Miracle-dz/UniFAR-FTUs huggingface dataset:
Bridging the Gap between Conversational Reasoning and Interactive Recommendation

There have been growing interests in building a conversational recommender system, where the system simultaneously interacts with the user and explores the user's preference throughout conversational interactions. Recommendation and conversation were usually treated as two separate modules with limited information exchange in existing works, which hinders the capability of both systems: (1) dialog merely incorporated recommendation entities without being guided by an explicit recommendation-oriented policy; (2) recommendation utilized dialog only as a form of interaction instead of improving recommendation effectively. To address the above issues, we propose a novel recommender dialog model: CR-Walker. In order to view the two separate systems within a unified framework, we seek high-level mapping between hierarchical dialog acts and multi-hop knowledge graph reasoning. The model walks on a large-scale knowledge graph to form a reasoning tree at each turn, then mapped to dialog acts to guide response generation. With such a mapping mechanism as a bridge between recommendation and conversation, our framework maximizes the mutual benefit between two systems: dialog as an enhancement to recommendation quality and explainability, recommendation as a goal and enrichment to dialog semantics. Quantitative evaluation shows that our model excels in conversation informativeness and recommendation effectiveness, at the same time explainable on the policy level.
Conditions on the regularity of balanced $c$-partite tournaments for the existence of strong subtournaments with high minimum degree

We consider the following problem posed by Volkmann in 2007: How close to regular must a c-partite tournament be, to secure a strongly connected subtournament of order $c$? We give sufficient conditions on the regularity of balanced $c$-partite tournaments to assure the existence of strong maximal subtournament with minimum degree at least $\left\lfloor \frac{c-2}{4}\right\rfloor+1$. We obtain this result as an application of counting the number of subtournaments of order $c$ for which a vertex has minimum out-degree (resp. in-degree) at most $q\geq 0$.
Rodent community dynamics as mediated by environment and competition in the San Joaquin Desert

We studied a rodent community in the San Joaquin Desert of California, United States, from 1993 to 2016. Using biannual trapping on a 144-trap plot, we found that mice of various species were rarely caught in appreciable numbers in contrast to many other studies of rodent communities in arid North America. Most captures were made of 3 species of kangaroo rats: giant kangaroo rat (Dipodomys ingens), Heermann's kangaroo rat (D. heermanni), and San Joaquin kangaroo rat (D. nitratoides). The site was dominated by D. ingens, which, in many years, was the only kangaroo rat captured. Precipitation varied extremely across the study period with 2 periods of high rainfall and 2 periods of well-below-average rainfall. The rodent community seems to have been responding to both bottom-up control and the competitive dominance of D. ingens. In particular, our trapping showed 2 complete cycles of abundance of D. ingens, from very high numbers to crashes with either no or only 2 individuals caught during a session. In both cases, the species recovered after these crashes in abundance. Although we found some D. ingens living to 4–5 years, and D. heermanni and D. nitratoides living up to 3 years, annual survivorship estimates were low compared with other studies of kangaroo rats. Other studies, however, were of much shorter duration and therefore may have missed eventual downturns in population abundances. Dipodomys ingens is a protected species, and from a conservation standpoint, our data indicate that populations can be highly resilient even in the face of high environmental variability.
Quantification of Ebola virus replication kinetics in vitro

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.
Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages

Spelling normalization for low resource languages is a challenging task because the patterns are hard to predict and large corpora are usually required to collect enough examples. This work shows a comparison of a neural model and character language models with varying amounts on target language data. Our usage scenario is interactive correction with nearly zero amounts of training examples, improving models as more data is collected, for example within a chat app. Such models are designed to be incrementally improved as feedback is given from users. In this work, we design a knowledge-base and prediction model embedded system for spelling correction in low-resource languages. Experimental results on multiple languages show that the model could become effective with a small amount of data. We perform experiments on both natural and synthetic data, as well as on data from two endangered languages (Ainu and Griko). Last, we built a prototype system that was used for a small case study on Hinglish, which further demonstrated the suitability of our approach in real world scenarios.
Automatic Multitrack Mixing With A Differentiable Mixing Console Of Neural Audio Effects

