Reviewerly/exHarmony huggingface dataset:
HCIC: Hardware-Assisted Control-Flow Integrity Checking

Recently, code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP), have emerged as a new class of ingenious security threats. Attackers can utilize CRAs to hijack the control flow of programs to perform malicious actions without injecting any codes. Many defenses, classed into software-based and hardware-based, have been proposed. However, software-based methods are difficult to be deployed in practical systems due to high performance overhead. Hardware-based methods can reduce performance overhead but may require extending instruction set architectures (ISAs) and modifying the compiler or suffer the vulnerability of key leakage. To tackle these issues, this paper proposes a new hardware-assisted control flow checking method to resist CRAs with negligible performance overhead without extending ISAs, modifying the compiler or leaking the encryption/decryption key. The key technique involves two control flow checking mechanisms. The first one is the encrypted Hamming distances matching between the physical unclonable function (PUF) response and the return addresses, which prevents attackers from returning between gadgets so long as the PUF response is secret, thus resisting ROP attacks. The second one is the linear encryption/decryption operation (XOR) between the PUF response and the instructions at target addresses of call and jmp instructions to defeat JOP attacks. Advanced return-based full-function reuse attacks will be prevented with the dynamic key-updating method. Experimental evaluations on benchmarks demonstrate that the proposed method introduces negligible 0.95% runtime overhead and 0.78% binary size overhead on average.
Understanding and bridging the gaps in current GNN performance optimizations

Graph Neural Network (GNN) has recently drawn a rapid increase of interest in many domains for its effectiveness in learning over graphs. Maximizing its performance is essential for many tasks, but remains preliminarily understood. In this work, we provide an in-depth examination of the state-of-the-art GNN frameworks, revealing five major gaps in the current frameworks in optimizing GNN performance, especially in handling the special complexities of GNN over traditional graph or DNN operations. Based on the insights, we put together a set of optimizations to fill the gaps. These optimizations leverage the state-of-the-art GPU optimization techniques and tailor them to the special properties of GNN. Experimental results show that these optimizations achieve 1.37×--15.5× performance improvement over the state-of-the-art frameworks on various GNN models.
Counterdiabaticity and the quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) is a near-term hybrid algorithm intended to solve combinatorial optimization problems, such as MaxCut. QAOA can be made to mimic an adiabatic schedule, and in the $p\to\infty$ limit the final state is an exact maximal eigenstate in accordance with the adiabatic theorem. In this work, the connection between QAOA and adiabaticity is made explicit by inspecting the regime of $p$ large but finite. By connecting QAOA to counterdiabatic (CD) evolution, we construct CD-QAOA angles which mimic a counterdiabatic schedule by matching Trotter "error" terms to approximate adiabatic gauge potentials which suppress diabatic excitations arising from finite ramp speed. In our construction, these "error" terms are helpful, not detrimental, to QAOA. Using this matching to link QAOA with quantum adiabatic algorithms (QAA), we show that the approximation ratio converges to one at least as $1-C(p)\sim 1/p^{\mu}$. We show that transfer of parameters between graphs, and interpolating angles for $p+1$ given $p$ are both natural byproducts of CD-QAOA matching. Optimization of CD-QAOA angles is equivalent to optimizing a continuous adiabatic schedule. Finally, we show that, using a property of variational adiabatic gauge potentials, QAOA is at least counterdiabatic, not just adiabatic, and has better performance than finite time adiabatic evolution. We demonstrate the method on three examples: a 2 level system, an Ising chain, and the MaxCut problem.
Cervical Cytology Classification Using PCA and GWO Enhanced Deep Features Selection

