Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Greybox Fuzzing to Detect Memory Errors

Greybox fuzzing is a proven and effective testing method for the detection of security vulnerabilities and other bugs in modern software systems. Greybox fuzzing can also be used in combination with a sanitizer, such as AddressSanitizer (ASAN), to further enhance the detection of certain classes of bugs such as buffer overflow and use-after-free errors. However, sanitizers also introduce additional performance overheads, and this can degrade the performance of greybox mode fuzzing—measured in the order of 2.36 × for fuzzing with ASAN—partially negating the benefit of using a sanitizer in the first place. Recent research attributes the extra overhead to program startup/teardown costs that can dominate fork-mode fuzzing. In this paper, we present a new memory error sanitizer design that is specifically optimized for fork-mode fuzzing. The basic idea is to mark object boundaries using randomized tokens rather than disjoint metadata (as used by traditional sanitizer designs). All read/write operations are then instrumented to check for the token, and if present, a memory error will be detected. Since our design does not use a disjoint metadata, it is also very lightweight, meaning that program startup and teardown costs are minimized for the benefit of fork-mode fuzzing. We implement our design in the form of the tool, and show an improved fuzzing performance overhead of 1.14-1.27 ×, depending on the configuration.
Scalable Mediated Semi-quantum Key Distribution

Mediated semi-quantum key distribution (M-SQKD) permits two limited"semi-quantum"or"classical"users to establish a secret key with the help of a third party (TP), in which TP has fully quantum power and may be untrusted. Several protocols have been studied recently for two-party scenarios, but no one has considered M-SQKD for multi-party scenarios. In this paper, we design a circular M-SQKD protocol based on Bell states, which offers an approach to realizing multiple"classical"users' key distribution. Then, we prove the protocol is unconditional security in the asymptotic scenario. The protocol's key rate and noise tolerance can be derived by utilizing the parameters observed in the channel. The results show that our protocol may hold similar security to a fully quantum one. We also compare the proposed protocol with similar protocols in terms of noise tolerance, qubit efficiency, communication cost, and scalability. Finally, the security proof method of this paper may contribute to studying the security of other circular semi-quantum cryptography protocols.
Scattering Amplitudes in Gauge Theory and Gravity

Providing a comprehensive, pedagogical introduction to scattering amplitudes in gauge theory and gravity, this book is ideal for graduate students and researchers. It offers a smooth transition from basic knowledge of quantum field theory to the frontier of modern research. Building on basic quantum field theory, the book starts with an introduction to the spinor helicity formalism in the context of Feynman rules for tree-level amplitudes. The material covered includes on-shell recursion relations, superamplitudes, symmetries of N=4 super Yang–Mills theory, twistors and momentum twistors, Grassmannians, and polytopes. The presentation also covers amplitudes in perturbative supergravity, 3D Chern–Simons matter theories, and color-kinematics duality and its connection to 'gravity=(gauge theory)x(gauge theory)'. Basic knowledge of Feynman rules in scalar field theory and quantum electrodynamics is assumed, but all other tools are introduced as needed. Worked examples demonstrate the techniques discussed, and over 150 exercises help readers absorb and master the material.
A comprehensive survey on recent deep learning-based methods applied to surgical data

Minimally invasive surgery is highly operator dependant with a lengthy procedural time causing fatigue to surgeon and risks to patients such as injury to organs, infection, bleeding, and complications of anesthesia. To mitigate such risks, real-time systems are desired to be developed that can provide intra-operative guidance to surgeons. For example, an automated system for tool localization, tool (or tissue) tracking, and depth estimation can enable a clear understanding of surgical scenes preventing miscalculations during surgical procedures. In this work, we present a systematic review of recent machine learning-based approaches including surgical tool localization, segmentation, tracking, and 3D scene perception. Furthermore, we provide a detailed overview of publicly available benchmark datasets widely used for surgical navigation tasks. While recent deep learning architectures have shown promising results, there are still several open research problems such as a lack of annotated datasets, the presence of artifacts in surgical scenes, and non-textured surfaces that hinder 3D reconstruction of the anatomical structures. Based on our comprehensive review, we present a discussion on current gaps and needed steps to improve the adaptation of technology in surgery.
Synergistic Redundancy: Towards Verifiable Safety for Autonomous Vehicles

