SPN) offer a data-driven alternative for decision making, which has predominantly relied on handcrafted models. SPMNs computationally represent a probabilistic decision-making problem whose solution scales linearly in the size of the network. However, SPMNs are not well suited for sequential decision making over multiple time steps. In this paper, we present recurrent SPMNs (RSPMN) that learn from and model decision-making data over time. RSPMNs utilize a template network that is unfolded as needed depending on the length of the data sequence. This is significant as RSPMNs not only inherit the benefits of SPMNs in being data driven and mostly tractable, they are also well suited for sequential problems. We establish conditions on the template network, which guarantee that the resulting SPMN is valid, and present a structure learning algorithm to learn a sound template network. We demonstrate that the RSPMNs learned on a testbed of sequential decision-making data sets generate MEUs and policies that are close to the optimal on perfectly-observed domains. They easily improve on a recent batch-constrained reinforcement learning method, which is important because RSPMNs offer a new model-based approach to offline reinforcement learning.
Building trust in digital policing: a scoping review of community policing apps

ABSTRACT Perceptions of police trustworthiness are linked to citizens’ willingness to cooperate with police. Trust can be fostered by introducing accountability mechanisms, or by increasing a shared police/citizen identity, both which can be achieved digitally. Digital mechanisms can also be designed to safeguard, engage, reassure, inform, and empower diverse communities. We systematically scoped 240 existing online citizen-police and relevant third-party communication apps, to examine whether they sought to meet community needs and policing visions. We found that 82% required registration or login details, 55% of those with a reporting mechanism allowed for anonymous reporting, and 10% provided an understandable privacy policy. Police apps were more likely to seek to reassure, safeguard and inform users, while third-party apps were more likely to seek to empower users. As poorly designed apps risk amplifying mistrust and undermining policing efforts, we suggest 12 design considerations to help ensure the development of high quality/fit for purpose Police/Citizen apps.
Assessment of genetic variation in Sicilian Helichrysum (Asteraceae) and implication to germplasm conservation

Abstract Some taxa of Sicilian Helichrysum related to sect. Stoeachadina have an uncertain systematic position; moreover, they are rare and endangered. In order to preserve these taxa in a genebank, a strategy to keep their genetic variation as high as possible is required. The high informative content of AFLP markers was used to assess genetic variations within and between populations and their relationships. The results show that all populations are genetically similar, even though there are two distinct groups: the first is the population referred to H. pendulum, and the second is represented by other populations belonging to several different specific and intraspecific taxa. The H. pendulum population is homogeneous, and characterised by the lowest genetic variation among all the populations considered. The second group shows a greater variation as evidenced by the presence of three subgroups: (i) populations referred to H. panormitanum var. panormitanum and H. panormitanum var. stramineum, (ii) populations referred to H. errerae var. errerae and H. errerae var. messerii, (iii) a population referred to H. hyblaeum, which, despite morphological and chorological differences, is similar to the other populations of the second group from a genetic stand point. Each population of the above-mentioned taxa has local fragment markers whose frequency is useful to determine how many plants should be sampled in order to collect the seeds necessary to preserve the population's genetic diversity in a genebank.
Generalized SHAP: Generating multiple types of explanations in machine learning

Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley Additive Explanations (SHAP). Our new method - Generalized Shapley Additive Explanations (G-SHAP) - produces many additional types of explanations, including: 1) General classification explanations; Why is this sample more likely to belong to one class rather than another? 2) Intergroup differences; Why do our model's predictions differ between groups of observations? 3) Model failure; Why does our model perform poorly on a given sample? We formally define these types of explanations and illustrate their practical use on real data.
Learning TSP Requires Rethinking Generalization

End-to-end training of neural network solvers for combinatorial problems such as the Travelling Salesman Problem is intractable and inefficient beyond a few hundreds of nodes. While state-of-the-art Machine Learning approaches perform closely to classical solvers for trivially small sizes, they are unable to generalize the learnt policy to larger instances of practical scales. Towards leveraging transfer learning to solve large-scale TSPs, this paper identifies inductive biases, model architectures and learning algorithms that promote generalization to instances larger than those seen in training. Our controlled experiments provide the first principled investigation into such zero-shot generalization, revealing that extrapolating beyond training data requires rethinking the entire neural combinatorial optimization pipeline, from network layers and learning paradigms to evaluation protocols.
Discovery and follow-up of ASASSN-19dj: an X-ray and UV luminous TDE in an extreme post-starburst galaxy

