Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the Perceived Safety of Urban City via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by expert evaluation policy for urban perception, we proposed a novel inverse reinforcement learning (IRL) based framework for predicting urban safety and recovering the corresponding reward function. We also presented a scalable state representation method to model the prediction problem as a Markov decision process (MDP) and use reinforcement learning (RL) to solve the problem. Additionally, we built a dataset called SmallCity based on the crowdsourcing method to conduct the research. As far as we know, this is the first time the IRL approach has been introduced to the urban safety perception and planning field to help experts quantitatively analyze perceptual features. Our results showed that IRL has promising prospects in this field. We will later open-source the crowdsourcing data collection site and the model proposed in this paper.
A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. Along with knowledge about the imaged molecules, cryo-EM maps allow de novo atomic modelling, which is typically done through a laborious manual process. Taking inspiration from recent advances in machine learning applications to protein structure prediction, we propose a graph neural network (GNN) approach for automated model building of proteins in cryo-EM maps. The GNN acts on a graph with nodes assigned to individual amino acids and edges representing the protein chain. Combining information from the voxel-based cryo-EM data, the amino acid sequence data and prior knowledge about protein geometries, the GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. Application to 28 test cases shows that our approach outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 \r{A}.
Deep Digging into the Generalization of Self-Supervised Monocular Depth Estimation

Self-supervised monocular depth estimation has been widely studied recently. Most of the work has focused on improving performance on benchmark datasets, such as KITTI, but has offered a few experiments on generalization performance. In this paper, we investigate the backbone networks (e.g., CNNs, Transformers, and CNN-Transformer hybrid models) toward the generalization of monocular depth estimation. We first evaluate state-of-the-art models on diverse public datasets, which have never been seen during the network training. Next, we investigate the effects of texture-biased and shape-biased representations using the various texture-shifted datasets that we generated. We observe that Transformers exhibit a strong shape bias and CNNs do a strong texture-bias. We also find that shape-biased models show better generalization performance for monocular depth estimation compared to texture-biased models. Based on these observations, we newly design a CNN-Transformer hybrid network with a multi-level adaptive feature fusion module, called MonoFormer. The design intuition behind MonoFormer is to increase shape bias by employing Transformers while compensating for the weak locality bias of Transformers by adaptively fusing multi-level representations. Extensive experiments show that the proposed method achieves state-of-the-art performance with various public datasets. Our method also shows the best generalization ability among the competitive methods.
Further studies of ionization in interstellar clouds

Interferometric scans of the interstellar lambda7699 line of K i or of the D$sub 1$ line of Na i are reported for 14 stars. Along with results obtained recently by others for the column densities N (H i), N (H$sub 2$), or N (Na i) toward a larger number of stars, these results are added to a previous compilation of such data, and are used to analyze again the relations among the well-correlated quantities N (K i), N (Na i), and N/sub H/. The estimated depletion factors of both K and Na are about 3 to 4. Alternatively, the fractional ionization n/sube//n/sub H/approximately-greater-than9x10$sup -4$ in these relatively low-density clouds, if the K and Na abundances are not to to exceed their solar values. Photoionization of the heavy elements by starlight can account for the greater part of this ionization, if interstellar carbon is undepleted. The influence of temperature effects and of gas-phase chemical reactions on the approximately quadratic variations of N (K i) and N (Na i) with N/sub H/ are discussed. (AIP)
FBG-Based Online Learning and 3-D Shape Control of Unmodeled Continuum and Soft Robots in Unstructured Environments

In this paper, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots embedded with fiber Bragg grating (FBG) sensors. Developments of 3-D shape perception and control technologies are crucial for continuum robots to perform the tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive controller by leveraging FBG shape feedback that can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study by using two continuum robots both integrated with multi-core FBGs, including a robotic-assisted colonoscope and multi-section extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments as well as phantom experiments.
Maximizing and Satisficing in Multi-armed Bandits with Graph Information

Pure exploration in multi-armed bandits has emerged as an important framework for modeling decision-making and search under uncertainty. In modern applications, however, one is often faced with a tremendously large number of options. Even obtaining one observation per option may be too costly rendering traditional pure exploration algorithms ineffective. Fortunately, one often has access to similar relationships amongst the options that can be leveraged. In this paper, we consider the pure exploration problem in stochastic multi-armed bandits where the similarities between the arms are captured by a graph and the rewards may be represented as a smooth signal on this graph. In particular, we consider the problem of finding the arm with the maximum reward (i.e., the maximizing problem) or one with a sufficiently high reward (i.e., the satisficing problem) under this model. We propose novel algorithms \textbf{\algoname{}} (GRaph-based UcB) and $\zeta$-\textbf{\algoname{}} for these problems and provide a theoretical characterization of their performance which specifically elicits the benefit of the graph side information. We also prove a lower bound on the data requirement, showing a large class of problems where these algorithms are near-optimal. We complement our theory with experimental results that show the benefit of capitalizing on such side information.
A Measurement of Weak Lensing by Large-Scale Structure in Red-Sequence Cluster Survey Fields

We have analyzed ~24 deg2 of RC-band imaging data from the Red-Sequence Cluster Survey (RCS) and measured the excess correlations between galaxy ellipticities on scales ranging from 1' to 30'. We have used data from two different telescopes: ~16.4 deg2 of Canada-France-Hawaii Telescope data and ~7.6 deg2 of Cerro Tololo Inter-American Observatory 4 m data, distributed over 13 widely separated patches. For the first time, a direct comparison can be made of the lensing signal measured using different instruments, which provides an important test of the weak-lensing analysis itself. The measurements obtained from the two telescopes agree well. For the lensing analysis, we use galaxies down to a limiting magnitude of RC = 24, for which the redshift distribution is known relatively well. This allows us to constrain some cosmological parameters. For the currently favored ΛCDM model (Ωm = 0.3, ΩΛ = 0.7, Γ = 0.21), we obtain σ8 = 0.81 (95% confidence), in agreement with the 2001 results from Van Waerbeke and coworkers, who used fainter galaxies (and consequently higher redshift galaxies). The good agreement between these two very different weak-lensing studies demonstrates that weak lensing is a useful tool in observational cosmology.
Deeper Chandra Follow-up of Cygnus TeV Source Perpetuates Mystery

A 50 ks Chandra observation of the unidentified TeV source in Cygnus reported by the HEGRA collaboration reveals no obvious diffuse X-ray counterpart. However, 240 pointlike X-ray sources are detected within or nearby the extended TeV J2032+4130 source region, of which at least 36 are massive stars and two may be radio emitters. That the HEGRA source is a composite, having as a counterpart the multiple pointlike X-ray sources we observe, cannot be ruled out. Indeed, the distribution of pointlike X-ray sources appears nonuniform and concentrated broadly within the extent of the TeV source region. We offer a hypothesis for the origin of the very high energy gamma-ray emission in Cyg OB2 based on the local acceleration of TeV-range cosmic rays and the differential distribution of OB versus less massive stars in this association.
A DPU Solution for Container Overlay Networks

There is an increasing demand to incorporate hybrid environments as part of workflows across edge, cloud, and HPC systems. In a such converging environment of cloud and HPC, containers are starting to play a more prominent role, bringing their networking infrastructure along with them. However, the current body of work shows that container overlay networks, which are often used to connect containers across physical hosts, are ill-suited for the HPC environment. They tend to impose significant overhead and noise, resulting in degraded performance and disturbance to co-processes on the same host. This paper focuses on utilizing a novel class of hardware, Data Processing Unit, to offload the networking stack of overlay networks away from the host onto the DPU. We intend to show that such ancillary offload is possible and that it will result in decreased overhead on host nodes which in turn will improve the performance of running processes.
Large orders in strong-field QED

We address the issue of large-order expansions in strong-field QED. Our approach is based on the one-loop effective action encoded in the associated photon polarization tensor. We concentrate on the simple case of crossed fields aiming at possible applications of high-power lasers to measure vacuum birefringence. A simple next-to-leading order derivative expansion reveals that the indices of refraction increase with frequency. This signals normal dispersion in the small-frequency regime where the derivative expansion makes sense. To gain information beyond that regime we determine the factorial growth of the derivative expansion coefficients evaluating the first 82 orders by means of computer algebra. From this we can infer a nonperturbative imaginary part for the indices of refraction indicating absorption (pair production) as soon as energy and intensity become (super)critical. These results compare favourably with an analytic evaluation of the polarization tensor asymptotics. Kramers-Kronig relations finally allow for a nonperturbative definition of the real parts as well and show that absorption goes hand in hand with anomalous dispersion for sufficiently large frequencies and fields.
VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning

Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal information and leverage different resources (e.g., visual-audio pairs, audio-text pairs, unlabeled speech, and unlabeled text) to facilitate speech representation learning was not well explored. In this paper, we propose a unified cross-modal representation learning framework VATLM (Visual-Audio-Text Language Model). The proposed VATLM employs a unified backbone network to model the modality-independent information and utilizes three simple modality-dependent modules to preprocess visual, speech, and text inputs. In order to integrate these three modalities into one shared semantic space, VATLM is optimized with a masked prediction task of unified tokens, given by our proposed unified tokenizer. We evaluate the pre-trained VATLM on audio-visual related downstream tasks, including audio-visual speech recognition (AVSR), visual speech recognition (VSR) tasks. Results show that the proposed VATLM outperforms previous the state-of-the-art models, such as audio-visual pre-trained AV-HuBERT model, and analysis also demonstrates that VATLM is capable of aligning different modalities into the same space. To facilitate future research, we release the code and pre-trained models at https://aka.ms/vatlm.
Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization

The vulnerability of smartphones to cyberattacks has been a severe concern to users arising from the integrity of installed applications (\textit{apps}). Although applications are to provide legitimate and diversified on-the-go services, harmful and dangerous ones have also uncovered the feasible way to penetrate smartphones for malicious behaviors. Thorough application analysis is key to revealing malicious intent and providing more insights into the application behavior for security risk assessments. Such in-depth analysis motivates employing deep neural networks (DNNs) for a set of features and patterns extracted from applications to facilitate detecting potentially dangerous applications independently. This paper presents an Analytic-based deep neural network, Android Malware detection (ADAM), that employs a fine-grained set of features to train feature-specific DNNs to have consensus on the application labels when their ground truth is unknown. In addition, ADAM leverages the transfer learning technique to obtain its adjustability to new applications across smartphones for recycling the pre-trained model(s) and making them more adaptable by model personalization and federated learning techniques. This adjustability is also assisted by federated learning guards, which protect ADAM against poisoning attacks through model analysis. ADAM relies on a diverse dataset containing more than 153000 applications with over 41000 extracted features for DNNs training. The ADAM's feature-specific DNNs, on average, achieved more than 98% accuracy, resulting in an outstanding performance against data manipulation attacks.
Photometric Properties of Jupiter Trojans Detected by the Dark Energy Survey

The Jupiter Trojans are a large group of asteroids that are coorbiting with Jupiter near its L4 and L5 Lagrange points. The study of Jupiter Trojans is crucial for testing different models of planet formation that are directly related to our understanding of solar system evolution. In this work, we select known Jupiter Trojans listed by the Minor Planet Center from the full six years data set (Y6) of the Dark Energy Survey (DES) to analyze their photometric properties. The DES data allow us to study Jupiter Trojans with a fainter magnitude limit than previous studies in a homogeneous survey with griz band measurements. We extract a final catalog of 573 unique Jupiter Trojans. Our sample include 547 asteroids belonging to L5. This is one of the largest analyzed samples for this group. By comparing with the data reported by other surveys we found that the color distribution of L5 Trojans is similar to that of L4 Trojans. We find that L5 Trojans’ g − i and g − r colors become less red with fainter absolute magnitudes, a trend also seen in L4 Trojans. Both the L4 and L5 clouds consistently show such a color–size correlation over an absolute magnitude range 11 < H < 18. We also use DES colors to perform taxonomic classifications. C- and P-type asteroids outnumber D-type asteroids in the L5 Trojans DES sample, which have diameters in the 5–20 km range. This is consistent with the color–size correlation.
On the prime Selmer ranks of cyclic prime twist families of elliptic curves over global function fields

Fix a prime number $p$. Let $\mathbb{F}_q$ be a finite field of characteristic coprime to 2, 3, and $p$, which also contains the primitive $p$-th root of unity $\mu_p$. Based on the works by Swinnerton-Dyer and Klagsbrun, Mazur, and Rubin, we prove that the probability distribution of the sizes of prime Selmer groups over a family of cyclic prime twists of non-isotrivial elliptic curves over $\mathbb{F}_q(t)$ satisfying a number of mild constraints conforms to the distribution conjectured by Bhargava, Kane, Lenstra, Poonen, and Rains with explicit error bounds. The key tools used in proving these results are the Riemann hypothesis over global function fields, the Erd\"os-Kac theorem, and the geometric ergodicity of Markov chains.
Leaf clustering using circular densities