As Autonomous Vehicle (AV) development has progressed, concerns regarding the safety of passengers and agents in their environment have risen. Each real world traffic collision involving autonomously controlled vehicles has compounded this concern. Open source autonomous driving implementations show a software architecture with complex interdependent tasks, heavily reliant on machine learning and Deep Neural Networks (DNN), which are vulnerable to non deterministic faults and corner cases. These complex subsystems work together to fulfill the mission of the AV while also maintaining safety. Although significant improvements are being made towards increasing the empirical reliability and confidence in these systems, the inherent limitations of DNN verification create an, as yet, insurmountable challenge in providing deterministic safety guarantees in AV. We propose Synergistic Redundancy (SR), a safety architecture for complex cyber physical systems, like AV. SR provides verifiable safety guarantees against specific faults by decoupling the mission and safety tasks of the system. Simultaneous to independently fulfilling their primary roles, the partially functionally redundant mission and safety tasks are able to aid each other, synergistically improving the combined system. The synergistic safety layer uses only verifiable and logically analyzable software to fulfill its tasks. Close coordination with the mission layer allows easier and early detection of safety critical faults in the system. SR simplifies the mission layer's optimization goals and improves its design. SR provides safe deployment of high performance, although inherently unverifiable, machine learning software. In this work, we first present the design and features of the SR architecture and then evaluate the efficacy of the solution, focusing on the crucial problem of obstacle existence detection faults in AV.
Time-Domain Speech Super-Resolution With GAN Based Modeling for Telephony Speaker Verification

Automatic Speaker Verification(ASV) technology has become commonplace in virtual assistants. However, its performance suffers when there is a mismatch between the train and test domains. Mixed bandwidth training, i.e., pooling training data from both domains, is a preferred choice for developing a universal model that works for both narrowband and wideband domains. We propose complementing this technique by performing neural upsampling of narrowband signals, also known as bandwidth extension. We aim to discover and analyze high-performing time-domain Generative Adversarial Network (GAN) based models to improve our downstream state-of-the-art ASV system. We choose GANs since they 1) are powerful for learning conditional distribution and 2) allow flexible plug-in usage as a pre-processor during the training of downstream tasks (ASV) with data augmentation. Prior work mainly focused on feature-domain bandwidth extension and limited experimental setups. We address these limitations by 1) using time-domain extension models, 2) reporting results on three real test sets, 3) extending training data, and 4) devising new test-time schemes. We compare supervised (conditional GAN) and unsupervised GANs (CycleGAN) and demonstrate an average relative improvement in the equal error rate of 8.6% and 7.7%, respectively. For further analysis, we study changes in the visual quality of the spectrogram, audio perceptual quality, t-SNE embeddings, and ASV score distributions. We show that our bandwidth extension leads to phenomena such as a shift of telephone (test) embeddings towards wideband (train) signals, a negative correlation of perceptual quality with downstream performance, and condition-independent score calibration.
Is it Fun?: Understanding Enjoyment in Non-Game HCI Research

An experience of fun can be an important factor for validating the value of games. In several studies, research on non-game human–computer interaction (HCI) has been attempted to measure the enjoyment of work. However, a majority of the studies do not discuss the importance and value of the result. It is not clear as to how the term fun is understood in a non-game context nowadays. To analyze this shortcoming, we reviewed extant studies, particularly studies on non-game HCI, and explored as to how researchers determine if the value of an activity is fun. Consequently, we discussed and categorized the usage of the terms and analyzed the methodologies that are used in extant studies that evaluate the effects of fun and related terms. To gain a better understanding of fun in HCI, we provided several directions that can be discussed for strengthening enjoyable HCI research beyond applications in-volving games.
Few-Shot Document-Level Event Argument Extraction

Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. In this paper, we study to capture event arguments that actually spread across sentences in documents. Prior works usually assume full access to rich document supervision, ignoring the fact that the available argument annotation is limited in production.To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset. We first define the new problem and reconstruct the corpus by a novel N-Way-D-Doc sampling instead of the traditional N-Way-K-Shot strategy. Then we adjust the current document-level neural models into the few-shot setting to provide baseline results under in- and cross-domain settings. Since the argument extraction depends on the context from multiple sentences and the learning process is limited to very few examples, we find this novel task to be very challenging with substantively low performance. Considering FewDocAE is closely related to practical use under low-resource regimes, we hope this benchmark encourages more research in this direction. Our data and codes will be available online.
Identifiying the domain wall spin structure in current-induced switching of antiferromagnetic NiO/Pt

