Miracle-dz/UniFAR-FTUs huggingface dataset:
Sim2SG: Sim-to-Real Scene Graph Generation for Transfer Learning

Scene graph (SG) generation has been gaining a lot of traction recently. Current SG generation techniques, however, rely on the availability of expensive and limited number of labeled datasets. Synthetic data offers a viable alternative as labels are essentially free. However, neural network models trained on synthetic data, do not perform well on real data because of the domain gap. To overcome this challenge, we propose Sim2SG, a scalable technique for sim-to-real transfer for scene graph generation. Sim2SG addresses the domain gap by decomposing it into appearance, label and prediction discrepancies between the two domains. We handle these discrepancies by introducing pseudo statistic based self-learning and adversarial techniques. Sim2SG does not require costly supervision from the real-world dataset. Our experiments demonstrate significant improvements over baselines in reducing the domain gap both qualitatively and quantitatively. We validate our approach on toy simulators, as well as realistic simulators evaluated on real-world data.
Vehicle Reconstruction and Texture Estimation Using Deep Implicit Semantic Template Mapping

We introduce VERTEX, an effective solution to recover 3D shape and intrinsic texture of vehicles from uncalibrated monocular input in real-world street environments. To fully utilize the template prior of vehicles, we propose a novel geometry and texture joint representation, based on implicit semantic template mapping. Compared to existing representations which infer 3D texture distribution, our method explicitly constrains the texture distribution on the 2D surface of the template as well as avoids limitations of fixed resolution and topology. Moreover, by fusing the global and local features together, our approach is capable to generate consistent and detailed texture in both visible and invisible areas. We also contribute a new synthetic dataset containing 830 elaborate textured car models labeled with sparse key points and rendered using Physically Based Rendering (PBRT) system with measured HDRI skymaps to obtain highly realistic images. Experiments demonstrate the superior performance of our approach on both testing dataset and in-the-wild images. Furthermore, the presented technique enables additional applications such as 3D vehicle texture transfer and material identification.
The impact of magnetic fields on cosmological galaxy mergers – I. Reshaping gas and stellar discs

Mergers play an important role in galaxy evolution. In particular, major mergers are able to have a transformative effect on galaxy morphology. In this paper, we investigate the role of magnetic fields in gas-rich major mergers. To this end, we run a series of high-resolution magnetohydrodynamic (MHD) zoom-in simulations with the moving-mesh code arepo and compare the outcome with hydrodynamic simulations run from the same initial conditions. This is the first time that the effect of magnetic fields in major mergers has been investigated in a cosmologically consistent manner. In contrast to previous non-cosmological simulations, we find that the inclusion of magnetic fields has a substantial impact on the production of the merger remnant. Whilst magnetic fields do not strongly affect global properties, such as the star formation history, they are able to significantly influence structural properties. Indeed, MHD simulations consistently form remnants with extended discs and well-developed spiral structure, whilst hydrodynamic simulations form more compact remnants that display distinctive ring morphology. We support this work with a resolution study and show that whilst global properties are broadly converged across resolution and physics models, morphological differences only develop given sufficient resolution. We argue that this is due to the more efficient excitement of a small-scale dynamo in higher resolution simulations, resulting in a more strongly amplified field that is better able to influence gas dynamics.
An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples

We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variational circuit. The presented algorithm utilizes qRAM and other quantum circuits in the forward pass. Further, instead of the usual practice of computing the loss classically, we calculate the loss using a Swap-test circuit. The algorithm thus brings down the training cost of a variational classifier to O(logN)from the usual O(N)when training on a dataset of N samples. Although we discuss only binary classification in the paper, the algorithm can be easily generalized to multi-class classification.
OpenKBP: The open-access knowledge-based planning grand challenge

PURPOSE To advance fair and consistent comparisons of dose prediction methods for knowledge-based planning (KBP) in radiation therapy research. METHODS We hosted OpenKBP, a 2020 AAPM Grand Challenge, and challenged participants to develop the best method for predicting the dose of contoured computed tomography (CT) images. The models were evaluated according to two separate scores: (a) dose score, which evaluates the full three-dimensional (3D) dose distributions, and (b) dose-volume histogram (DVH) score, which evaluates a set DVH metrics. We used these scores to quantify the quality of the models based on their out-of-sample predictions. To develop and test their models, participants were given the data of 340 patients who were treated for head-and-neck cancer with radiation therapy. The data were partitioned into training ( n = 200 ), validation ( n = 40 ), and testing ( n = 100 ) datasets. All participants performed training and validation with the corresponding datasets during the first (validation) phase of the Challenge. In the second (testing) phase, the participants used their model on the testing data to quantify the out-of-sample performance, which was hidden from participants and used to determine the final competition ranking. Participants also responded to a survey to summarize their models. RESULTS The Challenge attracted 195 participants from 28 countries, and 73 of those participants formed 44 teams in the validation phase, which received a total of 1750 submissions. The testing phase garnered submissions from 28 of those teams, which represents 28 unique prediction methods. On average, over the course of the validation phase, participants improved the dose and DVH scores of their models by a factor of 2.7 and 5.7, respectively. In the testing phase one model achieved the best dose score (2.429) and DVH score (1.478), which were both significantly better than the dose score (2.564) and the DVH score (1.529) that was achieved by the runner-up models. Lastly, many of the top performing teams reported that they used generalizable techniques (e.g., ensembles) to achieve higher performance than their competition. CONCLUSION OpenKBP is the first competition for knowledge-based planning research. The Challenge helped launch the first platform that enables researchers to compare KBP prediction methods fairly and consistently using a large open-source dataset and standardized metrics. OpenKBP has also democratized KBP research by making it accessible to everyone, which should help accelerate the progress of KBP research. The OpenKBP datasets are available publicly to help benchmark future KBP research.
Homology Localization Through the Looking-Glass of Parameterized Complexity Theory

Finding a cycle of lowest weight that represents a homology class in a simplicial complex is known as homology localization (HL). Here we address this NP-complete problem using parameterized complexity theory. We show that it is W[1]-hard to approximate the HL problem when it is parameterized by solution size. We have also designed and implemented two algorithms based on treewidth solving the HL problem in FPT-time. Both algorithms are ETH-tight but our results shows that one outperforms the other in practice.
Detecting the pure triangle singularity effect through the 
ψ(2S)→pp¯η/pp¯π0
 process

In this work, the triangle singularity mechanism is investigated in the $\ensuremath{\psi}(2S)\ensuremath{\rightarrow}p\overline{p}\ensuremath{\eta}/p\overline{p}{\ensuremath{\pi}}^{0}$ process. The triangle loop composed by $J/\ensuremath{\psi}$, $\ensuremath{\eta}$, and $p$ has a singularity in the physical kinematic range for the $\ensuremath{\psi}(2S)\ensuremath{\rightarrow}p\overline{p}\ensuremath{\eta}/p\overline{p}{\ensuremath{\pi}}^{0}$ process, and it would generate a very narrow peak in the invariant mass spectrum of $p\ensuremath{\eta}(\ensuremath{\pi})$ around 1.56387 GeV, which is far away from both the threshold and relative resonances. In these processes, all the involved vertices are constrained by the experimental data. Thus, we can make a precise model independent prediction here. It turns out that the peak in the $p\ensuremath{\eta}$ invariant mass spectrum is visible, while it is very small in the $p{\ensuremath{\pi}}^{0}$ invariant mass spectrum. We expect this effect shown in $p\overline{p}\ensuremath{\eta}$ final state can be observed by the Beijing Spectrometer and Super Tau-Charm Facility in the future.
Convergence of Narasimhan–Simha measures on degenerating families of Riemann surfaces

Given a compact Riemann surface $Y$ and a positive integer $m$, Narasimhan and Simha defined a measure on $Y$ associated to the $m$-th tensor power of the canonical line bundle. We study the limit of this measure on holomorphic families of Riemann surfaces with semistable reduction. The convergence takes place on a hybrid space whose central fiber is the associated metrized curve complex in the sense of Amini and Baker. We also study the limit of the measure induced by the Hermitian pairing defined by the Narasimhan-Simha measure. For $m = 1$, both these measures coincide with the Bergman measure on $Y$. We also extend the definition of the Narasimhan-Simha measure to the singular curves on the boundary of $\overline{\mathcal{M}_g}$ in such a way that these measures form a continuous family of measures on the universal curve over $\overline{\mathcal{M}_g}$.
Necessity of weak subordination for some strongly subordinated Lévy processes

Abstract Consider the strong subordination of a multivariate Lévy process with a multivariate subordinator. If the subordinate is a stack of independent Lévy processes and the components of the subordinator are indistinguishable within each stack, then strong subordination produces a Lévy process; otherwise it may not. Weak subordination was introduced to extend strong subordination, always producing a Lévy process even when strong subordination does not. Here we prove that strong and weak subordination are equal in law under the aforementioned condition. In addition, we prove that if strong subordination is a Lévy process then it is necessarily equal in law to weak subordination in two cases: firstly when the subordinator is deterministic, and secondly when it is pure-jump with finite activity.
Time Series Change Point Detection with Self-Supervised Contrastive Predictive Coding

Change Point Detection (CPD) methods identify the times associated with changes in the trends and properties of time series data in order to describe the underlying behaviour of the system. For instance, detecting the changes and anomalies associated with web service usage, application usage or human behaviour can provide valuable insights for downstream modelling tasks. We propose a novel approach for self-supervised Time Series Change Point detection method based on Contrastive Predictive coding (TS − CP2). TS − CP2 is the first approach to employ a contrastive learning strategy for CPD by learning an embedded representation that separates pairs of embeddings of time adjacent intervals from pairs of interval embeddings separated across time. Through extensive experiments on three diverse, widely used time series datasets, we demonstrate that our method outperforms five state-of-the-art CPD methods, which include unsupervised and semi-supervised approaches. TS − CP2 is shown to improve the performance of methods that use either handcrafted statistical or temporal features by 79.4% and deep learning-based methods by 17.0% with respect to the F1-score averaged across the three datasets.
Sharp phase transitions for exact support recovery under local differential privacy.

We address the problem of variable selection in the Gaussian mean model in $\mathbb{R}^d$ under the additional constraint that only privatised data are available for inference. For this purpose, we adopt a recent generalisation of classical minimax theory to the framework of local $\alpha$-differential privacy. We provide lower and upper bounds on the rate of convergence for the expected Hamming loss over classes of at most $s$-sparse vectors whose non-zero coordinates are separated from $0$ by a constant $a>0$. As corollaries, we derive necessary and sufficient conditions (up to log factors) for exact recovery and for almost full recovery. When we restrict our attention to non-interactive mechanisms that act independently on each coordinate our lower bound shows that, contrary to the non-private setting, both exact and almost full recovery are impossible whatever the value of $a$ in the high-dimensional regime such that $n \alpha^2/ d^2\lesssim 1$. However, in the regime $n\alpha^2/d^2\gg \log(n\alpha^2/d^2)\log(d)$ we can exhibit a sharp critical value $a^*$ (up to a logarithmic factor) such that exact and almost full recovery are possible for all $a\gg a^*$ and impossible for $a\leq a^*$. We show that these results can be improved when allowing for all non-interactive (that act globally on all coordinates) locally $\alpha-$differentially private mechanisms in the sense that phase transitions occur at lower levels.
Clustering US States by Time Series of COVID-19 New Case Counts with Non-negative Matrix Factorization

The spreading pattern of COVID-19 differ a lot across the US states under different quarantine measures and reopening policies. We proposed to cluster the US states into distinct communities based on the daily new confirmed case counts via a nonnegative matrix factorization (NMF) followed by a k-means clustering procedure on the coefficients of the NMF basis. A cross-validation method was employed to select the rank of the NMF. Applying the method to the entire study period from March 22 to July 25, we clustered the 49 continental states (including District of Columbia) into 7 groups, two of which contained a single state. To investigate the dynamics of the clustering results over time, the same method was successively applied to the time periods with increment of one week, starting from the period of March 22 to March 28. The results suggested a change point in the clustering in the week starting on May 30, which might be explained by a combined impact of both quarantine measures and reopening policies.
Co-design of Optimal Transmission Power and Controller for Networked Control Systems Under State-dependent Markovian Channels

This paper considers a co-design problem for industrial networked control systems to ensure both the stability and efficiency properties of such systems. The assurance of such properties is particularly challenging due to the fact that wireless communications in industrial environments are not only subject to shadow fading but also stochastically correlated with their surrounding environments. To address such challenges, this paper first introduces a novel state-dependent Markov channel (SD-MC) model that explicitly captures the state-dependent features of industrial wireless communication systems by defining the proposed model's transition probabilities as a function of both its environment's states and transmission power. Under the proposed channel model, sufficient conditions on Maximum Allowable Transmission Interval (MATI) are presented to ensure both asymptotic stability in expectation and almost sure asymptotic stability properties of a continuous nonlinear control system with state-dependent fading channels. Based on such conditions, the co-design problem is then formulated as a constrained polynomial optimization problem (CPOP), which can be efficiently solved using semidefinite programming methods for the case of a two-state state dependent Markovian channel. The solutions to such a CPOP represent optimal control and power strategies that optimize the average expected joint costs in an infinite time horizon while still respect the stability constraints. For a general SD-MC model, this paper further shows that sub-optimal solutions can be obtained from linear programming formulations of the considered CPOP. Simulation results are given to illustrate the efficacy of the proposed co-design scheme.
Investigating Human Response, Behaviour, and Preference in Joint-Task Interaction

Human interaction relies on a wide range of signals, including non-verbal cues. In order to develop effective Explainable Planning (XAIP) agents it is important that we understand the range and utility of these communication channels. Our starting point is existing results from joint task interaction and their study in cognitive science. Our intention is that these lessons can inform the design of interaction agents -- including those using planning techniques -- whose behaviour is conditioned on the user's response, including affective measures of the user (i.e., explicitly incorporating the user's affective state within the planning model). We have identified several concepts at the intersection of plan-based agent behaviour and joint task interaction and have used these to design two agents: one reactive and the other partially predictive. We have designed an experiment in order to examine human behaviour and response as they interact with these agents. In this paper we present the designed study and the key questions that are being investigated. We also present the results from an empirical analysis where we examined the behaviour of the two agents for simulated users.
Hybrid quantum-classical classifier based on tensor network and variational quantum circuit

One key step in performing quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding. Traditional principle component analysis (PCA) and neural networks have been used to perform this task; however, the classical and quantum layers are usually trained separately. A framework that allows for a better integration of the two key components is thus highly desirable. Here we introduce a hybrid model combining the quantum-inspired tensor networks (TN) and the variational quantum circuits (VQC) to perform supervised learning tasks, which allows for an end-to-end training. We show that a matrix product state based TN with low bond dimensions performs better than PCA as a feature extractor to compress data for the input of VQCs in the binary classification of MNIST dataset. The architecture is highly adaptable and can easily incorporate extra quantum resource when available.
Unsupervised Optical Flow Using Cost Function Unrolling

Analyzing motion between two consecutive images is one of the fundamental tasks in computer vision. In the lack of labeled data, the loss functions are split into consistency and smoothness, allowing for self-supervised training. This paper focuses on the cost function derivation and presents an unrolling iterative approach, transferring the hard L1 smoothness constraint into a softer multi-layer iterative scheme. More accurate gradients, especially near non-differential positions, improve the network's convergence, providing superior results on tested scenarios. We report state-of-the-art results on both MPI Sintel and KITTI 2015 unsupervised optical flow benchmarks. The provided approach can be used to enhance various architectures and not limited just to the presented pipeline.
BOTD: Bold Outline Text Detector

Recently, text detection for arbitrary shape has attracted more and more search attention. Although segmentation-based methods, which are not limited by the text shape, have been studied to improve the performance, the slow detection speed, complicated post-processing, and text adhesion problem are still limitations for the practical application. In this paper, we propose a simple yet effective arbitrary-shape text detector, named Bold Outline Text Detector (BOTD). It is a novel one-stage detection framework with few post-processing processes. At the same time, the text adhesion problem can also be well alleviated. Specifically, BOTD first generates a center mask (CM) for each text instance, which makes the adhesive text easy to distinguish. Base on the CM, we further compute the polar minimum distance (PMD) for each text instance. PMD denotes the shortest distance between the center point of CM and the outline of the text instance. By dividing the text mask into CM and PMD, the outline of arbitrary-shape text instance can be obtained by simply predicting its CM and PMD. Without any bells and whistles, BOTD achieves an F-measure of 80.1% on CTW1500 with 52 FPS. Note that the post-processing time only accounts for 9% of the whole inference time. Code and trained models will be publicly available soon.
Quantitative Assessment of Adulteration and Reuse of Coconut Oil Using Transmittance Multispectral Imaging

Coconut oil known for its wide range of uses is often adulterated with other edible oils. Repeated use of coconut oil in food preparation could lead to many health issues. Existing methods available for evaluating quality of oil are laborious and time consuming. Therefore, we propose an imaging system hardware and image processing-based algorithm to estimate the adulteration of coconut oil with palm oil as the adulterant. A clear functional relationship between adulteration level and Bhattacharyya distance was observed as R2 = 0.9876 on the training samples. Thereafter, another algorithm is proposed to develop a spectral-clustering based classifier to determine the effect of reheat and reuse of coconut oil. Distinct clusters were obtained for different levels of reheated oil classes and the classification was performed with an accuracy of 0.983 on training samples. Further, the input images for the proposed algorithms were generated using an in-house developed transmittance based multispectral imaging system.
Mana in Haar-random states

Mana is a measure of the amount of non-Clifford resources required to create a state; the mana of a mixed state on $\ell$ qudits bounded by $\le \frac 1 2 (\ell \ln d - S_2)$; $S_2$ the state's second Renyi entropy. We compute the mana of Haar-random pure and mixed states and find that the mana is nearly logarithmic in Hilbert space dimension: that is, extensive in number of qudits and logarithmic in qudit dimension. In particular, the average mana of states with less-than-maximal entropy falls short of that maximum by $\ln \pi/2$. We then connect this result to recent work on near-Clifford approximate $t$-designs; in doing so we point out that mana is a useful measure of non-Clifford resources precisely because it is not differentiable.