Cervical cancer is one of the most deadly and common diseases among women worldwide. It is completely curable if diagnosed in an early stage, but the tedious and costly detection procedure makes it unviable to conduct population-wise screening. Thus, to augment the effort of the clinicians, in this paper, we propose a fully automated framework that utilizes deep learning and feature selection using evolutionary optimization for cytology image classification. The proposed framework extracts deep feature from several convolution neural network (CNN) models and uses a two-step feature reduction approach to ensure reduction in computation cost and faster convergence. The features extracted from the CNN models form a large feature space whose dimensionality is reduced using principal component analysis while preserving 99% of the variance. A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the grey wolf optimizer, thus improving the classification performance. Finally, the selected feature subset is used to train an support vector machine classifier for generating the final predictions. The proposed framework is evaluated on three publicly available benchmark datasets: Mendeley Liquid Based Cytology (4-class) dataset, Herlev Pap Smear (7-class) dataset, and the SIPaKMeD Pap Smear (5-class) dataset achieving classification accuracies of 99.47, 98.32 and 97.87%, respectively, thus justifying the reliability of the approach. The relevant codes for the proposed approach can be found in: https://github.com/DVLP-CMATERJU/Two-Step-Feature-Enhancement .
Flexible entanglement-distribution network with an AlGaAs chip for secure communications

Abstract Quantum communication networks enable applications ranging from highly secure communication to clock synchronization and distributed quantum computing. Miniaturized, flexible, and cost-efficient resources will be key elements for ensuring the scalability of such networks as they progress towards large-scale deployed infrastructures. Here, we bring these elements together by combining an on-chip, telecom-wavelength, broadband entangled photon source with industry-grade flexible-grid wavelength division multiplexing techniques, to demonstrate reconfigurable entanglement distribution between up to 8 users in a resource-optimized quantum network topology. As a benchmark application we use quantum key distribution, and show low error and high secret key generation rates across several frequency channels, over both symmetric and asymmetric metropolitan-distance optical fibered links and including finite-size effects. By adapting the bandwidth allocation to specific network constraints, we also illustrate the flexible networking capability of our configuration. Together with the potential of our semiconductor source for distributing secret keys over a 60 nm bandwidth with commercial multiplexing technology, these results offer a promising route to the deployment of scalable quantum network architectures.
Sysrev: A FAIR Platform for Data Curation and Systematic Evidence Review

Well-curated datasets are essential to evidence based decision making and to the integration of artificial intelligence with human reasoning across disciplines. However, many sources of data remain siloed, unstructured, and/or unavailable for complementary and secondary research. Sysrev was developed to address these issues. First, Sysrev was built to aid in systematic evidence reviews (SER), where digital documents are evaluated according to a well defined process, and where Sysrev provides an easy to access, publicly available and free platform for collaborating in SER projects. Secondly, Sysrev addresses the issue of unstructured, siloed, and inaccessible data in the context of generalized data extraction, where human and machine learning algorithms are combined to extract insights and evidence for better decision making across disciplines. Sysrev uses FAIR - Findability, Accessibility, Interoperability, and Reuse of digital assets - as primary principles in design. Sysrev was developed primarily because of an observed need to reduce redundancy, reduce inefficient use of human time and increase the impact of evidence based decision making. This publication is an introduction to Sysrev as a novel technology, with an overview of the features, motivations and use cases of the tool. Methods: Sysrev. com is a FAIR motivated web platform for data curation and SER. Sysrev allows users to create data curation projects called "sysrevs" wherein users upload documents, define review tasks, recruit reviewers, perform review tasks, and automate review tasks. Conclusion: Sysrev is a web application designed to facilitate data curation and SERs. Thousands of publicly accessible Sysrev projects have been created, accommodating research in a wide variety of disciplines. Described use cases include data curation, managed reviews, and SERs.
Pulse-efficient circuit transpilation for quantum applications on cross-resonance-based hardware