The understanding of antiferromagnetic domain walls, which are the interface between domains with different N\'eel order orientations, is a crucial aspect to enable the use of antiferromagnetic materials as active elements in future spintronic devices. In this work, we demonstrate that in antiferromagnetic NiO/Pt bilayers circular domain structures can be generated by switching driven by electrical current pulses. The generated domains are T-domains, separated from each other by a domain wall whose spins are pointing toward the average direction of the two T-domains rather than the common axis of the two planes. Interestingly, this direction is the same for the whole circular domain indicating the absence of strong Lifshitz invariants. The domain wall can be micromagnetically modeled by strain distributions in the NiO thin film induced by the MgO substrate, deviating from the bulk anisotropy. From our measurements we determine the domain wall width to have a full width at half maximum of $\Delta = 98 \pm 10$ nm, demonstrating strong confinement.
Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting

In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video. We apply our method to the challenging case of Sign Language videos: given a source video of a sign language user, we can faithfully transfer the performed manual (e.g., handshape, palm orientation, movement, location) and non-manual (e.g., eye gaze, facial expressions, mouth patterns, head, and body movements) signs to a target video in a photo-realistic manner. Our method can be used for Sign Language Anonymization, Sign Language Production (synthesis module), as well as for reenacting other types of full body activities (dancing, acting performance, exercising, etc.). We conduct detailed qualitative and quantitative evaluations and comparisons, which demonstrate the particularly promising and realistic results that we obtain and the advantages of our method over existing approaches.
The Power of Uniform Sampling for Coresets

Motivated by practical generalizations of the classic k-median and k-means objectives, such as clustering with size constraints, fair clustering, and Wasserstein barycenter, we introduce a meta-theorem for designing coresets for constrained-clustering problems. The meta-theorem reduces the task of coreset construction to one on a bounded number of ring instances with a much-relaxed additive error. This reduction enables us to construct coresets using uniform sampling, in contrast to the widely-used importance sampling, and consequently we can easily handle constrained objectives. Notably and perhaps surprisingly, this simpler sampling scheme can yield coresets whose size is independent of n, the number of input points. Our technique yields smaller coresets, and sometimes the first coresets, for a large number of constrained clustering problems, including capacitated clustering, fair clustering, Euclidean Wasserstein barycenter, clustering in minor-excluded graph, and polygon clustering under Fréchet and Hausdorff distance. Finally, our technique yields also smaller coresets for 1-median in low-dimensional Euclidean spaces, specifically of size $\tilde{O}(\varepsilon^{-15})$ in $\mathbb{R}^{2}$ and $\tilde{O}(\varepsilon^{-16})$ in $\mathbb{R}^{3}$.
Regularity results for degenerate wave equations in a neighborhood of the boundary

In this paper we establish some regularity results concerning the behavior of weak solutions and very weak solutions of the degenerate wave equation near the boundary. For the nondegenerate case, the correponding results were originally obtained by Fabre and Puel (J. of Diff. Eq. 106, 1993). This kind of results is closely related to the exact boundary controllability for the wave equation as the limit of internal controllability.
Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a micro-lending marketplace, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.
Merged-GHCIDR: Geometrical Approach to Reduce Image Data

—The computational resources required to train a model have been increasing since the inception of deep networks. Training neural networks on massive datasets have become a challenging and time-consuming task. So, there arises a need to reduce the dataset without compromising the accuracy. In this paper, we present novel variations of an earlier approach called reduction through homogeneous clustering for reducing dataset size. The proposed methods are based on the idea of partitioning the dataset into homogeneous clusters and select- ing images that contribute signiﬁcantly to the accuracy. We propose two variations: Geometrical Homogeneous Clustering for Image Data Reduction (GHCIDR) and Merged-GHCIDR upon the baseline algorithm - Reduction through Homogeneous Clustering (RHC) to achieve better accuracy and training time. The intuition behind GHCIDR involves selecting data points by cluster weights and geometrical distribution of the training set. Merged-GHCIDR involves merging clusters having the same labels using complete linkage clustering. We used three deep learning models- Fully Connected Networks (FCN), VGG1, and VGG16. We experimented with the two variants on four datasets-MNIST, CIFAR10, Fashion-MNIST, and Tiny-Imagenet. Merged- GHCIDR with the same percentage reduction as RHC showed an increase of 2.8%, 8.9%, 7.6% and 3.5% accuracy on MNIST, Fashion-MNIST, CIFAR10, and Tiny-Imagenet, respectively.
Continuous simultaneous measurement of position and momentum of a particle