We present observations of ASASSN-19dj, a nearby tidal disruption event (TDE) discovered in the post-starburst galaxy KUG 0810+227 by the All-Sky Automated Survey for Supernovae (ASAS-SN) at a distance of d ≃ 98 Mpc. We observed ASASSN-19dj from −21 to 392 d relative to peak ultraviolet (UV)/optical emission using high-cadence, multiwavelength spectroscopy and photometry. From the ASAS-SN g-band data, we determine that the TDE began to brighten on 2019 February 6.8 and for the first 16 d the rise was consistent with a flux ∝t2 power law. ASASSN-19dj peaked in the UV/optical on 2019 March 6.5 (MJD = 58548.5) at a bolometric luminosity of L = (6.2 ± 0.2) × 1044 erg s−1. Initially remaining roughly constant in X-rays and slowly fading in the UV/optical, the X-ray flux increased by over an order of magnitude ∼225 d after peak, resulting from the expansion of the X-ray emitting region. The late-time X-ray emission is well fitted by a blackbody with an effective radius of ∼1 × 1012 cm and a temperature of ∼6 × 105 K. The X-ray hardness ratio becomes softer after brightening and then returns to a harder state as the X-rays fade. Analysis of Catalina Real-Time Transient Survey images reveals a nuclear outburst roughly 14.5 yr earlier with a smooth decline and a luminosity of LV ≥ 1.4 × 1043 erg s−1, although the nature of the flare is unknown. ASASSN-19dj occurred in the most extreme post-starburst galaxy yet to host a TDE, with Lick HδA = 7.67 ± 0.17 Å.
Faster DBSCAN via subsampled similarity queries

DBSCAN is a popular density-based clustering algorithm. It computes the $\epsilon$-neighborhood graph of a dataset and uses the connected components of the high-degree nodes to decide the clusters. However, the full neighborhood graph may be too costly to compute with a worst-case complexity of $O(n^2)$. In this paper, we propose a simple variant called SNG-DBSCAN, which clusters based on a subsampled $\epsilon$-neighborhood graph, only requires access to similarity queries for pairs of points and in particular avoids any complex data structures which need the embeddings of the data points themselves. The runtime of the procedure is $O(sn^2)$, where $s$ is the sampling rate. We show under some natural theoretical assumptions that $s \approx \log n/n$ is sufficient for statistical cluster recovery guarantees leading to an $O(n\log n)$ complexity. We provide an extensive experimental analysis showing that on large datasets, one can subsample as little as $0.1\%$ of the neighborhood graph, leading to as much as over 200x speedup and 250x reduction in RAM consumption compared to scikit-learn's implementation of DBSCAN, while still maintaining competitive clustering performance.
Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis

Reinforcement learning has shown great promise for synthesizing realistic human behaviors by learning humanoid control policies from motion capture data. However, it is still very challenging to reproduce sophisticated human skills like ballet dance, or to stably imitate long-term human behaviors with complex transitions. The main difficulty lies in the dynamics mismatch between the humanoid model and real humans. That is, motions of real humans may not be physically possible for the humanoid model. To overcome the dynamics mismatch, we propose a novel approach, residual force control (RFC), that augments a humanoid control policy by adding external residual forces into the action space. During training, the RFC-based policy learns to apply residual forces to the humanoid to compensate for the dynamics mismatch and better imitate the reference motion. Experiments on a wide range of dynamic motions demonstrate that our approach outperforms state-of-the-art methods in terms of convergence speed and the quality of learned motions. For the first time, we show a physics-based virtual character performing highly agile ballet dance moves such as pirouette, arabesque and jete. Furthermore, we propose a dual-policy control framework, where a kinematic policy and an RFC-based policy work in tandem to synthesize multi-modal infinite-horizon human motions without any task guidance or user input. Our approach is the first humanoid control method that successfully learns from a large-scale human motion dataset (Human3.6M) and generates diverse long-term motions.
Characterising Authors on the Extent of their Paper Acceptance: A Case Study of the Journal of High Energy Physics