Applications of deep learning to automatic multitrack mixing are largely unexplored. This is partly due to the limited available data, coupled with the fact that such data is relatively unstructured and variable. To address these challenges, we propose a domain-inspired model with a strong inductive bias for the mixing task. We achieve this with the application of pre-trained sub-networks and weight sharing, as well as with a sum/difference stereo loss function. The proposed model can be trained with a limited number of examples, is permutation invariant with respect to the input ordering, and places no limit on the number of input sources. Furthermore, it produces human-readable mixing parameters, allowing users to manually adjust or refine the generated mix. Results from a perceptual evaluation involving audio engineers indicate that our approach generates mixes that outperform baseline approaches. To the best of our knowledge, this work demonstrates the first approach in learning multitrack mixing conventions from real-world data at the waveform level, without knowledge of the underlying mixing parameters.
Understanding YouTube Communities via Subscription-based Channel Embeddings

YouTube is an important source of news and entertainment worldwide, but the scale makes it challenging to study the ideas and topics being discussed on the platform. This paper presents new methods to discover and classify YouTube channels which enable the analysis of communities and categories on the platform using orders of magnitude more channels than have been used in previous studies. Instead of using channel and video data as features for classification as other researchers have, these methods use a self-supervised learning approach that leverages the public subscription pages of commenters. We test the classification method on the task of predicting the political lean of YouTube news channels and find that it outperforms the previous best model on the task. Further experiments also show that there are important advantages to using commenter subscriptions to discover channels. The subscription data, along with an iterative approach, is applied to discover, to our current understanding, the most comprehensive set of English language socio-political YouTube channels yet to be analyzed. We experiment with predicting more fine grained political tags for channels using a previously annotated dataset and find that our model performs better than the average individual human reviewer for most of the top tags. This fine grained political tag model is then applied to the newly discovered English language socio-political channels to create a new dataset to analyze the amount of traffic going to different political content. The data shows that some tags, such as "Partisan Right" and "Conspiracy", are significantly under represented when looking only at the most popular socio-political channels. Through the use of our methods, we are able to get a much more accurate picture of the size of these communities on YouTube.
Voltage-Controlled Energy-Efficient Domain Wall Synapses With Stochastic Distribution of Quantized Weights in the Presence of Thermal Noise and Edge Roughness

We propose energy-efficient voltage-induced strain control of a domain wall (DW) in a perpendicularly magnetized nanoscale racetrack on a piezoelectric substrate that can implement a multistate synapse to be utilized in neuromorphic computing platforms. Here, strain generated in the piezoelectric is mechanically transferred to the racetrack and modulates the perpendicular magnetic anisotropy (PMA) in a system that has significant interfacial Dzyaloshinskii–Moriya interaction (DMI). When different voltages are applied (i.e., different strains are generated) in conjunction with spin–orbit torque (SOT) due to a fixed current flowing in the heavy metal layer for a fixed time, DWs are translated to different distances and implement different synaptic weights. We have shown using micromagnetic simulations that five-state and three-state synapses can be implemented in a racetrack that is modeled with the inclusion of natural edge roughness and room temperature thermal noise. These simulations show interesting dynamics of DWs due to interaction with roughness-induced pinning sites. Thus, notches need not be fabricated to implement multistate nonvolatile synapses. Such a strain-controlled synapse has an energy consumption of ~1 fJ and could thus be very attractive to implement energy-efficient quantized neural networks, which has been shown recently to achieve near equivalent classification accuracy to the full-precision neural networks.
PSR J1641+3627F: A Low-mass He White Dwarf Orbiting a Possible High-mass Neutron Star in the Globular Cluster M13

We report on the discovery of the companion star to the millisecond pulsar J1631+3627F in the globular cluster M13. By means of a combination of optical and near-UV high-resolution observations obtained with the Hubble Space Telescope, we identified the counterpart at the radio source position. Its location in the color–magnitude diagrams reveals that the companion star is a faint ( ) He-core white dwarf. We compared the observed companion magnitudes with those predicted by state-of-the-art binary evolution models and found out that it has a mass of , a radius of , and a surface temperature of K. Combining the companion mass with the pulsar mass function is not enough to determine the orbital inclination and the neutron star mass; however, the last two quantities become correlated: we found that either the system is observed at a low-inclination angle, or the neutron star is massive. In fact, assuming that binaries are randomly aligned with respect to the observer line of sight, there is a of probability that this system hosts a neutron star more massive than . In fact, the maximum and median mass of the neutron star, corresponding to orbital inclination angles of 90° and 60°, are and , respectively. On the other hand, also assuming an empirical neutron star mass probability distribution, we found that this system could host a neutron star with a mass of if orbiting with a low-inclination angle around 40°.
Learn to Navigate Maplessly With Varied LiDAR Configurations: A Support Point-Based Approach