We show a pulse-efficient circuit transpilation framework for noisy quantum hardware. This is achieved by scaling cross-resonance pulses and exposing each pulse as a gate to remove redundant single-qubit operations with the transpiler. Crucially, no additional calibration is needed to yield better results than a CNOT-based transpilation. This pulse-efficient circuit transpilation therefore enables a better usage of the finite coherence time without requiring knowledge of pulse-level details from the user. As demonstration, we realize a continuous family of cross-resonance-based gates for SU(4) by leveraging Cartan's decomposition. We measure the benefits of a pulse-efficient circuit transpilation with process tomography and observe up to a 50% error reduction in the fidelity of ${R}_{ZZ}(\ensuremath{\theta})$ and arbitrary SU(4) gates on IBM Quantum devices. We apply this framework for quantum applications by running circuits of the quantum approximate optimization algorithm applied to MAXCUT. For an 11-qubit nonhardware native graph, our methodology reduces the overall schedule duration by up to 52% and errors by up to 38%.
Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection

Weakly supervised Video Anomaly Detection (VAD) using Multi-Instance Learning (MIL) is usually based on the fact that the anomaly score of an abnormal snippet is higher than that of a normal snippet. In the beginning of training, due to the limited accuracy of the model, it is easy to select the wrong abnormal snippet. In order to reduce the probability of selection errors, we first propose a Multi-Sequence Learning (MSL) method and a hinge-based MSL ranking loss that uses a sequence composed of multiple snippets as an optimization unit. We then design a Transformer-based MSL network to learn both video-level anomaly probability and snippet-level anomaly scores. In the inference stage, we propose to use the video-level anomaly probability to suppress the fluctuation of snippet-level anomaly scores. Finally, since VAD needs to predict the snippet-level anomaly scores, by gradually reducing the length of selected sequence, we propose a self-training strategy to gradually refine the anomaly scores. Experimental results show that our method achieves significant improvements on ShanghaiTech, UCF-Crime, and XD-Violence.
Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis

Recent advances in quantum computing devices have brought attention to hybrid quantum-classical algorithms like the variational quantum eigensolver (VQE) as a potential route to practical quantum advantage in chemistry. However, it is not yet clear whether such algorithms, even in the absence of device error, could actually achieve quantum advantage for systems of practical interest. We have performed an exhaustive analysis to estimate the number of qubits and number of measurements required to compute the combustion energies of small organic molecules and related systems to within chemical accuracy of experimental values using the VQE. We consider several key modern improvements to the VQE, including low-rank factorizations of the Hamiltonian. Our results indicate that, although these techniques are useful, they will not be sufficient to achieve practical quantum computational advantage for our molecular set, or for similar molecules. This suggests that novel approaches to operator estimation leveraging quantum coherence, such as enhanced likelihood functions, may be required.
ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT, a lite and conversational variant of Generative Pretrained Transformer 4 (GPT-4) developed by OpenAI, is one of the milestone Large Language Models (LLMs) with billions of parameters. LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks, which profoundly impact various fields. This paper mainly discusses the future applications of LLMs in dentistry. We introduce two primary LLM deployment methods in dentistry, including automated dental diagnosis and cross-modal dental diagnosis, and examine their potential applications. Especially, equipped with a cross-modal encoder, a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations. We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application. While LLMs offer significant potential benefits, the challenges, such as data privacy, data quality, and model bias, need further study. Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
Approximate quantum Fourier transform with O(n log(n)) T gates

Abstract The ability to implement the Quantum Fourier Transform (QFT) efficiently on a quantum computer facilitates the advantages offered by a variety of fundamental quantum algorithms, such as those for integer factoring, computing discrete logarithm over Abelian groups, solving systems of linear equations, and phase estimation, to name a few. The standard fault-tolerant implementation of an n -qubit unitary QFT approximates the desired transformation by removing small-angle controlled rotations and synthesizing the remaining ones into Clifford+T gates, incurring the T-count complexity of $$O(n\,{\mathrm{log}}^{2}\,(n))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> <mml:mspace /> <mml:msup> <mml:mrow> <mml:mi>log</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . In this paper, we show how to obtain approximate QFT with the T-count of $$O(n\,{\mathrm{log}}\,(n))$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> <mml:mspace /> <mml:mi>log</mml:mi> <mml:mspace /> <mml:mrow> <mml:mo>(</mml:mo> <mml:mrow> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> . For brevity, the above figures omit the dependence on the approximation error ε , assuming the error is fixed. Our approach relies on quantum circuits with measurements and feedforward, and on reusing a special quantum state that induces the phase gradient transformation. We report asymptotic analysis as well as concrete circuits, demonstrating significant advantages in both theory and practice.
Exploiting Combination Effect for Unsupervised Feature Selection by &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$\ell_{2,0}$ &lt;/tex-math&gt; &lt;/inline-formula&gt; Norm

