Miracle-dz/UniFAR-FTUs huggingface dataset:
HRPose: Real-Time High-Resolution 6D Pose Estimation Network Using Knowledge Distillation

Real-time 6D object pose estimation is essential for many real-world applications, such as robotic grasping and augmented reality. To achieve an accurate object pose estimation from RGB images in real-time, we propose an effective and lightweight model, namely High-Resolution 6D Pose Estimation Network (HRPose). We adopt the efficient and small HRNetV2-W18 as a feature extractor to reduce computational burdens while generating accurate 6D poses. With only 33\% of the model size and lower computational costs, our HRPose achieves comparable performance compared with state-of-the-art models. Moreover, by transferring knowledge from a large model to our proposed HRPose through output and feature-similarity distillations, the performance of our HRPose is improved in effectiveness and efficiency. Numerical experiments on the widely-used benchmark LINEMOD demonstrate the superiority of our proposed HRPose against state-of-the-art methods.
A Survey on Neural Abstractive Summarization Methods and Factual Consistency of Summarization

Automatic summarization is the process of shortening a set of textual data computationally, to create a subset (a summary) that represents the most important pieces of information in the original text. Existing summarization methods can be roughly divided into two types: extractive and abstractive. An extractive summarizer explicitly selects text snippets (words, phrases, sentences, etc.) from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.
Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning

Recently, model-based agents have achieved better performance than model-free ones using the same computational budget and training time in single-agent environments. However, due to the complexity of multi-agent systems, it is tough to learn the model of the environment. The significant compounding error may hinder the learning process when model-based methods are applied to multi-agent tasks. This paper proposes an implicit model-based multi-agent reinforcement learning method based on value decomposition methods. Under this method, agents can interact with the learned virtual environment and evaluate the current state value according to imagined future states in the latent space, making agents have the foresight. Our approach can be applied to any multi-agent value decomposition method. The experimental results show that our method improves the sample efficiency in different partially observable Markov decision process domains.
Single SiGe quantum dot emission deterministically enhanced in a high-Q photonic crystal resonator.

We report the resonantly enhanced radiative emission from a single SiGe quantum dot (QD), which is deterministically embedded into a bichromatic photonic crystal resonator (PhCR) at the position of its largest modal electric field by a scalable method. By optimizing our molecular beam epitaxy (MBE) growth technique, we were able to reduce the amount of Ge within the whole resonator to obtain an absolute minimum of exactly one QD, accurately positioned by lithographic methods relative to the PhCR, and an otherwise flat, a few monolayer thin, Ge wetting layer (WL). With this method, record quality (Q) factors for QD-loaded PhCRs up to Q ∼ 105 are achieved. A comparison with control PhCRs on samples containing a WL but no QDs is presented, as well as a detailed analysis of the dependence of the resonator-coupled emission on temperature, excitation intensity, and emission decay after pulsed excitation. Our findings undoubtedly confirm a single QD in the center of the resonator as a potentially novel photon source in the telecom spectral range.
Thermal friction as a solution to the Hubble and large-scale structure tensions

Thermal friction offers a promising solution to the Hubble and the large-scale structure (LSS) tensions. This additional friction acts on a scalar field in the early universe and extracts its energy density into dark radiation, the cumulative effect being similar to that of an early dark energy (EDE) scenario. The dark radiation automatically redshifts at the minimal necessary rate to improve the Hubble tension. On the other hand, the addition of extra radiation to the Universe can improve the LSS tension. We explore this model in light of cosmic microwave background (CMB), baryon acoustic oscillation and supernova data, including the SH0ES $H_0$ measurement and the Dark Energy Survey Y1 data release in our analysis. Our results indicate a preference for the regime where the scalar field converts to dark radiation at very high redshifts, asymptoting effectively to an extra self-interacting radiation species rather than an EDE-like injection. In this limit, thermal friction can ease both the Hubble and the LSS tensions, but not resolve them. We find the source of this preference to be the incompatibility of the CMB data with the linear density perturbations of the dark radiation when injected at redshifts close to matter-radiation equality.
Identifying organizations receiving personal data in Android Apps

