Miracle-dz/UniFAR-FTUs huggingface dataset:
The Big Sibling of AU Mic: A Cold Dust-rich Debris Disk around CP−72 2713 in the β Pic Moving Group

Analyzing Spitzer and Herschel archival measurements we identified a debris disk around the young K7/M0 star CP−72 2713. The system belongs to the 24 Myr old β Pic moving group. Our new 1.33 mm continuum observation, obtained with the Atacama Large Millimeter/submillimeter Array 7 m array, revealed an extended dust disk with a peak radius of 140 au, probably tracing the location of the planetesimal belt in the system. The disk is outstandingly large compared to known spatially resolved debris disks and protoplanetary disks around stars of comparable masses. The dynamical excitation of the belt at this radius is found to be reconcilable with planetary stirring, while self-stirring by large planetesimals embedded in the belt can work only if these bodies form very rapidly, e.g., via pebble concentration. By analyzing the spectral energy distribution, we derived a characteristic dust temperature of 43 K and a fractional luminosity of 1.1 × 10−3. The latter value is prominently high; we know of only four other similarly dust-rich Kuiper Belt analogs within 40 pc of the Sun.
Knowledge Base Completion: Baseline strikes back (Again)

Knowledge Base Completion has been a very active area recently, where multiplicative models have generally outperformed additive and other deep learning methods -- like GNN, CNN, path-based models. Several recent KBC papers propose architectural changes, new training methods, or even a new problem reformulation. They evaluate their methods on standard benchmark datasets - FB15k, FB15k-237, WN18, WN18RR, and Yago3-10. Recently, some papers discussed how 1-N scoring can speed up training and evaluation. In this paper, we discuss how by just applying this training regime to a basic model like Complex gives near SOTA performance on all the datasets -- we call this model COMPLEX-V2. We also highlight how various multiplicative methods recently proposed in literature benefit from this trick and become indistinguishable in terms of performance on most datasets. This paper calls for a reassessment of their individual value, in light of these findings.
Quantifying Attention Flow in Transformers

In the Transformer model, “self-attention” combines information from attended embeddings into the representation of the focal embedding in the next layer. Thus, across layers of the Transformer, information originating from different tokens gets increasingly mixed. This makes attention weights unreliable as explanations probes. In this paper, we consider the problem of quantifying this flow of information through self-attention. We propose two methods for approximating the attention to input tokens given attention weights, attention rollout and attention flow, as post hoc methods when we use attention weights as the relative relevance of the input tokens. We show that these methods give complementary views on the flow of information, and compared to raw attention, both yield higher correlations with importance scores of input tokens obtained using an ablation method and input gradients.
Lyapunov Function PDEs Method to the Stability of Some Chemical Reaction Networks

This paper contributes to extending the validity of Lyapunov function PDEs (in-vented by Fang and Gao in [SIAM Journal on Applied Dynamical Systems, 18(2019), pp. 1163-1199]and whose solution is conjectured to be able to behave as a Lyapunov function) in stability analysisto more mass-action chemical reaction networks. By defining a new class of networks, called complexbalanced produced networks, we have proved that the Lyapunov function PDEs method is validin capturing the asymptotic stability of this class of networks, and also to their compound withany 1-dimensional independent network according to species and with any two-species autocatalyticnon-independent network if some moderate conditions are included. A notable point is that thesethree classes of networks are non-weakly reversible, any dimensional and of any deficiency. We applyour results to some practical biochemical reaction networks including birth-death processes, motifsrelated networks etc., to illustrate validity.
Probing the large scale structure using gravitational-wave observations of binary black holes

Third generation gravitational-wave (GW) detectors are expected to detect a large number of binary black holes (BBHs) to large redshifts, opening up an independent probe of the large scale structure using their clustering. This probe will be complementary to the probes using galaxy clustering -- GW events could be observed up to very large redshifts ($z \sim 10$) although the source localization will be much poorer at large distances ($\sim$ tens of square degrees). We explore the possibility of probing the large scale structure from the spatial distribution of the observed BBH population, using their two-point (auto)correlation function. We find that we can estimate the bias factor of population of BBH (up to $z \sim 1$) with a few years of observations with these detectors. Our method relies solely on the source-location posteriors obtained the GW events and does not require any information from electromagnetic observations. This will help in identifying the type of galaxies that host the BBH population, thus shedding light on their origins.
Robustness to Modification with Shared Words in Paraphrase Identification

Revealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we study the robustness of paraphrase identification models from a new perspective – via modification with shared words, and we show that the models have significant robustness issues when facing such modifications. To modify an example consisting of a sentence pair, we either replace some words shared by both sentences or introduce new shared words. We aim to construct a valid new example such that a target model makes a wrong prediction. To find a modification solution, we use beam search constrained by heuristic rules, and we leverage a BERT masked language model for generating substitution words compatible with the context. Experiments show that the performance of the target models has a dramatic drop on the modified examples, thereby revealing the robustness issue. We also show that adversarial training can mitigate this issue.
Simulations of stellar winds from X-ray bursts

Context. Photospheric radius expansion during X-ray bursts can be used to measure neutron star radii and help constrain the equation of state of neutron star matter. Understanding the stellar wind dynamics is important for interpreting observations, and therefore stellar wind models, though studied in past decades, have regained interest and need to be revisited with updated data and methods. Aims. Here, we study the radiative wind model in the context of X-ray bursts with modern techniques and physics input. We focus on characterization of the solutions and the study of observable magnitudes as a function of free model parameters. Methods. We implemented a spherically symmetric nonrelativistic wind model in a stationary regime, with updated opacity tables and modern numerical techniques. Total mass and energy outflows (Ṁ, Ė) were treated as free parameters. Results. A high-resolution parameter-space exploration was performed to allow better characterization of observable magnitudes. High correlation was found between different photospheric magnitudes and free parameters. For instance, the photospheric ratio of gravitational energy outflow to radiative luminosity is directly proportional to the photospheric wind velocity. Conclusions. The correlations found here could help determine the physical conditions of the inner layers, where nuclear reactions take place, by means of observable photospheric values. Further studies are needed to determine the range of physical conditions in which the correlations are valid.
On the Weyl Problem in Minkowski Space

Let $S$ be a closed surface of hyperbolic type. We show that, for every pair $(g_+,g_-)$ of negatively curved metrics over $S$, there exists a unique globally hyperbolic, maximal, and Cauchy compact Minkowski spacetime $X$ into which $(S,g_+)$ and $(S,g_-)$ isometrically embed as Cauchy surfaces in the future and past components, respectively.
VERTICILLIUM WILT OF POTATO IN RELATION TO VASCULAR INFECTION OF THE TUBER

Several potato varieties, differing in their susceptibility to Verticillium albo-atrum Reinke & Berthold, differed similarly in the degree to which the fungus invaded the vascular tissue of stems and tubers. Surface-sterilized, internally infected seed pieces consistently gave rise to a lower incidence of wilt than healthy seed pieces of the same variety carrying surface-borne inoculum only. In all the varieties tested, a preponderance of tubers produced by wilted plants were apparently free from vascular infection, and those that were infected more often than not gave rise to wilt-free plants.
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles

While feedback loops are known to play important roles in many complex systems, their existence is ignored in a large part of the causal discovery literature, as systems are typically assumed to be acyclic from the outset. When applying causal discovery algorithms designed for the acyclic setting on data generated by a system that involves feedback, one would not expect to obtain correct results. In this work, we show that---surprisingly---the output of the Fast Causal Inference (FCI) algorithm is correct if it is applied to observational data generated by a system that involves feedback. More specifically, we prove that for observational data generated by a simple and $\sigma$-faithful Structural Causal Model (SCM), FCI is sound and complete, and can be used to consistently estimate (i) the presence and absence of causal relations, (ii) the presence and absence of direct causal relations, (iii) the absence of confounders, and (iv) the absence of specific cycles in the causal graph of the SCM. We extend these results to constraint-based causal discovery algorithms that exploit certain forms of background knowledge, including the causally sufficient setting (e.g., the PC algorithm) and the Joint Causal Inference setting (e.g., the FCI-JCI algorithm).
Expertise Style Transfer: A New Task Towards Better Communication between Experts and Laymen

