Miracle-dz/UniFAR-FTUs huggingface dataset:
Spin Susceptibility in Neutron Matter from Quantum Monte Carlo Calculations

The spin susceptibility in pure neutron matter is computed from auxiliary field diffusion Monte Carlo calculations over a wide range of densities. The calculations are performed for different spin asymmetries, while using twist-averaged boundary conditions to reduce finite-size effects. The employed nuclear interactions include both the phenomenological Argonne AV8′ + UIX potential and local interactions that are derived from chiral effective field theory up to next-to-next-to-leading order.
Ada3D: Real-Time 3D Reconstruction with Compression Rate Adaptation

Augmented reality, so called AR, is recently the subject of attention by researchers from different fields and is developing rapidly. However, some attractive AR applications do not have mature technical basis yet, such as holographic conference and telesurgery. In these applications, 3D reconstruction of the target objects and real-time transmission of the reconstructed result are needed. It is challenging to build an integrated real-time 3D reconstruction system and effectively reduce the transmission delay within the system. In this paper, we propose Ada3D, a practical cross-platform system for real-time 3D reconstruction using multiple RGB-D sensors. It consists of several sensory clients and an edge server, which are responsible for data acquisition and data fusion, respectively. In addition, a compression-based method is applied in the system, which enables the client to automatically select an appropriate data compression level according to the current system state, including network status and device capability. Experiment results show that the method outperforms the common case by over 10% in delay saving.1
Robust Hierarchical Planning with Policy Delegation

We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation. This framework, the Markov Intent Process, features a collection of skills which are each specialised to perform a single task well. Skills are aware of their intended effects and are able to analyse planning goals to delegate planning to the best-suited skill. This principle dynamically creates a hierarchy of plans, in which each skill plans for sub-goals for which it is specialised. The proposed planning method features on-demand execution---skill policies are only evaluated when needed. Plans are only generated at the highest level, then expanded and optimised when the latest state information is available. The high-level plan retains the initial planning intent and previously computed skills, effectively reducing the computation needed to adapt to environmental changes. We show this planning approach is experimentally very competitive to classic planning and reinforcement learning techniques on a variety of domains, both in terms of solution length and planning time.
Correspondence Learning via Linearly-invariant Embedding

In this paper, we propose a fully differentiable pipeline for estimating accurate dense correspondences between 3D point clouds. The proposed pipeline is an extension and a generalization of the functional maps framework. However, instead of using the Laplace-Beltrami eigenfunctions as done in virtually all previous works in this domain, we demonstrate that learning the basis from data can both improve robustness and lead to better accuracy in challenging settings. We interpret the basis as a learned embedding into a higher dimensional space. Following the functional map paradigm the optimal transformation in this embedding space must be linear and we propose a separate architecture aimed at estimating the transformation by learning optimal descriptor functions. This leads to the first end-to-end trainable functional map-based correspondence approach in which both the basis and the descriptors are learned from data. Interestingly, we also observe that learning a \emph{canonical} embedding leads to worse results, suggesting that leaving an extra linear degree of freedom to the embedding network gives it more robustness, thereby also shedding light onto the success of previous methods. Finally, we demonstrate that our approach achieves state-of-the-art results in challenging non-rigid 3D point cloud correspondence applications.
Improving Noise Robustness of an End-to-End Neural Model for Automatic Speech Recognition

We present our experiments in training robust to noise an end-to-end automatic speech recognition (ASR) model using intensive data augmentation. We explore the efficacy of fine-tuning a pre-trained model to improve noise robustness, and we find it to be a very efficient way to train for various noisy conditions, especially when the conditions in which the model will be used, are unknown. Starting with a model trained on clean data helps establish baseline performance on clean speech. We carefully fine-tune this model to both maintain the performance on clean speech, and improve the model accuracy in noisy conditions. With this schema, we trained robust to noise English and Mandarin ASR models on large public corpora. All described models and training recipes are open sourced in NeMo, a toolkit for conversational AI.
Robust Correction of Sampling Bias Using Cumulative Distribution Functions

Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this often rely on estimating the ratio of training and target probability density functions. These techniques require parameter tuning and can be unstable across different datasets. We present a new method for handling covariate shift using the empirical cumulative distribution function estimates of the target distribution by a rigorous generalization of a recent idea proposed by Vapnik and Izmailov. Further, we show experimentally that our method is more robust in its predictions, is not reliant on parameter tuning and shows similar classification performance compared to the current state-of-the-art techniques on synthetic and real datasets.
Load balancing policies with server-side cancellation of replicas

