Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not available. Learned dynamics models are a promising approach for learning about the environment without rewards or task-directed data, but planning to reach goals with such a model requires a notion of functional similarity between observations and goal states. We present a self-supervised method for model-based visual goal reaching, which uses both a visual dynamics model as well as a dynamical distance function learned using model-free reinforcement learning. Our approach learns entirely using offline, unlabeled data, making it practical to scale to large and diverse datasets. In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot. In comparisons, we find that this approach substantially outperforms both model-free and model-based prior methods. Videos and visualizations are available here: http://sites.google. com/berkeley.edu/mbold.
DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data

Automated vehicles promise a future where drivers can engage in non-driving tasks without hands on the steering wheels for a prolonged period. Nevertheless, automated vehicles may still need to occasionally hand the control back to drivers due to technology limitations and legal requirements. While some systems determine the need for driver takeover using driver context and road condition to initiate a takeover request, studies show that the driver may not react to it. We present DeepTake, a novel deep neural network-based framework that predicts multiple aspects of takeover behavior to ensure that the driver is able to safely take over the control when engaged in non-driving tasks. Using features from vehicle data, driver biometrics, and subjective measurements, DeepTake predicts the driver’s intention, time, and quality of takeover. We evaluate DeepTake performance using multiple evaluation metrics. Results show that DeepTake reliably predicts the takeover intention, time, and quality, with an accuracy of 96%, 93%, and 83%, respectively. Results also indicate that DeepTake outperforms previous state-of-the-art methods on predicting driver takeover time and quality. Our findings have implications for the algorithm development of driver monitoring and state detection.
Why do classifier accuracies show linear trends under distribution shift?

Several recent studies observed that when classification models are evaluated on two different data distributions, the models' accuracies on one distribution are approximately a linear function of their accuracies on another distribution. We offer an explanation for these observations based on two assumptions that can be assessed empirically: (1) certain events have similar probabilities under the two distributions; (2) the probability that a lower accuracy model correctly classifies a data point sampled from one distribution when a higher accuracy model classifies it incorrectly is small.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

Interest has been rising lately towards modeling time-evolving knowledge graphs (KGs). Recently, graph representation learning approaches have become the dominant paradigm for link prediction on temporal KGs. However, the embedding-based approaches largely operate in a black-box fashion, lacking the ability to judge the results' reliability. This paper provides a future link forecasting framework that reasons over query-relevant subgraphs of temporal KGs and jointly models the graph structures and the temporal context information. Especially, we propose a temporal relational attention mechanism and a novel reverse representation update scheme to guide the extraction of an enclosing subgraph around the query. The subgraph is expanded by an iterative sampling of temporal neighbors and attention propagation. As a result, our approach provides human-understandable arguments for the prediction. We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task. While being more explainable, our model also obtains a relative improvement of up to 17.7 $\%$ on MRR compared to the previous best KG forecasting methods. We also conduct a survey with 53 respondents, and the results show that the reasoning arguments extracted by the model for link forecasting are aligned with human understanding.
XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders

Multilingual machine translation enables a single model to translate between different languages. Most existing multilingual machine translation systems adopt a randomly initialized Transformer backbone. In this work, inspired by the recent success of language model pre-training, we present XLM-T, which initializes the model with an off-the-shelf pretrained cross-lingual Transformer encoder and fine-tunes it with multilingual parallel data. This simple method achieves significant improvements on a WMT dataset with 10 language pairs and the OPUS-100 corpus with 94 pairs. Surprisingly, the method is also effective even upon the strong baseline with back-translation. Moreover, extensive analysis of XLM-T on unsupervised syntactic parsing, word alignment, and multilingual classification explains its effectiveness for machine translation. The code will be at https://aka.ms/xlm-t.
Control Barriers in Bayesian Learning of System Dynamics

This article focuses on learning a model of system dynamics online, while satisfying safety constraints. Our objective is to avoid offline system identification or hand-specified models and allow a system to safely and autonomously estimate and adapt its own model during operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distribution over the system dynamics. Specifically, we propose a new matrix variate Gaussian process (MVGP) regression approach with an efficient covariance factorization to learn the drift and input gain terms of a nonlinear control-affine system. The MVGP distribution is then used to optimize the system behavior and ensure safety with high probability, by specifying control Lyapunov function (CLF) and control barrier function (CBF) chance constraints. We show that a safe control policy can be synthesized for systems with arbitrary relative degree and probabilistic CLF-CBF constraints by solving a second-order cone program. Finally, we extend our design to a self-triggering formulation, adaptively determining the time at which a new control input needs to be applied in order to guarantee safety.
Contributions for the kaon pair from 
ρ(770)
, 
ω(782)
 and their excited states in the 
B→KK¯h
 decays