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only simplifies the professional language, but also improves the accuracy and expertise level of laymen descriptions using simple words. This is a challenging task, unaddressed in previous work, as it requires the models to have expert intelligence in order to modify text with a deep understanding of domain knowledge and structures. We establish the benchmark performance of five state-of-the-art models for style transfer and text simplification. The results demonstrate a significant gap between machine and human performance. We also discuss the challenges of automatic evaluation, to provide insights into future research directions. The dataset is publicly available at https://srhthu.github. io/expertise-style-transfer/.
When BERT Plays the Lottery, All Tickets Are Winning

Much of the recent success in NLP is due to the large Transformer-based models such as BERT (Devlin et al, 2019). However, these models have been shown to be reducible to a smaller number of self-attention heads and layers. We consider this phenomenon from the perspective of the lottery ticket hypothesis. For fine-tuned BERT, we show that (a) it is possible to find a subnetwork of elements that achieves performance comparable with that of the full model, and (b) similarly-sized subnetworks sampled from the rest of the model perform worse. However, the "bad" subnetworks can be fine-tuned separately to achieve only slightly worse performance than the "good" ones, indicating that most weights in the pre-trained BERT are potentially useful. We also show that the "good" subnetworks vary considerably across GLUE tasks, opening up the possibilities to learn what knowledge BERT actually uses at inference time.
Abstract Formulation of the Miura Transform

Miura transform is known as the transformation between Korweg de-Vries equation and modified Korweg de-Vries equation. Its formal similarity to the Cole-Hopf transform has been noticed. This fact sheds light on the logarithmic type transformations as an origin of a certain kind of nonlinearity in the soliton equations. In this article, based on the logarithmic representation of operators in infinite-dimensional Banach spaces, a structure common to both Miura and Cole-Hopf transforms is discussed. In conclusion, the Miura transform is generalized as the transform in abstract Banach spaces, and it is applied to the higher order abstract evolution equations.
Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compositionality: they achieve high accuracy when applied to problems in language and vision, while reflecting the compositional structure of the problem in the network architecture. However, prior work implicitly assumed that the structure of the network modules, describing the abstract reasoning process, provides a faithful explanation of the model’s reasoning; that is, that all modules perform their intended behaviour. In this work, we propose and conduct a systematic evaluation of the intermediate outputs of NMNs on NLVR2 and DROP, two datasets which require composing multiple reasoning steps. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour. To remedy that, we train the model with auxiliary supervision and propose particular choices for module architecture that yield much better faithfulness, at a minimal cost to accuracy.
Transformer-based End-to-End Question Generation

Question Generation (QG) is an important task in Natural Language Processing (NLP) that involves generating questions automatically when given a context paragraph. While many techniques exist for the task of QG, they employ complex model architectures, extensive features, and additional mechanisms to boost model performance. In this work, we show that transformer-based finetuning techniques can be used to create robust question generation systems using only a single pretrained language model, without the use of additional mechanisms, answer metadata, and extensive features. Our best model outperforms previous more complex RNN-based Seq2Seq models, with an 8.62 and a 14.27 increase in METEOR and ROUGE_L scores, respectively. We show that it also performs on par with Seq2Seq models that employ answer-awareness and other special mechanisms, despite being only a single-model system. We analyze how various factors affect the model's performance, such as input data formatting, the length of the context paragraphs, and the use of answer-awareness. In addition, we also look into the modes of failure that the model experiences and identify the reasons why it fails.
Semi-supervised semantic segmentation needs strong, varied perturbations

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems. Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success. We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success. We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions. We find that adapted variants of the recently proposed CutOut and CutMix augmentation techniques yield state-of-the-art semi-supervised semantic segmentation results in standard datasets. Furthermore, given its challenging nature we propose that semantic segmentation acts as an effective acid test for evaluating semi-supervised regularizers. Implementation at: this https URL.
Response-adaptive randomization in clinical trials: from myths to practical considerations

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930’s and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
Systematic Status of Wild Canis in North-Central Texas

Abstract Skulls of wild Canis collected 2003–2004 in north-central Texas are morphometrically similar to a series taken there and in nearby areas in 1964–1971, which was considered to represent a population of Coyotes (C. latrans) modified through introgression from Red Wolves (C. rufus). A few of the new specimens closely resemble small examples of Red Wolves. Such affinity is supported by authoritative examination of living and videotaped animals. The persistence of influence of Red Wolves, long after presumed extirpation through hybridization and human persecution, may be relevant to wolf conservation.
High-Dimensional Robust Mean Estimation via Gradient Descent

We study the problem of high-dimensional robust mean estimation in the presence of a constant fraction of adversarial outliers. A recent line of work has provided sophisticated polynomial-time algorithms for this problem with dimension-independent error guarantees for a range of natural distribution families. In this work, we show that a natural non-convex formulation of the problem can be solved directly by gradient descent. Our approach leverages a novel structural lemma, roughly showing that any approximate stationary point of our non-convex objective gives a near-optimal solution to the underlying robust estimation task. Our work establishes an intriguing connection between algorithmic high-dimensional robust statistics and non-convex optimization, which may have broader applications to other robust estimation tasks.
Complex social contagion induces bistability on multiplex networks

Exploring complex social contagion, especially the social reinforcement dynamics when exposing to multiple sources, is of vital importance for understanding the spread of complicated collective behaviors nowadays. While previous works exclusively focus on single-layer networks, the detailed impacts of multilayer reinforcement which characterizes the promoting effects among multiple social circles remain largely unknown. To this end, we incorporate multilayer reinforcement into ignorant-spreader-ignorant (SIS) model on multiplex networks. Our theoretical analysis combines pairwise method and mean-field theory and agrees well with large-scale simulations. Surprisingly, we find this complex social contagion mechanism triggers the emergence of bistability phenomena, where extinction and outbreak states coexist. Further, we show that the final state of bistable regions depends on the initial density of adopters, the critical value of which decreases as the contagion transmissibility or the multilayer reinforcement increases. In particular, we highlight two possible conditions for the outbreak of social contagion: to possess large contagion transmissibility, or to possess large initial density of adopters with strong multilayer reinforcement. Our results show the powerful and non-negligible impacts of complex dynamical mechanisms, which provides valuable insights toward the spreading behaviors in the digital age.
A comparison of the flora and arthropod fauna of organically and conventionally grown winter wheat in southern England

Summary Fields of winter wheat on organic and conventional farms were sampled for weeds and invertebrates in June/July in 1990 and 1991. Organic fields were paired with adjoining conventional ones and their invertebrate fauna compared with a D-vac vacuum suction sampler. Weed counts showed greater percentage cover of broad-leaved weeds in organic fields than in conventional ones, with three times as many species present where herbicides were not used. Significantly higher densities of nematoceran and acalypteran Diptera, Hem-iptera (especially aphids), aphid-specific predators, parasitic Hymenoptera and cryptophagid and cantharid Coleoptera were found in conventionally grown fields. Significantly higher densities of weevils, spiders, springtails, plant hoppers and sawfly larvae were found in organic fields. Reasons for these differences or the lack of them are discussed.
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees

Systems based on artificial intelligence and machine learning models should be transparent, in the sense of being capable of explaining their decisions to gain humans' approval and trust. While there are a number of explainability techniques that can be used to this end, many of them are only capable of outputting a single one-size-fits-all explanation that simply cannot address all of the explainees' diverse needs. In this work we introduce a model-agnostic and post-hoc local explainability technique for black-box predictions called LIMEtree, which employs surrogate multi-output regression trees. We validate our algorithm on a deep neural network trained for object detection in images and compare it against Local Interpretable Model-agnostic Explanations (LIME). Our method comes with local fidelity guarantees and can produce a range of diverse explanation types, including contrastive and counterfactual explanations praised in the literature. Some of these explanations can be interactively personalised to create bespoke, meaningful and actionable insights into the model's behaviour. While other methods may give an illusion of customisability by wrapping, otherwise static, explanations in an interactive interface, our explanations are truly interactive, in the sense of allowing the user to "interrogate" a black-box model. LIMEtree can therefore produce consistent explanations on which an interactive exploratory process can be built.