In the biology ﬁeld of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have diﬀerent resolution, so normalisation is done by considering that they are circular densities. Densities are rotated by subtracting the mean preferred direction. Distance measures between densities are used to produce a hierarchical clustering method to classify the leaves. We illustrate our approach with a real dataset.
Privacy in Practice: Private COVID-19 Detection in X-Ray Images

Machine learning (ML) can help fight the COVID-19 pandemic by enabling rapid screening of large volumes of chest X-ray images. To perform such data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP). Previous works exploring private COVID-19 ML models are in part based on small or skewed datasets, are lacking in their privacy guarantees, and do not investigate practical privacy. In this work, we therefore suggest several improvements to address these open gaps. We account for inherent class imbalances in the data and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets than in previous work. Our evaluation is supported by empirically estimating practical privacy leakage through actual attacks. Based on theory, the introduced DP should help limit and mitigate information leakage threats posed by black-box Membership Inference Attacks (MIAs). Our practical privacy analysis is the first to test this hypothesis on the COVID-19 detection task. In addition, we also re-examine the evaluation on the MNIST database. Our results indicate that based on the task-dependent threat from MIAs, DP does not always improve practical privacy, which we show on the COVID-19 task. The results further suggest that with increasing DP guarantees, empirical privacy leakage reaches an early plateau and DP therefore appears to have a limited impact on MIA defense. Our findings identify possibilities for better utility-privacy trade-offs, and we thus believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy.
Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression

Previous analysis of regularized functional linear regression in a reproducing kernel Hilbert space (RKHS) typically requires the target function to be contained in this kernel space. This paper studies the convergence performance of divide-and-conquer estimators in the scenario that the target function does not necessarily reside in the underlying RKHS. As a decomposition-based scalable approach, the divide-and-conquer estimators of functional linear regression can substantially reduce the algorithmic complexities in time and memory. We develop an integral operator approach to establish sharp finite sample upper bounds for prediction with divide-and-conquer estimators under various regularity conditions of explanatory variables and target function. We also prove the asymptotic optimality of the derived rates by building the mini-max lower bounds. Finally, we consider the convergence of noiseless estimators and show that the rates can be arbitrarily fast under mild conditions.
A Two-Stage Active Learning Algorithm for k-Nearest Neighbors

$k$-nearest neighbor classification is a popular non-parametric method because of desirable properties like automatic adaption to distributional scale changes. Unfortunately, it has thus far proved difficult to design active learning strategies for the training of local voting-based classifiers that naturally retain these desirable properties, and hence active learning strategies for $k$-nearest neighbor classification have been conspicuously missing from the literature. In this work, we introduce a simple and intuitive active learning algorithm for the training of $k$-nearest neighbor classifiers, the first in the literature which retains the concept of the $k$-nearest neighbor vote at prediction time. We provide consistency guarantees for a modified $k$-nearest neighbors classifier trained on samples acquired via our scheme, and show that when the conditional probability function $\mathbb{P}(Y=y|X=x)$ is sufficiently smooth and the Tsybakov noise condition holds, our actively trained classifiers converge to the Bayes optimal classifier at a faster asymptotic rate than passively trained $k$-nearest neighbor classifiers.
Entity-Assisted Language Models for Identifying Check-worthy Sentences

We propose a new uniform framework for text classification and ranking that can automate the process of identifying check-worthy sentences in political debates and speech transcripts. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. In particular, we analyse the semantic meaning of each sentence using state-of-the-art neural language models such as BERT, ALBERT, and RoBERTa, while embeddings for entities are obtained from knowledge graph (KG) embedding models. Specifically, we instantiate our framework using five different language models, entity embeddings obtained from six different KG embedding models, as well as two combination methods leading to several Entity-Assisted neural language models. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF' 2019&2020 CheckThat! Labs. Our results show that the neural language models significantly outperform traditional TF.IDF and LSTM methods. In addition, we show that the ALBERT model is consistently the most effective model among all the tested neural language models. Our entity embeddings significantly outperform other existing approaches from the literature that are based on similarity and relatedness scores between the entities in a sentence, when used alongside a KG embedding.