Popular dispatching policies such as the join shortest queue (JSQ), join smallest work (JSW) and their power of two variants are used in load balancing systems where the instantaneous queue length or workload information at all queues or a subset of them can be queried. In situations where the dispatcher has an associated memory, one can minimize this query overhead by maintaining a list of idle servers to which jobs can be dispatched. Recent alternative approaches that do not require querying such information include the cancel on start and cancel on complete based replication policies. The downside of such policies however is that the servers must communicate the start or completion of each service to the dispatcher and must allow cancellation of redundant copies. In this work, we consider a load balancing environment where the dispatcher cannot query load information, does not have a memory, and cannot cancel any replica that it may have created. In such a rigid environment, we allow the dispatcher to possibly append a server side cancellation criteria to each job or its replica. A job or a replica is served only if it satisfies the predefined criteria at the time of service. We focus on a criteria that is based on the waiting time experienced by a job or its replica and analyze several variants of this policy based on the assumption of asymptotic independence of queues. The proposed policies are novel and perform remarkably well in spite of the rigid operating constraints.
Drift of random walks on abelian covers of finite volume homogeneous spaces

Let $G$ be a connected simple real Lie group, $\Lambda_{0}\subseteq G$ a lattice and $\Lambda \unlhd \Lambda_{0}$ a normal subgroup such that $\Lambda_{0}/\Lambda\simeq \mathbb{Z}^d$. We study the drift of a random walk on the $\mathbb{Z}^d$-cover $G/\Lambda$ of the finite volume homogeneous space $G/\Lambda_{0}$. This walk is defined by a Zariski-dense compactly supported probability measure $\mu$ on $G$. We first assume the covering map $G/\Lambda\rightarrow G/\Lambda_{0}$ has a fundamental domain with compact boundary and compute the drift at $every$ starting point. Then we remove this assumption and describe the drift almost everywhere on abelian covers of hyperbolic manifolds. The dimension 2 stands out with non-converging type behaviors related to cusps on the basis that are unfolded in the cover. The recurrence of the trajectories is also characterized.
Patient-Derived Tumor Xenograft Models: Toward the Establishment of Precision Cancer Medicine

Patient-derived xenografts (PDXs) describe models involving the implantation of patient-derived tumor tissue into immunodeficient mice. Compared with conventional preclinical models involving the implantation of cancer cell lines into mice, PDXs can be characterized by the preservation of tumor heterogeneity, and the tumor microenvironment (including stroma/vasculature) more closely resembles that in patients. Consequently, the use of PDX models has improved the predictability of clinical therapeutic responses to 80% or greater, compared with approximately 5% for existing models. In the future, molecular biological analyses, omics analyses, and other experiments will be conducted using recently prepared PDX models under the strong expectation that the analysis of cancer pathophysiology, stem cells, and novel treatment targets and biomarkers will be improved, thereby promoting drug development. This review outlines the methods for preparing PDX models, advances in cancer research using PDX mice, and perspectives for the establishment of precision cancer medicine within the framework of personalized cancer medicine.
The influence of light of different wavelengths on laying hen production and egg quality

SUMMARY Worldwide, commercial laying hens are frequently being used for only one production cycle mainly due to a decline in egg quality and/or egg quantity at 70–80 weeks of age. Extending the production cycle of these hens by improving or maintaining their egg quality and laying persistency could lead to both economic and environmental benefits as well as a more sustainable egg industry. Laying performance of the modern-day laying hen is highly dependent upon the light source used during housing and artificial lighting programmes have been used for years to control egg production. Birds are able to perceive coloured light (400–700 nm) as well as the ultraviolet (UV) part of the spectrum (100–400 nm) due to the presence of an extraretinal cone in the eye. Studies have shown that red light, due to its longer wavelength of 660 nm, is able to stimulate the hypothalamus and elicit a photosexual response in all animals including birds. This can result in increased egg production in comparison to green, blue or white light exposure, although responses may vary depending on bird strain and the intensity of the light used. UV light has a shorter wavelength (100–400 nm) than visible light (400–700 nm) and so its effects on egg production are likely to be weak to non-existent. The benefits of UV light are mediated through its ability to activate cholecalciferol from 7-dehydroxycholesterol in the skin resulting in improvements in eggshell quality. These improvements in shell quality are most likely to occur in birds that are exposed to (natural) daylight, as UV light can compensate to some extent, the lack of vitamin D in poultry diets. This review highlights the variable effects of light of different wavelengths and intensity on laying hen production, egg quality and its potential to extend the end of lay in commercial hens.
A misunderstood measure in screening: the positive predictive value