We formulate a model of a quantum particle continuously monitored by detectors measuring simultaneously its position and momentum. We implement the postulate of wavefunction collapse by assuming that upon detection the particle is found in one of the meters' states chosen as a discrete subset of coherent states. The dynamics, as observed by the meters, is thus a random sequence of jumps between coherent states. We generate such trajectories using the Monte Carlo Wavefunction method. For sparsely distributed detectors, we use methods from renewal theory of stochastic processes to obtain some semi-analytic results. In particular, the different regimes of dynamics of the free particle are identified and quantitatively discussed: from stroboscopic motion in the case of low interrogation frequency, to delayed dynamics reminiscent of the Zeno effect if monitoring is frequent. For a semi-continuous spatial distribution of meters the emergence of classical trajectories is shown. Their statistical properties are discussed and compared to other detection schemes in which the effect of measurement corresponds to"spatial filtering"of the wavefunction.
The soft X-ray background with Suzaku. I. Milky Way halo

We present measurements of the soft X-ray background emission for 130 Suzaku observations at 75° < l < 285° and |b| > 15° obtained from 2005 to 2015, covering nearly one solar cycle. In addition to the standard soft X-ray background model consisting of the local hot bubble and the Milky Way Halo (MWH), we include a hot collisional-ionization-equilibrium component with a temperature of ∼0.8 keV to reproduce spectra of a significant fraction of the lines of sight. Then, the scatter in the relation between the emission measure vs. temperature of the MWH component is reduced. Here, we exclude time ranges with high count rates to minimize the effect of the solar wind charge exchange (SWCX). However, the spectra of almost the same lines of sight are inconsistent. The heliospheric SWCX emissions likely contaminate and give a bias in measurements of temperature and the emission measure of the MWH. Excluding the data around the solar maximum and using the data taken before the end of 2009, at |b| > 35° and 105° < l < 255°, the temperature (0.22 keV) and emission measure (2 × 10−3 cm−6 pc) of the MWH are fairly uniform. The increase of the emission measure toward the lower Galactic latitude at |b| < 35° indicates the presence of a disk-like morphology component. A composite model which consists of disk-like and spherical-morphology components also reproduces the observed emission measure distribution of MWH. In this case, the hydrostatic mass at a few tens of kiloparsec from the Galactic center agrees with the gravitational mass of the Milky Way. The plasma with the virial temperature likely fills the Milky Way halo in nearly hydrostatic equilibrium. Assuming a gas metallicity of 0.3 solar, the upper limit of the gas mass of the spherical component out to 250 kpc, or the virial radius, is ∼ a few × 1010 M⊙.
Exploring the Verifiability of Code Generated by GitHub Copilot

GitHub's Copilot generates code quickly. We investigate whether it generates good code. Our approach is to identify a set of problems, ask Copilot to generate solutions, and attempt to formally verify these solutions with Dafny. Our formal verification is with respect to hand-crafted specifications. We have carried out this process on 6 problems and succeeded in formally verifying 4 of the created solutions. We found evidence which corroborates the current consensus in the literature: Copilot is a powerful tool; however, it should not be"flying the plane"by itself.
Toward Intelligent Millimeter and Terahertz Communication for 6G: Computer Vision-Aided Beamforming

Beamforming technique realized by the multipleinput-multiple-output (MIMO) antenna arrays has been widely used to compensate for the severe path loss in the millimeter wave (mmWave) bands. In 5G NR system, the beam sweeping and beam refinement are employed to find out the best beam codeword aligned to the mobile. Due to the complicated handshaking and finite resolution of the codebook, today's 5G-based beam management strategy is ineffective in various scenarios in terms of the data rate, energy consumption, and also processing latency. An aim of this article is to introduce a new type of beam management framework based on the computer vision (CV) technique. In this framework referred to as computer vision-aided beam management (CVBM), a camera attached to the BS captures the image and the deep learning-based object detector identifies the 3D location of the mobile. Since the base station can directly set the beam direction without the codebook quantization and feedback delay, CVBM achieves the significant beamforming gain and latency reduction. Using the specially designed dataset called Vision Objects for Beam Management (VOBEM), we demonstrate that CVBM achieves more than 40 percent improvement in the beamforming gain and 40 percent reduction in the beam training overhead over the 5G NR beam management.
Capture of Dark Matter Particles by a Galaxy in the Case of a Bimodal Distribution of Their Velocities

We have analyzed the rate of capture of dark matter (DM) particles by the galaxy in the case of the existence of two different types of DM or a bimodal velocity distribution function for DM. It is shown that, in addition to the scenario considered in our previous work which is based on the assumption of an unimodal distribution, more complex scenarios are possible in which the transition to the state of intense capture and/or exit from it can occur in two stages. A detailed description is given of the change in the curve describing the rate of capture of dark matter particles as a function of the rate of increase in the baryon mass of the galaxy for various values of the rate of decrease of the DM density.