New researchers are usually very curious about the recipe that could accelerate the chances of their paper getting accepted in a reputed forum (journal/conference). In search of such a recipe, we investigate the profile and peer review text of authors whose papers almost always get accepted at a venue (Journal of High Energy Physics in our current work). We find authors with high acceptance rate are likely to have a high number of citations, high h-index, higher number of collaborators etc. We notice that they receive relatively lengthy and positive reviews for their papers. In addition, we also construct three networks -- co-reviewer, co-citation and collaboration network and study the network-centric features and intra- and inter-category edge interactions. We find that the authors with high acceptance rate are more 'central' in these networks; the volume of intra- and inter-category interactions are also drastically different for the authors with high acceptance rate compared to the other authors. Finally, using the above set of features, we train standard machine learning models (random forest, XGBoost) and obtain very high class wise precision and recall. In a followup discussion we also narrate how apart from the author characteristics, the peer-review system might itself have a role in propelling the distinction among the different categories which could lead to potential discrimination and unfairness and calls for further investigation by the system admins.
EMOGI: Efficient Memory-access for Out-of-memory Graph-traversal In GPUs

Modern analytics and recommendation systems are increasingly based on graph data that capture the relations between entities being analyzed. Practical graphs come in huge sizes, offer massive parallelism, and are stored in sparse-matrix formats such as compressed sparse row (CSR). To exploit the massive parallelism, developers are increasingly interested in using GPUs for graph traversal. However, due to their sizes, graphs often do not fit into the GPU memory. Prior works have either used input data pre-processing/partitioning or unified virtual memory (UVM) to migrate chunks of data from the host memory to the GPU memory. However, the large, multi-dimensional, and sparse nature of graph data presents a major challenge to these schemes and results in significant amplification of data movement and reduced effective data throughput. In this work, we propose EMOGI, an alternative approach to traverse graphs that do not fit in GPU memory using direct cache-line-sized access to data stored in host memory. This paper addresses the open question of whether a sufficiently large number of overlapping cache-line-sized accesses can be sustained to 1) tolerate the long latency to host memory, 2) fully utilize the available bandwidth, and 3) achieve favorable execution performance. We analyze the data access patterns of several graph traversal applications in GPU over PCIe using an FPGA to understand the cause of poor external bandwidth utilization. By carefully coalescing and aligning external memory requests, we show that we can minimize the number of PCIe transactions and nearly fully utilize the PCIe bandwidth with direct cache-line accesses to the host memory. EMOGI achieves 2.60X speedup on average compared to the optimized UVM implementations in various graph traversal applications. We also show that EMOGI scales better than a UVM-based solution when the system uses higher bandwidth interconnects such as PCIe 4.0.
Potential Field Guided Actor-Critic Reinforcement Learning

In this paper, we consider the problem of actor-critic reinforcement learning. Firstly, we extend the actor-critic architecture to actor-critic-N architecture by introducing more critics beyond rewards. Secondly, we combine the reward-based critic with a potential-field-based critic to formulate the proposed potential field guided actor-critic reinforcement learning approach (actor-critic-2). This can be seen as a combination of the model-based gradients and the model-free gradients in policy improvement. State with large potential field often contains a strong prior information, such as pointing to the target at a long distance or avoiding collision by the side of an obstacle. In this situation, we should trust potential-field-based critic more as policy evaluation to accelerate policy improvement, where action policy tends to be guided. For example, in practical application, learning to avoid obstacles should be guided rather than learned by trial and error. State with small potential filed is often lack of information, for example, at the local minimum point or around the moving target. At this time, we should trust reward-based critic as policy evaluation more to evaluate the long-term return. In this case, action policy tends to explore. In addition, potential field evaluation can be combined with planning to estimate a better state value function. In this way, reward design can focus more on the final stage of reward, rather than reward shaping or phased reward. Furthermore, potential field evaluation can make up for the lack of communication in multi-agent cooperation problem, i.e., multi-agent each has a reward-based critic and a relative unified potential-field-based critic with prior information. Thirdly, simplified experiments on predator-prey game demonstrate the effectiveness of the proposed approach.
ESAD: Endoscopic Surgeon Action Detection Dataset

In this work, we take aim towards increasing the effectiveness of surgical assistant robots. We intended to make assistant robots safer by making them aware about the actions of surgeon, so it can take appropriate assisting actions. In other words, we aim to solve the problem of surgeon action detection in endoscopic videos. To this, we introduce a challenging dataset for surgeon action detection in real-world endoscopic videos. Action classes are picked based on the feedback of surgeons and annotated by medical professional. Given a video frame, we draw bounding box around surgical tool which is performing action and label it with action label. Finally, we presenta frame-level action detection baseline model based on recent advances in ob-ject detection. Results on our new dataset show that our presented dataset provides enough interesting challenges for future method and it can serveas strong benchmark corresponding research in surgeon action detection in endoscopic videos.