Deep reinforcement learning (DRL) demonstrates great potential in mapless navigation domain. However, such a navigation model is normally restricted to a fixed configuration of the range sensor because its input format is fixed. In this letter, we propose a DRL model that can address range data obtained from different range sensors with different installation positions. Our model first extracts the goal-directed features from each obstacle point. Subsequently, it chooses global obstacle features from all point-feature candidates and uses these features for the final decision. As only a few points are used to support the final decision, we refer to these points as support points and our approach as support point-based navigation (SPN). Our model can handle data from different LiDAR setups and demonstrates good performance in simulation and real-world experiments. Moreover, it shows great potential in crowded scenarios with small obstacles when using a high-resolution LiDAR.
Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration

In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration of the human-like agent performing the same task (social perception), and ii) coordinate with the human-like agent to solve the task in an unseen environment as fast as possible (human-AI collaboration). For this challenge, we build VirtualHome-Social, a multi-agent household environment, and provide a benchmark including both planning and learning based baselines. We evaluate the performance of AI agents with the human-like agent as well as with real humans using objective metrics and subjective user ratings. Experimental results demonstrate that the proposed challenge and virtual environment enable a systematic evaluation on the important aspects of machine social intelligence at scale.
G0.173−0.42: an X-ray and radio magnetized filament near the galactic centre

The recent detection of an X-ray filament associated with the radio filament G0.173-0.42 adds to four other nonthermal radio filaments with X-ray counterparts, amongst the more than 100 elongated radio structures that have been identified as synchrotron-emitting radio filaments in the inner couple of degrees of the Galactic center. The synchrotron mechanism has also been proposed to explain the emission from X-ray filaments. However, the origin of radio filaments and the acceleration sites of energetic particles to produce synchrotron emission in radio and X-rays remain mysterious. Using MeerKAT, VLA, Chandra, WISE and Spitzer, we present structural details of G0.173-0.42 which consists of multiple radio filaments, one of which has an X-ray counterpart. A faint oblique radio filament crosses the radio and X-ray filaments. Based on the morphology, brightening of radio and X-ray intensities, and radio spectral index variation, we argue that a physical interaction is taking place between two magnetized filaments. We consider that the reconnection of the magnetic field lines at the interaction site leads to the acceleration of particles to GeV energies. We also argue against the synchrotron mechanism for the X-ray emission due to the short $\sim$30 year lifetime of TeV relativistic particles. Instead, we propose that the inverse Compton scattering mechanism is more likely to explain the X-ray emission by upscattering of seed photons emitted from a 10^6 solar luminosity star located at the northern tip of the X-ray filament.
Generating Strategic Dialogue for Negotiation with Theory of Mind

We propose a framework to integrate the concept of Theory of Mind (ToM) into generating utterances for task-oriented dialogue. Our approach explores the ability to model and infer personality types of opponents, predicts their responses, and uses this information to adapt the agent's high-level strategy in negotiation tasks. We introduce a probabilistic formulation for the first-order theory of mind and test our approach on the CraigslistBargain dataset. Experiments show that our method using ToM inference achieves a 40\% higher dialogue agreement rate compared to baselines on a mixed population of opponents. We also show that our model displays diverse negotiation behavior with different types of opponents.
The mid-infrared Leavitt law for classical Cepheids in the Magellanic Clouds

The Cepheid Leavitt Law (LL), also known as the Period-Luminosity relation, is a crucial tool for assembling the cosmic distance ladder. By combining data from the OGLE-IV catalogue with mid-infrared photometry from the Spitzer Space Telescope, we have determined the $3.6$ $\mu$m and $4.5$ $\mu$m LLs for the Magellanic Clouds using around 5000 fundamental-mode Classical Cepheids. Mean magnitudes were determined using a Monte Carlo Markov Chain (MCMC) template fitting procedure, with template light curves constructed from a subsample of these Cepheids with fully-phased, well-sampled light curves. The dependence of the Large Magellanic Cloud LL coefficients on various period cuts was tested, in addition to the linearity of the relationship. The zero point of the LL was calibrated using the parallaxes of Milky Way Cepheids from the Hubble Space Telescope and Gaia Data Release 2. Our final calibrated relations are $M_{[3.6]} = -3.246(\pm 0.008)(\log(P)-1.0)-5.784(\pm 0.030)$ and $M_{[4.5]} = -3.162(\pm 0.008)(\log(P)-1.0)-5.751(\pm 0.030)$.
Enhancing keyphrase extraction from microblogs using human reading time