In learning applications, exploring the cluster structures of the high dimensional data is an important task. It requires projecting or visualizing the cluster structures into a low dimensional space. The challenges are: 1) how to perform the projection or visualization with less information loss and 2) how to preserve the interpretability of the original data. Recent methods address these challenges simultaneously by unsupervised feature selection. They learn the cluster indicators based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor similarity graph, then select the features highly correlated with these indicators. Under this direction, many techniques, such as local discriminative analysis, nonnegative spectral analysis, nonnegative matrix factorization, etc., have been successfully introduced to make the selection more accurate. In this paper, we focus on enhancing the unsupervised feature selection in another perspective, namely, making the selection exploit the combination effect of the features. Given the expected feature amount, previous works operate on the whole features then select those of high coefficients one by one as the output. Our proposed method, instead, operates on a group of features initially then update the selection when a better group appears. Compared to the previous methods, the proposed method exploits the combination effect of the features by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell {}_{2,0}$ </tex-math></inline-formula> norm. It improves the selection accuracy where the cluster structures are strongly related to a group of features. We conduct the experiments on six open access data sets from different domains. The experimental results show that our proposed method is more accurate than the recent methods which do not specially consider the combination effect of the features.
NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models

With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.
Sampling-based optimal kinodynamic planning with motion primitives

This paper proposes a novel sampling-based motion planner, which integrates in Rapidly exploring Random Tree star ( $$\hbox {RRT}^{\star }$$ ) a database of pre-computed motion primitives to alleviate its computational load and allow for motion planning in a dynamic or partially known environment. The database is built by considering a set of initial and final state pairs in some grid space, and determining for each pair an optimal trajectory that is compatible with the system dynamics and constraints, while minimizing a cost. Nodes are progressively added to the tree of feasible trajectories in the $$\hbox {RRT}^{\star }$$ algorithm by extracting at random a sample in the gridded state space and selecting the best obstacle-free motion primitive in the database that joins it to an existing node. The tree is rewired if some nodes can be reached from the new sampled state through an obstacle-free motion primitive with lower cost. The computationally more intensive part of motion planning is thus moved to the preliminary offline phase of the database construction at the price of some performance degradation due to gridding. Grid resolution can be tuned so as to compromise between (sub)optimality and size of the database. The planner is shown to be asymptotically optimal as the grid resolution goes to zero and the number of sampled states grows to infinity.
Effect of Depth and Width on Local Minima in Deep Learning

In this paper, we analyze the effects of depth and width on the quality of local minima, without strong overparameterization and simplification assumptions in the literature. Without any simplification assumption, for deep nonlinear neural networks with the squared loss, we theoretically show that the quality of local minima tends to improve toward the global minimum value as depth and width increase. Furthermore, with a locally induced structure on deep nonlinear neural networks, the values of local minima of neural networks are theoretically proven to be no worse than the globally optimal values of corresponding classical machine learning models. We empirically support our theoretical observation with a synthetic data set, as well as MNIST, CIFAR-10, and SVHN data sets. When compared to previous studies with strong overparameterization assumptions, the results in this letter do not require overparameterization and instead show the gradual effects of overparameterization as consequences of general results.
Deep divergence-based approach to clustering

A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps.