Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Scene Decoration from a Single Photograph

Furnishing and rendering indoor scenes has been a long-standing task for interior design, where artists create a conceptual design for the space, build a 3D model of the space, decorate, and then perform rendering. Although the task is important, it is tedious and requires tremendous effort. In this paper, we introduce a new problem of domain-specific indoor scene image synthesis, namely neural scene decoration. Given a photograph of an empty indoor space and a list of decorations with layout determined by user, we aim to synthesize a new image of the same space with desired furnishing and decorations. Neural scene decoration can be applied to create conceptual interior designs in a simple yet effective manner. Our attempt to this research problem is a novel scene generation architecture that transforms an empty scene and an object layout into a realistic furnished scene photograph. We demonstrate the performance of our proposed method by comparing it with conditional image synthesis baselines built upon prevailing image translation approaches both qualitatively and quantitatively. We conduct extensive experiments to further validate the plausibility and aesthetics of our generated scenes. Our implementation is available at \url{https://github.com/hkust-vgd/neural_scene_decoration}.
An Exhaustive Approach to Detecting Transient Execution Side Channels in RTL Designs of Processors

Hardware (HW) security issues have been emerging at an alarming rate in recent years. Transient execution attacks, such as Spectre and Meltdown, in particular, pose a genuine threat to the security of modern computing systems. Despite recent advances, understanding the intricate implications of microarchitectural design decisions on processor security remains a great challenge and has caused a number of update cycles in the past. This papers addresses the need for a new approach to HW sign-off verification which guarantees the security of processors at the Register Transfer Level (RTL). To this end, we introduce a formal definition of security with respect to transient execution attacks, formulated as a HW property. We present a formal proof methodology based on Unique Program Execution Checking (UPEC) which can be used to systematically detect all vulnerabilities to transient execution attacks in RTL designs. UPEC does not exploit any a priori knowledge on known attacks and can therefore detect also vulnerabilities based on new, so far unknown, types of channels. This is demonstrated by two new attack scenarios discovered in our experiments with UPEC. UPEC scales to a wide range of HW designs, including in-order processors (RocketChip), pipelines with out-of-order writeback (Ariane), and processors with deep out-of-order speculative execution (BOOM). To the best of our knowledge, UPEC is the first RTL verification technique that exhaustively covers transient execution side channels in processors of realistic complexity.
On simulation of rough Volterra stochastic volatility models

Rough Volterra volatility models are a progressive and promising ﬁeld of research in derivative pricing. Although rough fractional stochastic volatility models already proved to be superior in real market data ﬁtting, techniques used in simulation of these models are still ineﬃcient in terms of speed and accuracy. This paper aims to present accurate and eﬃcient tools and techniques for Monte-Carlo simulations for a wide range of rough volatility models. In particular, we compare three commonly used simulation methods: the Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on the implementation of variance reduction techniques. In particular, we show the obstacles of the so-called turbocharging technique whose performance is sometimes counter-productive. To overcome these obstacles, we suggest several modiﬁcations.
Current-induced breakdown of the quantum anomalous Hall effect

The quantum anomalous Hall eﬀect (QAHE) realizes dissipationless longitudinal resistivity and quantized Hall resistance without the need of an external magnetic ﬁeld. However, when reducing the device dimensions or increasing the current density, an abrupt breakdown of the dissipationless state occurs with a relatively small critical current, limiting the applications of the QAHE. We investigate the mechanism of this breakdown by studying multi-terminal devices and identiﬁed that the electric ﬁeld created between opposing chiral edge states lies at the origin. We propose that electric-ﬁeld-driven percolation of two-dimensional charge puddles in the gapped surface states of compensated topological-insulator ﬁlms is the most likely cause of the breakdown.
Essential Features: Content-Adaptive Pixel Discretization to Improve Model Robustness to Adaptive Adversarial Attacks

—To remove the effects of adversarial perturbations, preprocessing defenses such as pixel discretization are appealing due to their simplicity but have so far been shown to be ineffective except on simple datasets such as MNIST, leading to the belief that pixel discretization approaches are doomed to failure as a defense technique. This paper revisits the pixel discretization approaches. We hypothesize that the reason why existing approaches have failed is that they have used a ﬁxed codebook for the entire dataset. In particular, we ﬁnd that can lead to situations where images become more susceptible to adversarial perturbations and also suffer signiﬁcant loss of accuracy after discretization. We propose a novel image preprocessing technique called Essential Features that uses an adaptive codebook that is based on per-image content and threat model. Essential Features adaptively selects a separable set of color clusters for each image to reduce the color space while preserving the pertinent features of the original image, maximizing both separability and representation of colors. Additionally, to limit the adversary’s ability to inﬂuence the chosen color clusters, Essential Features takes advantage of spatial correlation with an adaptive blur that moves pixels closer to their original value without destroying original edge information. We design several adaptive attacks and ﬁnd that our approach is more robust than previous baselines on L ∞ and L 2 bounded attacks for several challenging datasets including CIFAR-10, GTSRB, RESISC45, and ImageNet.
Unveiling the dynamics of optical frequency combs from phase-amplitude correlations

M. Ansquer, V. Thiel, S. De, B. Argence, G. Gredat, F. Bretenaker, and N. Treps ∗ Laboratoire Kastler Brossel, Sorbonne Université, ENS-Université PSL, CNRS, Collège de France, 4 place Jussieu, 75252 Paris, France Department of Physics and Oregon Center for Optical, Molecular, and Quantum Science, University of Oregon, Eugene, Oregon 97403, USA Integrated Quantum Optics Group, Applied Physics, 100 Warburger Straße, Paderborn University, Paderborn 33098, Germany Université Paris-Saclay, CNRS, ENS Paris-Saclay, CentraleSupélec, LuMIn, Gif-sur-Yvette, France (Dated: April 27, 2021)
Automatic hemisphere segmentation in rodent MRI with lesions

We present MedicDeepLabv3+, a convolutional neural network that is the ﬁrst completely automatic method to segment brain hemispheres in magnetic resonance (MR) images of rodents with lesions. MedicDeepLabv3+ improves the state-of-the-art DeepLabv3+ with an advanced decoder, incorporating spatial attention layers and additional skip connections that, as we show in our experiments, lead to more precise segmentations. MedicDeepLabv3+ requires no MR image preprocessing, such as bias-ﬁeld correction or registration to a template, produces segmentations in less than a second, and its GPU memory requirements can be adjusted based on the available resources. Using a large dataset of 723 MR rat brain images, we evaluated our MedicDeepLabv3+, two state-of-the-art convolutional neural networks (DeepLabv3+, UNet) and three approaches that were speciﬁcally designed for skull-stripping rodent MR images (Demon, RATS and RBET). In our experiments, MedicDeepLabv3+ outperformed the other methods, yielding an average Dice coefﬁcient of 0.952 and 0.944 in the brain and contralateral hemisphere regions. Additionally, we show that despite limiting the GPU memory and the training data to only three images, our MedicDeepLabv3+ also provided satisfactory segmentations. In conclusion, our method, publicly available at https://github.com/jmlipman/MedicDeepLabv3Plus, yielded excellent results in multiple scenarios, demonstrating its capability to reduce human workload in rodent neuroimaging studies.
Video Similarity and Alignment Learning on Partial Video Copy Detection

Existing video copy detection methods generally measure video similarity based on spatial similarities between key frames, neglecting the latent similarity in temporal dimension, so that the video similarity is biased towards spatial information. There are methods modeling unified video similarity in an end-to-end way, but losing detailed partial alignment information, which causes the incapability of copy segments localization. To address the above issues, we propose the Video Similarity and Alignment Learning (VSAL) approach, which jointly models spatial similarity, temporal similarity and partial alignment. To mitigate the spatial similarity bias, we model the temporal similarity as the mask map predicted from frame-level spatial similarity, where each element indicates the probability of frame pair lying right on the partial alignments. To further localize partial copies, the step map is learned from the spatial similarity where the elements indicate extending directions of the current partial alignments on the spatial-temporal similarity map. Obtained from the mask map, the start points extend out into partial optimal alignments following instructions of the step map. With the similarity and alignment learning strategy, VSAL achieves the state-of-the-art F1-score on VCDB core dataset. Furthermore, we construct a new benchmark of partial video copy detection and localization by adding new segment-level annotations for FIVR-200k dataset, where VSAL also achieves the best performance, verifying its effectiveness in more challenging situations. Our project is publicly available at https://pvcd-vsal.github. io/vsal/.
Statistical Analysis of Wasserstein Distributionally Robust Estimators

We consider statistical methods which invoke a min-max distributionally robust formulation to extract good out-of-sample performance in data-driven optimization and learning problems. Acknowledging the distributional uncertainty in learning from limited samples, the min-max formulations introduce an adversarial inner player to explore unseen covariate data. The resulting Distributionally Robust Optimization (DRO) formulations, which include Wasserstein DRO formulations (our main focus), are specified using optimal transportation phenomena. Upon describing how these infinite-dimensional min-max problems can be approached via a finite-dimensional dual reformulation, the tutorial moves into its main component, namely, explaining a generic recipe for optimally selecting the size of the adversary's budget. This is achieved by studying the limit behavior of an optimal transport projection formulation arising from an inquiry on the smallest confidence region that includes the unknown population risk minimizer. Incidentally, this systematic prescription coincides with those in specific examples in high-dimensional statistics and results in error bounds that are free from the curse of dimensions. Equipped with this prescription, we present a central limit theorem for the DRO estimator and provide a recipe for constructing compatible confidence regions that are useful for uncertainty quantification. The rest of the tutorial is devoted to insights into the nature of the optimizers selected by the min-max formulations and additional applications of optimal transport projections.
Parallelized Reverse Curriculum Generation

For reinforcement learning (RL), it is challenging for an agent to master a task that requires a specific series of actions due to sparse rewards. To solve this problem, reverse curriculum generation (RCG) provides a reverse expansion approach that automatically generates a curriculum for the agent to learn. More specifically, RCG adapts the initial state distribution from the neighborhood of a goal to a distance as training proceeds. However, the initial state distribution generated for each iteration might be biased, thus making the policy overfit or slowing down the reverse expansion rate. While training RCG for actor-critic (AC) based RL algorithms, this poor generalization and slow convergence might be induced by the tight coupling between an AC pair. Therefore, we propose a parallelized approach that simultaneously trains multiple AC pairs and periodically exchanges their critics. We empirically demonstrate that this proposed approach can improve RCG in performance and convergence, and it can also be applied to other AC based RL algorithms with adapted initial state distribution.
Multi-Round Parsing-based Multiword Rules for Scientific OpenIE

Information extraction (IE) in scientific literature has facilitated many down-stream tasks. OpenIE, which does not require any relation schema but identifies a relational phrase to describe the relationship between a subject and an object, is being a trending topic of IE in sciences. The subjects, objects, and relations are often multiword expressions, which brings challenges for methods to identify the boundaries of the expressions given very limited or even no training data. In this work, we present a set of rules for extracting structured information based on dependency parsing that can be applied to any scientific dataset requiring no expert's annotation. Results on novel datasets show the effectiveness of the proposed method. We discuss negative results as well.
Model-Based Opponent Modeling

When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before. Modeling the behaviors, goals, or beliefs of opponents could help the agent adjust its policy to adapt to different opponents. In addition, it is also important to consider opponents who are learning simultaneously or capable of reasoning. However, existing work usually tackles only one of the aforementioned types of opponents. In this paper, we propose model-based opponent modeling (MBOM), which employs the environment model to adapt to all kinds of opponents. MBOM simulates the recursive reasoning process in the environment model and imagines a set of improving opponent policies. To effectively and accurately represent the opponent policy, MBOM further mixes the imagined opponent policies according to the similarity with the real behaviors of opponents. Empirically, we show that MBOM achieves more effective adaptation than existing methods in a variety of tasks, respectively with different types of opponents, i.e., fixed policy, na\"ive learner, and reasoning learner.
Robustness of convolutional neural networks to physiological electrocardiogram noise

The electrocardiogram (ECG) is a widespread diagnostic tool in healthcare and supports the diagnosis of cardiovascular disorders. Deep learning methods are a successful and popular technique to detect indications of disorders from an ECG signal. However, there are open questions around the robustness of these methods to various factors, including physiological ECG noise. In this study, we generate clean and noisy versions of an ECG dataset before applying symmetric projection attractor reconstruction (SPAR) and scalogram image transformations. A convolutional neural network is used to classify these image transforms. For the clean ECG dataset, F1 scores for SPAR attractor and scalogram transforms were 0.70 and 0.79, respectively. Scores decreased by less than 0.05 for the noisy ECG datasets. Notably, when the network trained on clean data was used to classify the noisy datasets, performance decreases of up to 0.18 in F1 scores were seen. However, when the network trained on the noisy data was used to classify the clean dataset, the decrease was less than 0.05. We conclude that physiological ECG noise impacts classification using deep learning methods and careful consideration should be given to the inclusion of noisy ECG signals in the training data when developing supervised networks for ECG classification. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.
Backscatter Communication System With Dumb Diffusing Surface

Ambient backscatter communications have been identified for ultra-low energy wireless communications. Indeed, a tag can send a message to a reader without emitting any wave and without battery, simply by backscattering the waves generated by a source. In the simplest implementation of such a system, the tag sends a binary message by oscillating between two states and the reader detects the bits by comparing the two distinct received powers. In this paper, for the first time, we propose to study an ambient backscatter communication system, in the presence of a diffusing surface, a simple flat panel that diffuses in all directions. We establish the analytical closed form expression of the power contrast in the presence of the surface. We show that the diffusing surface improves the power contrast. Moreover our approach allows us to express the contrast to noise ratio, and therefore to establish the BER performance. Furthermore, we derive the optimum source transmit power for a given target power contrast. This makes it possible to quantify the amount of energy that can be saved at the source side, thanks to the diffusing surface.
MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease

Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms. Recently, generative adversarial networks (GANs) with the ability to synthesize realist images have shown great potential as an alternative to standard data augmentation techniques. Our work focuses on cross-modality synthesis of fluorodeoxyglucose~(FDG) Positron Emission Tomography~(PET) scans from structural Magnetic Resonance~(MR) images using generative models to facilitate multi-modal diagnosis of Alzheimer's disease (AD). Specifically, we propose a novel end-to-end, globally and locally aware image-to-image translation GAN (GLA-GAN) with a multi-path architecture that enforces both global structural integrity and fidelity to local details. We further supplement the standard adversarial loss with voxel-level intensity, multi-scale structural similarity (MS-SSIM) and region-of-interest (ROI) based loss components that reduce reconstruction error, enforce structural consistency at different scales and perceive variation in regional sensitivity to AD respectively. Experimental results demonstrate that our GLA-GAN not only generates synthesized FDG-PET scans with enhanced image quality but also superior clinical utility in improving AD diagnosis compared to state-of-the-art models. Finally, we attempt to interpret some of the internal units of the GAN that are closely related to this specific cross-modality generation task.
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning

Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward. Deep reinforcement learning provides good opportunity to model the complexity of portfolio choice in high-dimensional and data-driven environment by leveraging the powerful representation of deep neural networks. In this paper, we build a portfolio management system using direct deep reinforcement learning to make optimal portfolio choice periodically among S\&P500 underlying stocks by learning a good factor representation (as input). The result shows that an effective learning of market conditions and optimal portfolio allocations can significantly outperform the average market.
TreCap: A wearable device to measure and assess tremor data of visually guided hand movements in real time

The assessment and treatment of motor symptoms such as tremor in Parkinson's disease depends exclusively on the physician’s visual observation of standardised movements (i.e. motor tasks). Wearable sensors such as accelerometers are able to detect some manifestations of these pathological signs in movement disorders. Sensor data from motor tasks, however, must be processed sequentially with annotated data from clinical experts. Hence, we designed TreCap, a custombuilt wearable device with new software to capture and evaluate motor symptoms such as tremor in real time. Inertial sensor data is systematically processed, stored and tailored to each motor task by this software, including annotated data from clinical rating scores and deep brain stimulation parameters. For prototype testing, the wearable device was validated in a pilot study on subjects with physiological hand tremor. The processed data sets are suitable for machine learning to classify motor tasks. Results on healthy subjects demonstrate an accuracy of 95% with support vector machine algorithms. TreCap software is expandable and allows full access to the configuration of all sensors via Bluetooth. Finally, the functions of the entire device provide a platform to be apt for future clinical trials.
Challenges in Diabetic Macular Edema Management: An Expert Consensus Report

Purpose This paper aimed to present daily-practice recommendations for the management of diabetic macular edema (DME) patients based on available scientific evidence and the clinical experience of the consensus panel. Methods A group of Spanish retina experts agreed to discuss different aspects related with the clinical management of DME patients. Results Panel was mainly focused on therapeutic objectives in DME management; definition terms; and role of biomarkers as prognostic and predictive factors to intravitreal treatment response. The panel recommends to start DME treatment as soon as possible in those eyes with a visual acuity less than 20/25 (always according to the retina unit capacity). Naïve patient was defined, in a strict manner, as a patient who, up to that moment, had never received any treatment. A refractory DME patient may be defined as the one who did not achieve a complete resolution of the disease, regardless of the treatment administered. Different optical coherence tomography biomarkers, such as disorganization of the retinal inner layers, hyperreflective dots, and cysts, have been identified as prognostic factors. Conclusion This document has sought to lay down a set of recommendations and to identify key issues that may be useful for the daily management of DME patients.