Miracle-dz/UniFAR-FTUs huggingface dataset:
Carpet-bombing patch: attacking a deep network without usual requirements

Although deep networks have shown vulnerability to evasion attacks, such attacks have usually unrealistic requirements. Recent literature discussed the possibility to remove or not some of these requirements. This paper con-tributes to this literature by introducing a carpet-bombing patch attack which has almost no requirement. Targeting the feature representations, this patch attack does not require knowing the network task. This attack decreases accuracy on Imagenet, mAP on Pascal Voc, and IoU on Cityscapes without being aware that the underlying tasks involved classiﬁcation, detection or semantic segmentation, respectively. Beyond the potential safety issues raised by this attack, the impact of the carpet-bombing attack high-lights some interesting property of deep network layer dynamic.
Saddle-Point Approach to Large-Time Volatility Smile

We extend upon the saddle-point equation presented in [1] to derive large-time model-implied volatility smiles, providing its theoretical foundation and studying its applications in classical models. As long as characteristic function fulfills a Lévy-type scaling behavior in large time, the approach allows us to study analytically the large-time smile behaviors under specific models, and moreover, to reach a very wide class of arbitrage-free model-inspired parametrizations, in the same manner as stochasticvolatility-inspired (SVI).
CoVRage: Millimeter-Wave Beamforming for Mobile Interactive Virtual Reality

Contemporary Virtual Reality (VR) setups often include an external source delivering content to a Head-Mounted Display (HMD). “Cutting the wire” in such setups and going truly wireless will require a wireless network capable of delivering enormous amounts of video data at an extremely low latency. The massive bandwidth of higher frequencies, such as the millimeter-wave (mmWave) band, can meet these requirements. Due to high attenuation and path loss in the mmWave frequencies, beamforming is essential. In wireless VR, where the antenna is integrated into the HMD, any head rotation also changes the antenna’s orientation. As such, beamforming must adapt, in real-time, to the user’s head rotations. An HMD’s built-in sensors providing accurate orientation estimates may facilitate such rapid beamforming. In this work, we present coVRage, a receive-side beamforming solution tailored for VR HMDs. Using built-in orientation prediction present on modern HMDs, the algorithm estimates how the Angle of Arrival (AoA) at the HMD will change in the near future, and covers this AoA trajectory with a dynamically shaped oblong beam, synthesized using sub-arrays. We show that this solution can cover these trajectories with consistently high gain, even in light of temporally or spatially inaccurate orientational data.
2D/3D Deep Image Registration by Learning 3D Displacement Fields for Abdominal Organs

Deformable registration of two-dimensional/three-dimensional (2D/3D) images of abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in two-dimensional X-ray images. We propose a supervised deep learning framework that achieves 2D/3D deformable image registration between 3D volumes and single-viewpoint 2D projected images. The proposed method learns the translation from the target 2D projection images and the initial 3D volume to 3D displacement fields. In experiments, we registered 3D-computed tomography (CT) volumes to digitally reconstructed radiographs generated from abdominal 4D-CT volumes. For validation, we used 4D-CT volumes of 35 cases and confirmed that the 3D-CT volumes reflecting the nonlinear and local respiratory organ displacement were reconstructed. The proposed method demonstrate the compatible performance to the conventional methods with a dice similarity coefficient of 91.6 \% for the liver region and 85.9 \% for the stomach region, while estimating a significantly more accurate CT values.
Hitting Times of Random Walks on Edge Corona Product Graphs

Graph products have been extensively applied to model complex networks with striking properties observed in real-world complex systems. In this paper, we study the hitting times for random walks on a class of graphs generated iteratively by edge corona product. We first derive recursive solutions to the eigenvalues and eigenvectors of the normalized adjacency matrix associated with the graphs. Based on these results, we further obtain interesting quantities about hitting times of random walks, providing iterative formulas for two-node hitting time, as well as closed-form expressions for the Kemeny's constant defined as a weighted average of hitting times over all node pairs, as well as the arithmetic mean of hitting times of all pairs of nodes.
A singularly perturbed linear eigenvalue problem in C1 domains

Some non-existence result is established for bounded solutions of a Neumann problem on the upper half space. Based on this non-existence result, precise asymptotic behavior is given for the principal eigenvalue of some linear eigenvalue problem in bounded C 1 domains, and this answers a question that appeared in Lacey et al, 1998.
Scheme for implementing quantum dense coding in cavity QED

An experimentally feasible scheme for implementing quantum dense coding in cavity QED is proposed. In the scheme the atoms interact simultaneously with a highly detuned cavity mode with the assistance of a classical field. The scheme is insensitive to the cavity decay and the thermal field. Bell states can be exactly distinguished via detecting the atomic state, and the quantum dense coding can be realized in a simple way.
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning

Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to interpret the knowledge and reuse it across tasks. Inductive biases can address such issues by explicitly providing generic yet useful decomposition that is otherwise difficult or expensive to learn implicitly. For example, object-centered approaches decompose a high dimensional observation into individual objects. Expanding on this, we utilize an inductive bias for explicit object-centered knowledge separation that provides further decomposition into semantic representations and dynamics knowledge. For this, we introduce a semantic module that predicts an objects' semantic state based on its context. The resulting affordance-like object state can then be used to enrich perceptual object representations. With a minimal setup and an environment that enables puzzle-like tasks, we demonstrate the feasibility and benefits of this approach. Specifically, we compare three different methods of integrating semantic representations into a model-based RL architecture. Our experiments show that the degree of explicitness in knowledge separation correlates with faster learning, better accuracy, better generalization, and better interpretability.
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals

Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying federated learning requires a large communication bandwidth, which limits its deployment in wireless networks. To address this bottleneck, we introduce a residual-based federated learning framework (ResFed), where residuals rather than model parameters are transmitted in communication networks for training. In particular, we integrate two pairs of shared predictors for the model prediction in both server-to-client and client-to-server communication. By employing a common prediction rule, both locally and globally updated models are always fully recoverable in clients and the server. We highlight that the residuals only indicate the quasi-update of a model in a single inter-round, and hence contain more dense information and have a lower entropy than the model, comparing to model weights and gradients. Based on this property, we further conduct lossy compression of the residuals by sparsification and quantization and encode them for efficient communication. The experimental evaluation shows that our ResFed needs remarkably less communication costs and achieves better accuracy by leveraging less sensitive residuals, compared to standard federated learning. For instance, to train a 4.08 MB CNN model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, our approach achieves a compression ratio over 700X in each communication round with minimum impact on the accuracy. To reach an accuracy of 70%, it saves around 99% of the total communication volume from 587.61 Mb to 6.79 Mb in up-streaming and to 4.61 Mb in down-streaming on average for all clients.
Photon Emission from Ice during Fracture

We performed the first demonstration of photon emission from ice during deformation and fracture. Emission of visible photons was confirmed in accordance with crack generation and fracture. Time-resolved photon emission signals are presented along with load changing. Emission intensity roughly increased with increasing strain energy released by fracture, while scattering in the intensity data showed that emission intensity was strongly dependent on thc characteristics of an individual crack. Experimental evidence indicates that the generated electric charge on crack surfaces and at crack tips should have higher energy than that of electromagnetic emissions reported by Fifolt, Petrenko and Schulson. [rf23]
Leveraging Architectural Approaches in Web3 Applications - A DAO Perspective Focused

Architectural design contexts contain a set of factors that greatly influence software application development. Among them, organizational design contexts consist of high-level company concerns and how it is structured, for example, stakeholders and development schedules heavily impacting design considerations. The Decentralized Autonomous Organization (DAO), as a vital concept in the Web3 space, represents an organization constructed by automatically executed rules, such as via smart contracts, holding features of the permissionless committee, transparent proposals, and fair contribution by participated stakeholders. In this work, we conduct a systematic literature review of existing DAO literature to summarize its structural features, benefits and challenges, and potential development directions in the context of Web3 applications.
Technological taxonomies for hypernym and hyponym retrieval in patent texts

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.
Detection Selection Algorithm: A Likelihood based Optimization Method to Perform Post Processing for Object Detection

In object detection, post-processing methods like Non-maximum Suppression (NMS) are widely used. NMS can substantially reduce the number of false positive detections but may still keep some detections with low objectness scores. In order to find the exact number of objects and their labels in the image, we propose a post processing method called Detection Selection Algorithm (DSA) which is used after NMS or related methods. DSA greedily selects a subset of detected bounding boxes, together with full object reconstructions that give the interpretation of the whole image with highest likelihood, taking into account object occlusions. The algorithm consists of four components. First, we add an occlusion branch to Faster R-CNN to obtain occlusion relationships between objects. Second, we develop a single reconstruction algorithm which can reconstruct the whole appearance of an object given its visible part, based on the optimization of latent variables of a trained generative network which we call the decoder. Third, we propose a whole reconstruction algorithm which generates the joint reconstruction of all objects in a hypothesized interpretation, taking into account occlusion ordering. Finally we propose a greedy algorithm that incrementally adds or removes detections from a list to maximize the likelihood of the corresponding interpretation. DSA with NMS or Soft-NMS can achieve better results than NMS or Soft-NMS themselves, as is illustrated in our experiments on synthetic images with mutiple 3d objects.
Advantage of quantum coherence in postselected metrology