: Many studies have demonstrated that mobile applications are common means to collect massive amounts of personal data. This goes unnoticed by most users, who are also unaware that many different organizations are receiving this data, even from multiple apps in parallel. This paper assesses different techniques to identify the organizations that are receiving personal data flows in the Android ecosystem, namely the WHOIS service, SSL certificates inspection, and privacy policy textual analysis. Based on our findings, we propose a fully automated method that combines the most successful techniques, achieving a 94.73% precision score in identifying the recipient organization. We further demonstrate our method by evaluating 1,000 Android apps and exposing the corporations that collect the users’ personal data.
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive?

We study the theory of neural network (NN) from the lens of classical nonparametric regression problems with a focus on NN's ability to adaptively estimate functions with heterogeneous smoothness -- a property of functions in Besov or Bounded Variation (BV) classes. Existing work on this problem requires tuning the NN architecture based on the function spaces and sample sizes. We consider a"Parallel NN"variant of deep ReLU networks and show that the standard weight decay is equivalent to promoting the $\ell_p$-sparsity ($0<p<1$) of the coefficient vector of an end-to-end learned function bases, i.e., a dictionary. Using this equivalence, we further establish that by tuning only the weight decay, such Parallel NN achieves an estimation error arbitrarily close to the minimax rates for both the Besov and BV classes. Notably, it gets exponentially closer to minimax optimal as the NN gets deeper. Our research sheds new lights on why depth matters and how NNs are more powerful than kernel methods.
A Mobile Food Recognition System for Dietary Assessment

Food recognition is an important task for a variety of applications, including managing health conditions and assisting visually impaired people. Several food recognition studies have focused on generic types of food or specific cuisines, however, food recognition with respect to Middle Eastern cuisines has remained unexplored. Therefore, in this paper we focus on developing a mobile friendly, Middle Eastern cuisine focused food recognition application for assisted living purposes. In order to enable a low-latency, high-accuracy food classification system, we opted to utilize the Mobilenet-v2 deep learning model. As some of the foods are more popular than the others, the number of samples per class in the used Middle Eastern food dataset is relatively imbalanced. To compensate for this problem, data augmentation methods are applied on the underrepresented classes. Experimental results show that using Mobilenet-v2 architecture for this task is beneficial in terms of both accuracy and the memory usage. With the model achieving 94% accuracy on 23 food classes, the developed mobile application has potential to serve the visually impaired in automatic food recognition via images.
Unleashing the Power of Compiler Intermediate Representation to Enhance Neural Program Embeddings

Neural program embeddings have demonstrated considerable promise in a range of program analysis tasks, including clone identification, program repair, code completion, and program synthesis. However, most existing methods generate neural program embeddings di-rectly from the program source codes, by learning from features such as tokens, abstract syntax trees, and control flow graphs. This paper takes a fresh look at how to improve program embed-dings by leveraging compiler intermediate representation (IR). We first demonstrate simple yet highly effective methods for enhancing embedding quality by training embedding models alongside source code and LLVM IR generated by default optimization levels (e.g., -02). We then introduce IRGEN, a framework based on genetic algorithms (GA), to identify (near-)optimal sequences of optimization flags that can significantly improve embedding quality. We use IRGEN to find optimal sequences of LLVM optimization flags by performing GA on source code datasets. We then extend a popular code embedding model, CodeCMR, by adding a new objective based on triplet loss to enable a joint learning over source code and LLVM IR. We benchmark the quality of embedding using a rep-resentative downstream application, code clone detection. When CodeCMR was trained with source code and LLVM IRs optimized by findings of IRGEN, the embedding quality was significantly im-proved, outperforming the state-of-the-art model, CodeBERT, which was trained only with source code. Our augmented CodeCMR also outperformed CodeCMR trained over source code and IR optimized with default optimization levels. We investigate the properties of optimization flags that increase embedding quality, demonstrate IRGEN's generalization in boosting other embedding models, and establish IRGEN's use in settings with extremely limited training data. Our research and findings demonstrate that a straightforward addition to modern neural code embedding models can provide a highly effective enhancement.
Exploring Continuous Integrate-and-Fire for Efﬁcient and Adaptive Simultaneous Speech Translation