We study the resonance contributions for the kaon pair originating from the intermediate states $\rho(770,1450,1700)$ and $\omega(782,1420,1650)$ for the three-body hadronic decays $B\to K\bar K h$ in the perturbative QCD approach, where $h=(\pi, K)$. The branching fractions of the virtual contributions for $K\bar K$ from the Breit-Wigner formula tails of $\rho(770)$ and $\omega(782)$ which have been ignored in experimental and theoretical studies for these decays are found larger than the corresponding contributions from the resonances $\rho(1450,1700)$ and $\omega(1420,1650)$. The differential branching fractions for $B\to \rho(770) h\to K\bar K h$ and $B\to\omega(782) h \to K\bar K h$ are found nearly unaffected by the quite different values of the full widths for $\rho(770)$ and $\omega(782)$ in this paper. The predictions in this work for the branching fractions of the quasi-two-body decays $B^+\to \pi^+ \rho(1450)^0\to \pi^+K^+K^-$ and $B^+\to \pi^+ \rho(1450)^0\to \pi^+\pi^+\pi^-$ meet the requirement of $SU(3)$ symmetry relation.
Improving Adversarial Robustness in Weight-quantized Neural Networks

Neural networks are getting deeper and more computation-intensive nowadays. Quantization is a useful technique in deploying neural networks on hardware platforms and saving computation costs with negligible performance loss. However, recent research reveals that neural network models, no matter full-precision or quantized, are vulnerable to adversarial attacks. In this work, we analyze both adversarial and quantization losses and then introduce criteria to evaluate them. We propose a boundary-based retraining method to mitigate adversarial and quantization losses together and adopt a nonlinear mapping method to defend against white-box gradient-based adversarial attacks. The evaluations demonstrate that our method can better restore accuracy after quantization than other baseline methods on both black-box and white-box adversarial attacks. The results also show that adversarial training suffers quantization loss and does not cooperate well with other training methods.
Reverberation Mapping of Changing-look Active Galactic Nucleus NGC 3516

The changes of broad emission lines should be a crucial issue in understanding the physical properties of changing-look active galactic nuclei (CL-AGNs). Here we present the results of an intensive and homogeneous 6 month long reverberation mapping (RM) monitoring campaign during a low-activity state of the CL-AGN Seyfert galaxy NGC 3516. Photometric and spectroscopic monitoring was carried out during 2018–2019 with the Lijiang 2.4 m telescope. The sampling is 2 days in most nights, and the average sampling is ∼3 days. The rest-frame time lags of Hα and Hβ are and days, respectively. From an rms Hβ line dispersion of σ line = 1713.3 ± 46.7 km s−1 and a virial factor of f σ = 5.5, the central black hole mass of NGC 3516 is estimated to be , which is in agreement with previous estimates. The velocity-resolved delays show that the time lags increase toward negative velocity for both Hα and Hβ. The velocity-resolved RM of Hα is done for the first time. These RM results are consistent with other observations before the spectral-type change, indicating a basically constant broad-line region structure during the CL process. The CL model of changes of accretion rate seems to be favored by long-term Hβ variability and RM observations of NGC 3516.
Metric monads

We develop universal algebra over an enriched category and relate it to finitary enriched monads over . Using it, we deduce recent results about ordered universal algebra where inequations are used instead of equations. Then we apply it to metric universal algebra where quantitative equations are used instead of equations. This contributes to understanding of finitary monads on the category of metric spaces.
Generalized Thomas-Yau Uniqueness Theorems

We generalize Thomas–Yau’s uniqueness theorem [17, Theorem 4.3] in two ways. We prove a stronger statement for special Lagranigans and include minimal Lagrangians in Kähler–Einstein manifolds or more generally J-minimal Lagrangians introduced by Lotay and Pacini [13, 14]. In every case the heart of the proof is to make certain Hamiltonian perturbations. For this we use the method by Imagi, Joyce and Oliveira dos Santos [8, Theorem 4.7].
Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup

Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key enabler of the tremendous success of modern RL. Among many asynchronous RL algorithms, arguably the most popular and effective one is the asynchronous advantage actor-critic (A3C) algorithm. Although A3C is becoming the workhorse of RL, its theoretical properties are still not well-understood, including the non-asymptotic analysis and the performance gain of parallelism (a.k.a. speedup). This paper revisits the A3C algorithm with TD(0) for the critic update, termed A3C-TD(0)
From Semantic Communication to Semantic-Aware Networking: Model, Architecture, and Open Problems

Existing communication systems are mainly built based on Shannon's information theory, which deliberately ignores the semantic aspects of communication. The recent iteration of wireless technology, 5G and beyond, promises to support a plethora of services enabled by carefully tailored network capabilities based on contents, requirements, as well as semantics. This has sparked significant interest in semantic communication, a novel paradigm that involves the meaning of messages in communication. In this article, we first review classic semantic communication frameworks and then summarize key challenges that hinder its popularity. We observe that some semantic communication processes such as semantic detection, knowledge modeling, and coordination can be resource-consuming and inefficient, especially for communication between a single source and a destination. We therefore propose a novel architecture based on federated edge intelligence for supporting resource-efficient semantic-aware networking. Our architecture allows each user to offload computationally intensive semantic encoding and decoding tasks to edge servers and protect its proprietary model-re-lated information by coordinating via intermediate results. Our simulation result shows that the proposed architecture can reduce resource consumption and significantly improve communication efficiency.
On groupoids and $C^*$-algebras from self-similar actions.

Given a self-similar groupoid action $(G,E)$ on the path space of a finite graph, we study the associated Exel-Pardo \'etale groupoid ${\mathcal G}(G,E)$ and its $C^*$-algebra $C^*(G,E)$. We review some facts about groupoid actions, skew products and semi-direct products and generalize a result of Renault about similarity of groupoids which resembles Takai duality. We also describe a general strategy to compute the $K$-theory of $C^*(G,E)$ and the homology of ${\mathcal G}(G,E)$ in certain cases and illustrate with an example.
Meta Learning Backpropagation And Improving It

Many concepts have been proposed for meta learning with neural networks (NNs), e.g., NNs that learn to reprogram fast weights, Hebbian plasticity, learned learning rules, and meta recurrent NNs. Our Variable Shared Meta Learning (VSML) unifies the above and demonstrates that simple weight-sharing and sparsity in an NN is sufficient to express powerful learning algorithms (LAs) in a reusable fashion. A simple implementation of VSML where the weights of a neural network are replaced by tiny LSTMs allows for implementing the backpropagation LA solely by running in forward-mode. It can even meta learn new LAs that differ from online backpropagation and generalize to datasets outside of the meta training distribution without explicit gradient calculation. Introspection reveals that our meta learned LAs learn through fast association in a way that is qualitatively different from gradient descent.
Highly enhanced adsorption of lead ions on chitosan granules functionalized with poly(acrylic acid)

Cross-linked chitosan granules (denoted as CTS) were surface-functionalized with poly(acrylic acid) (PAAc) and examined for the adsorption of lead ions from aqueous solutions. PAAc was successfully grafted on CTS through a simple two-step reaction in a solution. The PAAc-functionalized chitosan granules (denoted as CTS-PAAc) showed significantly greater adsorption capacities for lead ions than CTS, and the performance improved with the increase of pH in the pH range of 1−6 examined. An adsorption isotherm and kinetic study conducted at pH 4 and room temperature showed a maximum adsorption capacity of 294.12 mg/g and an adsorption equilibrium time of less than 5 h for lead ions on CTS-PAAC, in contrast to only 95.15 mg/g and up to 8 h on CTS. Mechanism study revealed that the excellent adsorption performance of CTS-PAAc for lead ions was attributed to the many carboxyl groups grafted on CTS-PAAc. It was found that adsorbed lead ions on CTS-PAAc can be effectively desorbed and the regenerated CTS-PAAc can b.
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray

In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention. These diseases can be effectively diagnosed and treated with the help of lung imaging. With the development of deep learning technology and the emergence of many public medical image datasets, the diagnosis of lung diseases via medical imaging has been further improved. This article reviews pulmonary CT and X-ray image detection and classification in the last decade. It also provides an overview of the detection of lung nodules, pneumonia, and other common lung lesions based on the imaging characteristics of various lesions. Furthermore, this review introduces 26 commonly used public medical image datasets, summarizes the latest technology, and discusses current challenges and future research directions.
Detection of Magnetic Gap in Topological Surface States of MnBi2Te4

Recently, intrinsic antiferromagnetic topological insulator MnBi2Te4 has drawn intense research interest and leads to plenty of significant progress in physics and materials science by hosting quantum anomalous Hall effect, axion insulator state, and other quantum phases. An essential ingredient to realize these quantum states is the magnetic gap in the topological surface states induced by the out-of-plane ferromagnetism on the surface of MnBi2Te4. However, the experimental observations of the surface gap remain controversial. Here, we report the observation of the surface gap via the point contact tunneling spectroscopy. In agreement with theoretical calculations, the gap size is around 50 meV, which vanishes as the sample becomes paramagnetic with increasing temperature. The magnetoresistance hysteresis is detected through the point contact junction on the sample surface with an out-of-plane magnetic field, substantiating the surface ferromagnetism. Furthermore, the non-zero transport spin polarization coming from the ferromagnetism is determined by the point contact Andreev reflection spectroscopy. Combining these results, the magnetism-induced gap in topological surface states of MnBi2Te4 is revealed.
Exploring a strongly non-Markovian animal behavior

A freely walking fly visits roughly 100 stereotyped states in a strongly non-Markovian sequence. To explore these dynamics, we develop a generalization of the information bottleneck method, compressing the large number of behavioral states into a more compact description that maximally preserves the correlations between successive states. Surprisingly, preserving these short time correlations with a compression into just two states captures the long ranged correlations seen in the raw data. Having reduced the behavior to a binary sequence, we describe the distribution of these sequences by an Ising model with pairwise interactions, which is the maximum entropy model that matches the two-point correlations. Matching the correlation function at longer and longer times drives the resulting model toward the Ising model with inverse square interactions and near zero magnetic field. The emergence of this statistical physics problem from the analysis real data on animal behavior is unexpected.
Is Human Scoring the Best Criteria for Summary Evaluation?

Normally, summary quality measures are compared with quality scores produced by human annotators. A higher correlation with human scores is considered to be a fair indicator of a better measure. We discuss observations that cast doubt on this view. We attempt to show a possibility of an alternative indicator. Given a family of measures, we explore a criterion of selecting the best measure not relying on correlations with human scores. Our observations for the BLANC family of measures suggest that the criterion is universal across very different styles of summaries.
Minimal and nonminimal universal extra dimension models in the light of LHC data at 13 TeV

Universal Extra Dimension (UED) is a well-motivated and well-studied scenario. One of the main motivations is the presence of a dark matter (DM) candidate namely, the lightest level-1 Kaluza-Klein (KK) particle (LKP), in the particle spectrum of UED. The minimal version of UED (mUED) scenario is highly predictive with only two parameters namely, the radius of compactification and cut-off scale, to determine the phenomenology. Therefore, stringent constraint results from the WMAP/PLANCK measurement of DM relic density (RD) of the universe. The production and decays of level-1 quarks and gluons in UED scenarios give rise to multijet final states at the Large Hadron Collider (LHC) experiment. We study the ATLAS search for multijet plus missing transverse energy signatures at the LHC with 13 TeV center of mass energy and 139 inverse femtobarn integrated luminosity. In view of the fact that the DM RD allowed part of mUED parameter-space has already been ruled out by the ATLAS multijet search, we move on to a less restricted version of UED namely, the non-minimal UED (nmUED), with non-vanishing boundary-localized terms (BLTs). The presence of BLTs significantly alters the dark matter as well as the collider phenomenology of nmUED. We obtain stringent bounds on the BLT parameters from the ATLAS multijet plus missing transverse energy search.
Dimension-free estimates for the discrete spherical maximal functions

Abstract. We prove that the discrete spherical maximal functions (in the spirit of Magyar, Stein and Wainger) corresponding to the Euclidean spheres in Z with dyadic radii have l(Z) bounds for all p ∈ [2,∞] independent of the dimensions d ≥ 5. An important part of our argument is the asymptotic formula in the Waring problem for the squares with a dimension-free multiplicative error term. By considering new approximating multipliers we will show how to absorb an exponential in dimension (like C for some C > 1) growth in norms arising from the sampling principle of Magyar, Stein and Wainger, and ultimately deduce dimension-free estimates for the discrete spherical maximal functions.
A Subword Guided Neural Word Segmentation Model for Sindhi

Deep neural networks employ multiple processing layers for learning text representations to alleviate the burden of manual feature engineering in Natural Language Processing (NLP). Such text representations are widely used to extract features from unlabeled data. The word segmentation is a fundamental and inevitable prerequisite for many languages. Sindhi is an under-resourced language, whose segmentation is challenging as it exhibits space omission, space insertion issues, and lacks the labeled corpus for segmentation. In this paper, we investigate supervised Sindhi Word Segmentation (SWS) using unlabeled data with a Subword Guided Neural Word Segmenter (SGNWS) for Sindhi. In order to learn text representations, we incorporate subword representations to recurrent neural architecture to capture word information at morphemic-level, which takes advantage of Bidirectional Long-Short Term Memory (BiLSTM), self-attention mechanism, and Conditional Random Field (CRF). Our proposed SGNWS model achieves an F1 value of 98.51% without relying on feature engineering. The empirical results demonstrate the benefits of the proposed model over the existing Sindhi word segmenters.