The premise of manual keyphrase annotation is to read the corresponding content of an annotated object. Intuitively, when we read, more important words will occupy a longer reading time. Hence, by leveraging human reading time, we can find the salient words in the corresponding content. However, previous studies on keyphrase extraction ignore human reading features. In this article, we aim to leverage human reading time to extract keyphrases from microblog posts. There are two main tasks in this study. One is to determine how to measure the time spent by a human on reading a word. We use eye fixation durations (FDs) extracted from an open source eye‐tracking corpus. Moreover, we propose strategies to make eye FD more effective on keyphrase extraction. The other task is to determine how to integrate human reading time into keyphrase extraction models. We propose two novel neural network models. The first is a model in which the human reading time is used as the ground truth of the attention mechanism. In the second model, we use human reading time as the external feature. Quantitative and qualitative experiments show that our proposed models yield better performance than the baseline models on two microblog datasets.
Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning

Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to complex hysteresis effects, unmodelled friction and stiction phenomena, and unknown effects during contact situations. A promising approach is to obtain spatio-temporal models in a data-driven way using recurrent neural networks, as they can overcome those issues. However, such models often do not meet accuracy demands sufficiently, degenerate in performance for the required high sampling frequencies and cannot provide uncertainty estimates. We adopt a recent probabilistic recurrent neural network architecture, called Recurrent Kalman Networks (RKNs), to model learning by conditioning its transition dynamics on the control actions. RKNs outperform standard recurrent networks such as LSTMs on many state estimation tasks. Inspired by Kalman filters, the RKN provides an elegant way to achieve action conditioning within its recurrent cell by leveraging additive interactions between the current latent state and the action variables. We present two architectures, one for forward model learning and one for inverse model learning. Both architectures significantly outperform existing model learning frameworks as well as analytical models in terms of prediction performance on a variety of real robot dynamics models.
Childhood obesity: rapid weight gain in early childhood and subsequent cardiometabolic risk

Abstract. Dynamic changes in body weight have long been recognized as important indicators of risk for human health. Many population-based observational studies have shown that rapid weight gain during infancy, including a catch-up growth phenomenon or adiposity rebound in early childhood, predisposes a person to the development of obesity, type 2 diabetes, and cardiovascular diseases later in life. However, a consensus has not been established regarding which period of weight gain contributes to future risks. This review evaluates recent evidence on the relationship between early rapid growth and future obesity and cardiometabolic risk, with a focus on the differential significance of rapid weight gain in infancy and early childhood. Although there is a need for attention to childhood growth during early infancy before 1 yr of age as it may be related to future obesity, emerging evidence strongly suggests that toddlers showing an increase in body mass index (BMI) before 3 yr of age, a period normally characterized by decreased BMI, are prone to developing later cardiometabolic risk.
Burden of Mycobacterium Tuberculosis in Animals Versus M. bovis in Human

worker who suffered from pulmonary tuberculosis 1 year prior to this case were the same strains [26]. Except for slight macroscopic enlargement, no gross pathological changes were visible on any lymph node specimen collected from the three slaughtered animals. Histopathological examination of hematoxylin-eosin-stained tissues revealed nonspecific findings. No acid-fast bacilli were detected on Ziehl-Neelsen stained impression smears. Abstract M. tuberculosis and M. bovis are member of Mycobacterium complex (MTBC). M. tuberculosis , a primarily pathogen of human tuberculosis (TB) can infect cattle and vice versa M. bovis can infect human beings as well as animals with progressive disease. The burden of either of infections is not known in human and animals. M. tuberculosis does not cause over disease in cattle, however, M. bovis cause TB in human indistinguishable from M. tuberculosis . This review provides detailed information about the status of M. tuberculosis infection in cattle and the magnitude of cases wherein M. tuberculosis was isolated from animals.
A multicritical Landau-Potts field theory

We investigate a perturbatively renormalizable $S_{q}$ invariant model with $N=q-1$ scalar field components below the upper critical dimension $d_c=\frac{10}{3}$. Our results hint at the existence of multicritical generalizations of the critical models of spanning random clusters and percolations in three dimensions. We also discuss the role of our multicritical model in a conjecture that involves the separation of first and second order phases in the $(d,q)$ diagram of the Potts model.