Simultaneous speech translation (SimulST) is a challenging task that aims to directly translate streaming speech before the complete input is observed. A SimulST system generally includes two important components: the pre-decision that aggregates the speech information, and the policy that decides read or write. While recent works had proposed a variety of strategies to improve the pre-decision, they mostly adopt the ﬁxed wait-k policy. The adaptive policies are rarely explored. We propose to model the adaptive policy using the Continuous Integrate-and-Fire (CIF). In our proposed model, the CIF is not only responsible for aggregating speech information, but also deciding when to read or write. To adapt the CIF to SimulST task, we propose two modiﬁcations: a token-level quantity loss or an in-ﬁnite lookback attention. We show that our model can learn an adaptive policy effectively, achieving comparable or superior performance to MMA at lower latency, while being more efﬁcient to train.
Entropy-based Stability-Plasticity for Lifelong Learning

The ability to continuously learn remains elusive for deep learning models. Unlike humans, models cannot accumulate knowledge in their weights when learning new tasks, mainly due to an excess of plasticity and the low incentive to reuse weights when training a new task. To address the stability-plasticity dilemma in neural networks, we propose a novel method called Entropy-based Stability-Plasticity (ESP). Our approach can decide dynamically how much each model layer should be modified via a plasticity factor. We incorporate branch layers and an entropy-based criterion into the model to find such factor. Our experiments in the domains of natural language and vision show the effectiveness of our approach in leveraging prior knowledge by reducing interference. Also, in some cases, it is possible to freeze layers during training leading to speed up in training.
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation

Domain Adaptation (DA) of Neural Machine Translation (NMT) model often relies on a pre-trained general NMT model which is adapted to the new domain on a sample of in-domain parallel data. Without parallel data, there is no way to estimate the potential benefit of DA, nor the amount of parallel samples it would require. It is however a desirable functionality that could help MT practitioners to make an informed decision before investing resources in dataset creation. We propose a Domain adaptation Learning Curve prediction (DaLC) model that predicts prospective DA performance based on in-domain monolingual samples in the source language. Our model relies on the NMT encoder representations combined with various instance and corpus-level features. We demonstrate that instance-level is better able to distinguish between different domains compared to corpus-level frameworks proposed in previous studies Finally, we perform in-depth analyses of the results highlighting the limitations of our approach, and provide directions for future research.
Shape Anisotropy Effect on Magnetic Domain Wall Dynamics in Nanowires Under Thermal Gradient

We investigate the magnetic-domain wall (DW) dynamics in uniaxial ∕ biaxial-nanowires under a ther- mal gradient (TG). The ﬁndings reveal that the DW propagates toward the hotter region in both nanowires. Inuniaxial-nanowire, theDWpropagatesaccompanyingarotationoftheDW-plane. Inbi- axialnanowire, DWpropagatesin thehotterregion, andtheso-calledWalkerbreakdownphenomenon is observed. The main physics of such DW dynamics is the magnonic angular momentum transfer to the DW. The hard (shape) anisotropy exists in biaxial-nanowire, which contributes an additional torque; hence DW speed is larger than that in uniaxial-nanowire. But rotational speed is lower ini- tially as hard anisotropy suppresses the DW-rotation. After certain TG, DW-plane overcomes the hard anisotropy; thus, the rotational speed increases again. DW dynamics show a decreasing trend with the damping since the magnon propagation length decreases. Therefore, the above ﬁndings might be useful to realize the spintronics (i.e., fast racetrack memory) devices.
Indiscriminate Data Poisoning Attacks on Neural Networks