ABSTRACT Background: The misunderstanding of numbers and probabilities is a problem that runs across practically the entire population. This problem concerns both physicians, nurses, and patients, affecting medical decisions and informed consent. Notably, this problem hurts the proper interpretation of a positive mammogram, which covers the computation of the probability of a woman having breast cancer under a positive test, called the positive predictive value. Method: In this study, one ascertains the literacy of health professionals and the general population concerning the knowledge and computation of the positive predictive value under a positive mammogram. This knowledge is required to enable women with the necessary information to make an informed decision before and after screening. The consequences of a lack of this knowledge run from psychological distress to more harmful situations such as overdiagnosis and overtreatment. The information was collected through a questionnaire available online from 02/01/2019 to 12/04/2019. Results: Most physicians overestimated the actual probability of a woman having breast cancer, with the majority equating this value with the sensitivity of the test. They also had the lowest rate of correct answers. A little less than one-third of nurses matched the positive predictive value with the prevalence, and, unexpectedly, the general population had the highest percentage of correct answers. Conclusions: These results point to the need for the reinforcement of statistical education among all participants involved, especially health professionals.
Factors affecting Zantedeschia Spreng. dedifferentiation in vitro

Zantedeschia Spreng. is an economically important ornamental plant species. Improvement of aesthetic parameters and creation of novel variations of decorative plants are important economic goals for the commercial ornamental industry. The effect of the combination of growth regulators and the explant type on dedifferentiation induction in Zantedeschia aethiopica and Zantedeschia elliottianna was investigated. Research was carried out at the Institute of Biology and Plant Biotechnology of Aleksandras Stulginskis University and at the Laboratory of Agrobiotechnology of the Joint Research Centre in 2016–2017. Explants (leaf discs, spathe discs, and petiole segments) were cultured on the MS medium supplemented with different concentrations of indole-3-acetic acid (IAA), indole-3-butyric acid (IBA) and 6-benzylaminopurine (BAP), 30.0 g l –1 sucrose, and 8.0 g l –1 agar. Leaf discs and spathe discs did not show any response in the medium without growth regulators, while 16.3% ( Zantedeschia aethiopica ) and 24.3% ( Zantedeschia elliottianna ) of isolated petiole segments formed callus. Our results showed that an appropriate combination of growth regulators for callus induction varied depending on the genetic background and the explant type. It was documented that isolated petiole segments of the arum lily and the calla lily induced more statistically reliable callus in a medium supplemented with combination BAP + IAA, while the combination BAP + IBA promoted callus formation from spathe disc tissues. Petiole segments manifested the highest dedifferentiation capability among the tested explant types. The results of the study showed that the ability of the somatic tissues of the arum lily and the calla lily to induce dedifferentiation seems to be a valuable material for improvement of ornamental values in these plants.
Irregular collective dynamics in a Kuramoto–Daido system

We analyze the collective behavior of a mean-field model of phase-oscillators of Kuramoto–Daido type coupled through pairwise interactions which depend on phase differences: the coupling function is composed of three harmonics. We provide convincing evidence of a transient but long-lasting chaotic collective chaos, which persists in the thermodynamic limit. The regime is analyzed with the help of clever direct numerical simulations, by determining the maximum Lyapunov exponent and assessing the transversal stability to the self-consistent mean field. The structure of the invariant measure is finally described in terms of a resolution-dependent entropy.
Intersection types and (positive) almost-sure termination

Randomized higher-order computation can be seen as being captured by a λ-calculus endowed with a single algebraic operation, namely a construct for binary probabilistic choice. What matters about such computations is the probability of obtaining any given result, rather than the possibility or the necessity of obtaining it, like in (non)deterministic computation. Termination, arguably the simplest kind of reachability problem, can be spelled out in at least two ways, depending on whether it talks about the probability of convergence or about the expected evaluation time, the second one providing a stronger guarantee. In this paper, we show that intersection types are capable of precisely characterizing both notions of termination inside a single system of types: the probability of convergence of any λ-term can be underapproximated by its type, while the underlying derivation’s weight gives a lower bound to the term’s expected number of steps to normal form. Noticeably, both approximations are tight—not only soundness but also completeness holds. The crucial ingredient is non-idempotency, without which it would be impossible to reason on the expected number of reduction steps which are necessary to completely evaluate any term. Besides, the kind of approximation we obtain is proved to be optimal recursion theoretically: no recursively enumerable formal system can do better than that.
Modularity Improves Out-of-Domain Instruction Following

We propose a modular architecture for following natural language instructions that describe sequences of diverse subgoals, such as navigating to landmarks or picking up objects. Standard, non-modular, architectures used in instruction following do not exploit subgoal compositionality and often struggle on out-of-distribution tasks and environments. In our approach, subgoal modules each carry out natural language instructions for a specific subgoal type. A sequence of modules to execute is chosen by learning to segment the instructions and predicting a subgoal type for each segment. When compared to standard sequence-to-sequence approaches on ALFRED, a challenging instruction following benchmark, we find that modularization improves generalization to environments unseen in training and to novel tasks.
"Nice Try, Kiddo": Ad Hominems in Dialogue Systems

Ad hominem attacks are those that attack some feature of a person's character instead of the position the person is maintaining. As a form of toxic and abusive language, ad hominems contain harmful language that could further amplify the skew of power inequality for marginalized populations. Since dialogue systems are designed to respond directly to user input, it is important to study ad hominems in these system responses. In this work, we propose categories of ad hominems that allow us to analyze human and dialogue system responses to Twitter posts. We specifically compare responses to Twitter posts about marginalized communities (#BlackLivesMatter, #MeToo) and other topics (#Vegan, #WFH). Furthermore, we propose a constrained decoding technique that uses salient $n$-gram similarity to apply soft constraints to top-$k$ sampling and can decrease the amount of ad hominems generated by dialogue systems. Our results indicate that 1) responses composed by both humans and DialoGPT contain more ad hominems for discussions around marginalized communities versus other topics, 2) different amounts of ad hominems in the training data can influence the likelihood of the model generating ad hominems, and 3) we can thus carefully choose training data and use constrained decoding techniques to decrease the amount of ad hominems generated by dialogue systems.
Exploring task-based query expansion at the TREC-COVID track

We explore how to generate effective queries based on search tasks. Our approach has three main steps: 1) identify search tasks based on research goals, 2) manually classify search queries according to those tasks, and 3) compare three methods to improve search rankings based on the task context. The most promising approach is based on expanding the user's query terms using task terms, which slightly improved the NDCG@20 scores over a BM25 baseline. Further improvements might be gained if we can identify more specific search tasks.
ReadOnce Transformers: Reusable Representations of Text for Transformers

We present ReadOnce Transformers, an approach to convert a transformer-based model into one that can build an information-capturing, task-independent, and compressed representation of text. The resulting representation is reusable across different examples and tasks, thereby requiring a document shared across many examples or tasks to only be read once. This leads to faster training and evaluation of models. Additionally, we extend standard text-to-text transformer models to Representation+Text-to-text models, and evaluate on multiple downstream tasks: multi-hop QA, abstractive QA, and long-document summarization. Our one-time computed representation results in a 2x-5x speedup compared to standard text-to-text models, while the compression also allows existing language models to handle longer documents without the need for designing new pre-trained models.
PoliWAM: An Exploration of a Large Scale Corpus of Political Discussions on WhatsApp Messenger

WhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. Its widespread usage has made it one of the most popular media for information propagation among the masses during any socially engaging event. In the recent past, several countries have witnessed its effectiveness and influence in political and social campaigns. We observe a high surge in information and propaganda flow during election campaigning. In this paper, we explore a high-quality large-scale user-generated dataset curated from WhatsApp comprising of 281 groups, 31,078 unique users, and 223,404 messages shared before, during, and after the Indian General Elections 2019, encompassing all major Indian political parties and leaders. In addition to the raw noisy user-generated data, we present a fine-grained annotated dataset of 3,848 messages that will be useful to understand the various dimensions of WhatsApp political campaigning. We present several complementary insights into the investigative and sensational news stories from the same period. Exploratory data analysis and experiments showcase several exciting results and future research opportunities. To facilitate reproducible research, we make the anonymized datasets available in the public domain.
Castelnuovo polytopes

It is known that the sectional genus of a polarized variety has an upper bound, which is an extension of the Castelnuovo bound on the genus of a projective curve. Polarized varieties whose sectional genus achieves this bound are called Castelnuovo. On the other hand, a lattice polytope is called Castelnuovo if the associated polarized toric variety is Castelnuovo. Kawaguchi characterized Castelnuovo polytopes having interior lattice points in terms of their h∗-vectors. In the present paper, we give a characterization of all Castelnuovo polytopes in terms of their h∗-vectors. Finally, as an application of our characterization, we give a necessary condition for IDP polytopes, which is a generalization of a result proven by Katthän and Yanagawa.