Shao-Jie Xiong,1, 2 Peng-Fei Wei,1 Huang-Qiu-Chen Wang,1 Lei Shao,2 Yong-Nan Sun,1 Jing Liu,3, ∗ Zhe Sun,1, † and Xiao-Guang Wang2, ‡ School of physics, Hangzhou Normal University, Hangzhou 310036, China Zhejiang Institute of Modern Physics and Department of Physics, Zhejiang University, Hangzhou, Zhejiang 310027, China National Precise Gravity Measurement Facility, MOE Key Laboratory of Fundamental Physical Quantities Measurement, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, China
On Computing Probabilistic Abductive Explanations

The most widely studied explainable AI (XAI) approaches are unsound. This is the case with well-known model-agnostic explanation approaches, and it is also the case with approaches based on saliency maps. One solution is to consider intrinsic interpretability, which does not exhibit the drawback of unsoundness. Unfortunately, intrinsic interpretability can display unwieldy explanation redundancy. Formal explainability represents the alternative to these non-rigorous approaches, with one example being PI-explanations. Unfortunately, PI-explanations also exhibit important drawbacks, the most visible of which is arguably their size. Recently, it has been observed that the (absolute) rigor of PI-explanations can be traded off for a smaller explanation size, by computing the so-called relevant sets. Given some positive {\delta}, a set S of features is {\delta}-relevant if, when the features in S are fixed, the probability of getting the target class exceeds {\delta}. However, even for very simple classifiers, the complexity of computing relevant sets of features is prohibitive, with the decision problem being NPPP-complete for circuit-based classifiers. In contrast with earlier negative results, this paper investigates practical approaches for computing relevant sets for a number of widely used classifiers that include Decision Trees (DTs), Naive Bayes Classifiers (NBCs), and several families of classifiers obtained from propositional languages. Moreover, the paper shows that, in practice, and for these families of classifiers, relevant sets are easy to compute. Furthermore, the experiments confirm that succinct sets of relevant features can be obtained for the families of classifiers considered.
Phases, Modalities, Temporal and Spatial Locality: Domain Specific ML Prefetcher for Accelerating Graph Analytics

—Graph processing applications are severely bottle-necked by memory system performance due to low data reuse and irregular memory accesses. While state-of-the-art prefetchers using Machine Learning (ML) have made great progress, they do not perform well on graph analytics applications due to phase transitions in the execution and irregular data access that is hard to predict. We propose MPGraph: a novel ML-based Prefetcher for Graph analytics. MPGraph makes three novel optimizations based on domain knowledge of graph analytics. It detects the transition of graph processing phases during execution using a novel soft detection technique, predicts memory accesses and pages using phase-speciﬁc multi-modality predictors, and prefetches using a novel chain spatio-temporal prefetching strategy. We evaluate our approach using three widely-used graph processing frameworks and a variety of graph datasets. Our approach achieves 34.17%–82.15% higher precision in phase transition detection than the KSWIN and decision tree baselines. Our predictors achieve 6.80%–16.02% higher F1-score for access prediction and 11.68%–15.41% higher accuracy-at-10 for page prediction compared with the baselines LSTM-based and vanilla attention-based models. Simulations show that MPGraph achieves on the average 87.16% (prefetch accuracy) and 73.29% (prefetch coverage), leading to 12.52%– 21.23% IPC improvement. It outperforms the widely-used non-ML prefetcher BO by 7.58%–12.03%, and outperforms state-of-the-art ML-based prefetchers Voyager by 3.27%–4.42% and TransFetch by 3.73%–4.58% with respect to IPC improvement
Effect of interfaces in Monte Carlo computer simulations of ferroelectric materials

In this article, Monte Carlo simulation methods were used to investigate the influence of interface layers between the ferroelectric core material and the electrodes on the hysteresis loop in ferroelectric thin films. The hysteresis loops were calculated using an existing Monte Carlo model. For certain interface configurations, the simulations resulted in asymmetric hysteresis loops, similar to imprinted loops, due to asymmetric nucleation kinetics. Although the results might not offer a new explanation for imprint in ferroelectric thin films, they provide insight into the often observed phenomenon of initially imprinted hysteresis loops of as-prepared thin-film samples.
T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics

Modern embedding-based metrics for evaluation of generated text generally fall into one of two paradigms: discriminative metrics that are trained to directly predict which outputs are of higher quality according to supervised human annotations, and generative metrics that are trained to evaluate text based on the probabilities of a generative model. Both have their advantages; discriminative metrics are able to directly optimize for the problem of distinguishing between good and bad outputs, while generative metrics can be trained using abundant raw text. In this paper, we present a framework that combines the best of both worlds, using both supervised and unsupervised signals from whatever data we have available. We operationalize this idea by training T5Score, a metric that uses these training signals with mT5 as the backbone. We perform an extensive empirical comparison with other existing metrics on 5 datasets, 19 languages and 280 systems, demonstrating the utility of our method. Experimental results show that: T5Score achieves the best performance on all datasets against existing top-scoring metrics at the segment level. We release our code and models at https://github.com/qinyiwei/T5Score.
Experimental Demonstration of Nanolaser with sub-μAThreshold Current

We demonstrate a photonic crystal nanolaser exhibiting an ultra-low threshold of 730 nA at telecom wavelengths. The laser can be directly modulated at 3 GHz at an energy cost of 1 fJ/bit. This is the lowest threshold reported for any laser operating at room temperature and facilitates low-energy on-chip links.
Tunable energy-level inversion in spin-orbit-coupled Bose-Einstein condensates

A method to realize controllable inversion of energy levels in a one-dimensional spin-orbit (SO)-coupled two-component Bose-Einstein condensate under the action of a gradient magnetic ﬁeld and harmonic-oscillator (HO) trapping potential is proposed. The linear version of the system is solved exactly. By adjusting the SO coupling strength and magnetic-ﬁeld gradient, the energy-level inversion makes it possible to transform any excited state into the ground state. The full nonlinear system is solved numerically, and it is found that the results are consistent with the linear prediction in the case of the repulsive inter-component interaction. On the other hand, the inter-component attraction gives rise to states of superposition and edge types. Similar results are also reported for the system with the HO trap replaced by the box potential. These results suggest a possibility to realize any excited state and observe it in the experiment.
Parallel remote state preparation for fully device-independent verifiable blind quantum computation

We introduce a device-independent two-prover scheme in which a classical verifier is able to use a simple untrusted quantum measurement device (the client device) to securely delegate a quantum computation to an untrusted quantum server. To do this, we construct a parallel self-testing protocol to perform device-independent remote state preparation of n qubits and compose this with the unconditionally secure universal verifiable blind quantum computation (VBQC) scheme of J. F. Fitzsimons and E. Kashefi [Phys. Rev. A 96, 012303 (2017)]. Our selftest achieves a multitude of desirable properties for the application we consider, giving rise to practical and fully device-independent VBQC. It certifies parallel measurements of all cardinal and intercardinal directions in the XY -plane as well as the computational basis, uses few input questions (of size logarithmic in n for the client and a constant number communicated to the server), and requires only single-qubit measurements to be performed by the client device.
Extracting Constructive Content From Classical Proofs

This thesis is concerned with the relationship between classical and constructive mathematics. It is well-known that in many constructive logics, we can interpret mathematical sentences as program specifications, and we can interpret a constructive proof of such a sentence as a program which meets this specification. It is also well-known that many classical logics do not have the property, as shown by Brouwer''s counterexamples to some theorems of analysis. Kreisel and Friedman showed that for certain classes of sentences $(\Pi_{2}^{0})$, the classical theories conservatively extend their constructive counterparts, and furthermore give effective translations from classical proofs to constructive proofs. This thesis consists of two parts. In the first, we describe our implementation of Friedman''s translation results, and their use in translating Higman''s Lemma, a nontrivial theorem of combinatorics. To do this, we delineate a subtheory of a constructive type theory (Nuprl) for which Friedman''s translation is guaranteed to succeed. We also extend the Nuprl type theory with impredicative $\Pi$-quantification, and use this to provide a classical proof of Higman''s Lemma, which we go on to mechanically translate to a constructive proof. In the second part, we discuss connections that we have discovered between Friedman''s translation and a standard compilation technique, continuation-passing-style (CPS) translation. We demonstrate that a classical proof of a $(\Pi_{2}^{0})$ sentence $\Phi$ is a program which meets the specification $\Phi$. We demonstrate that we can consistently give algorithmic content to the only constructively problematic rule of classical logic, the rule of double-negation elimination. This algorithmic content is the nonlocal control operator $C$ (a relative of call-with-current-continuation). Moreover, we show that Friedman''s translation is exactly a CPS-translation on the classical ``program'''' (with $C$), converting it into a pure functional program (without $C$). Our work provides a semantic account of Friedman''s translation, in terms of its effect on programs, making the connections (and the differences) between classical and constructive systems clearer and more precise. Moreover, we provide the first steps towards integrating nonlocal control operators into a type-theoretic explanation of computation.