Data poisoning attacks, in which a malicious adversary aims to influence a model by injecting"poisoned"data into the training process, have attracted significant recent attention. In this work, we take a closer look at existing poisoning attacks and connect them with old and new algorithms for solving sequential Stackelberg games. By choosing an appropriate loss function for the attacker and optimizing with algorithms that exploit second-order information, we design poisoning attacks that are effective on neural networks. We present efficient implementations that exploit modern auto-differentiation packages and allow simultaneous and coordinated generation of tens of thousands of poisoned points, in contrast to existing methods that generate poisoned points one by one. We further perform extensive experiments that empirically explore the effect of data poisoning attacks on deep neural networks.
QUADRATICS GENERICALLY IDENTIFIABLE UP TO QUADRATIC RANK

We consider the inverse problem for the map which captures the moment problem for mixtures of centered Gaussians in the smallest interesting degree. We show that for any n ∈ N , this map is generically one-to-one (up to permutations of q 1 ,.,q m ) as long as m ≤ (cid:0) n 2 (cid:1) + 1, thus proving generic identiﬁability for mixtures of centered Gaussians from their (exact) moments of degree at most 6 up to rank (cid:0) n 2 (cid:1) + 1. We rely on the study of tangent spaces of secant varieties and the contact locus.
Estimating probabilistic dynamic origin-destination demands using multi-day traffic data on computational graphs

System-level decision making in transportation needs to understand day-to-day variation of network flows, which calls for accurate modeling and estimation of probabilistic dynamic travel demand on networks. Most existing studies estimate deterministic dynamic origin-destination (OD) demand, while the day-to-day variation of demand and flow is overlooked. Estimating probabilistic distributions of dynamic OD demand is challenging due to the complexity of the spatio-temporal networks and the computational intensity of the high-dimensional problems. With the availability of massive traffic data and the emergence of advanced computational methods, this paper develops a data-driven framework that solves the probabilistic dynamic origin-destination demand estimation (PDODE) problem using multi-day data. Different statistical distances (e.g., lp-norm, Wasserstein distance, KL divergence, Bhattacharyya distance) are used and compared to measure the gap between the estimated and the observed traffic conditions, and it is found that 2-Wasserstein distance achieves a balanced accuracy in estimating both mean and standard deviation. The proposed framework is cast into the computational graph and a reparametrization trick is developed to estimate the mean and standard deviation of the probabilistic dynamic OD demand simultaneously. We demonstrate the effectiveness and efficiency of the proposed PDODE framework on both small and real-world networks. In particular, it is demonstrated that the proposed PDODE framework can mitigate the overfitting issues by considering the demand variation. Overall, the developed PDODE framework provides a practical tool for public agencies to understand the sources of demand stochasticity, evaluate day-to-day variation of network flow, and make reliable decisions for intelligent transportation systems.
A random Hall-Paige conjecture

A complete mapping of a group $G$ is a bijection $\phi\colon G\to G$ such that $x\mapsto x\phi(x)$ is also bijective. Hall and Paige conjectured in 1955 that a finite group $G$ has a complete mapping whenever $\prod_{x\in G} x$ is the identity in the abelianization of $G$. This was confirmed in 2009 by Wilcox, Evans, and Bray with a proof using the classification of finite simple groups. \par In this paper, we give a combinatorial proof of a far-reaching generalisation of the Hall-Paige conjecture for large groups. We show that for random-like and equal-sized subsets $A,B,C$ of a group $G$, there exists a bijection $\phi\colon A\to B$ such that $x\mapsto x\phi(x)$ is a bijection from $A$ to $C$ whenever $\prod_{a\in A} a \prod_{b\in B} b=\prod_{c\in C} c$ in the abelianization of $G$. We use this statement as a black-box to settle the following old problems in combinatorial group theory for large groups. (1) We characterise sequenceable groups, that is, groups which admit a permutation $\pi$ of their elements such that the partial products $\pi_1$, $\pi_1\pi_2$, $\pi_1\pi_2\cdots \pi_n$ are all distinct. This resolves a problem of Gordon from 1961 and confirms conjectures made by several authors, including Keedwell's 1981 conjecture that all large non-abelian groups are sequenceable. We also characterise the related $R$-sequenceable groups, addressing a problem of Ringel from 1974. (2) We confirm in a strong form a conjecture of Snevily from 1999 by characterising large subsquares of multiplication tables of finite groups that admit transversals. Previously, this characterisation was known only for abelian groups of odd order (by a combination of papers by Alon and Dasgupta-K\'arolyi-Serra-Szegedy and Arsovski).
Model Reduction via Dynamic Mode Decomposition

This work proposes a new framework of model reduction for parametric complex systems. The framework employs a popular model reduction technique dynamic mode decomposition (DMD), which is capable of combining data-driven learning and physics ingredients based on the Koopman operator theory. In the oﬄine step of the proposed framework, DMD constructs a low-rank linear surrogate model for the high dimensional quantities of interest (QoIs) derived from the (nonlinear) complex high ﬁdelity models (HFMs) of unknown forms. Then in the online step, the resulting local reduced order bases (ROBs) and parametric reduced order models (PROMs) at the training parameter sample points are interpolated to construct a new PROM with the corresponding ROB for a new set of target/test parameter values. The interpolations need to be done on the appropriate manifolds within consistent sets of generalized coordinates. The proposed framework is illustrated by numerical examples for both linear and nonlinear problems. In particular, its advantages in computational costs and accuracy are demonstrated by the comparisons with projection-based proper orthogonal decomposition (POD)-PROM and Kriging.
The origin of UV/optical emission in the black hole low-mass X-ray binary Swift J1753.5-0127

The emission from the accreting black holes (BHs) in low-mass X-ray binaries (LMXBs) covers a broad energy band from radio to X-rays. Studying the correlations between emission in diﬀerent energy bands during outbursts can provide valuable information about the accretion process. We analyse the simultaneous optical, ultraviolet (UV) and X-ray data of the BH-LMXB Swift J1753.5-0127 during its ∼ 12 − year long outburst with the Neil Gehrels Swift Observatory . We ﬁnd that the UV/optical and X-ray emission are strongly correlated during the hard states of the outburst. We ﬁt the relation with a power-law function 𝐹 UV / optical ∝ 𝐹 𝛽𝑋 and ﬁnd that the power-law index 𝛽 increases from ∼ 0 . 24 to ∼ 0 . 33 as the UV/optical wavelength decreases from ∼ 5400 Å (V) to ∼ 2030 Å (UVW2). We explore the possible reasons for this and suggest that in Swift J1753.5-0127 the UV/optical emission is dominated by a viscously heated accretion disc at large radii. We ﬁnd that the data that deviate from the correlation correspond to the low-intensity peaks appeared in the X-ray band during the outburst, and suggest that these deviations are driven by the emission from the inner part of the accretion disc.
4D-MultispectralNet: Multispectral Stereoscopic Disparity Estimation using Human Masks

Multispectral stereoscopy is an emerging field. A lot of work has been done in classical stereoscopy, but multispectral stereoscopy is not studied as frequently. This type of stereoscopy can be used in autonomous vehicles to complete the information given by RGB cameras. It helps to identify objects in the surroundings when the conditions are more difficult, such as in night scenes. This paper focuses on the RGB-LWIR spectrum. RGB-LWIR stereoscopy has the same challenges as classical stereoscopy, that is occlusions, textureless surfaces and repetitive patterns, plus specific ones related to the different modalities. Finding matches between two spectrums adds another layer of complexity. Color, texture and shapes are more likely to vary from a spectrum to another. To address this additional challenge, this paper focuses on estimating the disparity of people present in a scene. Given the fact that people's shape is captured in both RGB and LWIR, we propose a novel method that uses segmentation masks of the human in both spectrum and than concatenate them to the original images before the first layer of a Siamese Network. This method helps to improve the accuracy, particularly within the one pixel error range.