NeuroComb: Improving SAT Solving with Graph Neural Networks

Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers by improving their variable branching heuristics through predictions generated by Graph Neural Networks(GNNs). However, so far this approach either has not made solving more effective, or has required online access to substantial GPU resources. Aiming to make GNN improvements practical, this paper proposes an approach called NeuroComb, which builds on two insights: (1) predictions of important variables and clauses can be combined with dynamic branching into a more effective hybrid branching strategy, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts. NeuroComb is implemented as an enhancement to a classic CDCL solver called MiniSat and a more recent CDCL solver called Glucose. As a result, it allowed MiniSat to solve 11% and Glucose 5% more problems on the recent SATCOMP-2021 competition problem set, with the computational resource requirement of only one GPU. NeuroComb is therefore a both effective and practical approach to improving SAT solving through machine learning

SurgicalSAM: Efficient Class Promptable Surgical Instrument Segmentation

The Segment Anything Model (SAM) is a powerful foundation model that has revolutionised image segmentation. To apply SAM to surgical instrument segmentation, a common approach is to locate precise points or boxes of instruments and then use them as prompts for SAM in a zero-shot manner. However, we observe two problems with this naive pipeline: (1) the domain gap between natural objects and surgical instruments leads to poor generalisation of SAM; and (2) SAM relies on precise point or box locations for accurate segmentation, requiring either extensive manual guidance or a well-performing specialist detector for prompt preparation, which leads to a complex multi-stage pipeline. To address these problems, we introduce SurgicalSAM, a novel end-to-end efficient-tuning approach for SAM to effectively integrate surgical-specific information with SAM's pre-trained knowledge for improved generalisation. Specifically, we propose a lightweight prototype-based class prompt encoder for tuning, which directly generates prompt embeddings from class prototypes and eliminates the use of explicit prompts for improved robustness and a simpler pipeline. In addition, to address the low inter-class variance among surgical instrument categories, we propose contrastive prototype learning, further enhancing the discrimination of the class prototypes for more accurate class prompting. The results of extensive experiments on both EndoVis2018 and EndoVis2017 datasets demonstrate that SurgicalSAM achieves state-of-the-art performance while only requiring a small number of tunable parameters. The source code will be released at https://github.com/wenxi-yue/SurgicalSAM.Comment: Technical Report. The source code will be released at https://github.com/wenxi-yue/SurgicalSA

Robust Audio Anti-Spoofing with Fusion-Reconstruction Learning on Multi-Order Spectrograms

Robust audio anti-spoofing has been increasingly challenging due to the recent advancements on deepfake techniques. While spectrograms have demonstrated their capability for anti-spoofing, complementary information presented in multi-order spectral patterns have not been well explored, which limits their effectiveness for varying spoofing attacks. Therefore, we propose a novel deep learning method with a spectral fusion-reconstruction strategy, namely S2pecNet, to utilise multi-order spectral patterns for robust audio anti-spoofing representations. Specifically, spectral patterns up to second-order are fused in a coarse-to-fine manner and two branches are designed for the fine-level fusion from the spectral and temporal contexts. A reconstruction from the fused representation to the input spectrograms further reduces the potential fused information loss. Our method achieved the state-of-the-art performance with an EER of 0.77% on a widely used dataset: ASVspoof2019 LA Challenge

Diffraction of Quantum Dots Reveals Nanoscale Ultrafast Energy Localization

Unlike in bulk materials, energy transport in low-dimensional and nanoscale systems may be governed by a coherent “ballistic” behavior of lattice vibrations, the phonons. If dominant, such behavior would determine the mechanism for transport and relaxation in various energy-conversion applications. In order to study this coherent limit, both the spatial and temporal resolutions must be sufficient for the length-time scales involved. Here, we report observation of the lattice dynamics in nanoscale quantum dots of gallium arsenide using ultrafast electron diffraction. By varying the dot size from h = 11 to 46 nm, the length scale effect was examined, together with the temporal change. When the dot size is smaller than the inelastic phonon mean-free path, the energy remains localized in high-energy acoustic modes that travel coherently within the dot. As the dot size increases, an energy dissipation toward low-energy phonons takes place, and the transport becomes diffusive. Because ultrafast diffraction provides the atomic-scale resolution and a sufficiently high time resolution, other nanostructured materials can be studied similarly to elucidate the nature of dynamical energy localization

Ultrafast atomic-scale visualization of acoustic phonons generated by optically excited quantum dots

Understanding the dynamics of atomic vibrations confined in quasi-zero dimensional systems is crucial from both a fundamental point-of-view and a technological perspective. Using ultrafast electron diffraction, we monitored the lattice dynamics of GaAs quantum dots—grown by Droplet Epitaxy on AlGaAs—with sub-picosecond and sub-picometer resolutions. An ultrafast laser pulse nearly resonantly excites a confined exciton, which efficiently couples to high-energy acoustic phonons through the deformation potential mechanism. The transient behavior of the measured diffraction pattern reveals the nonequilibrium phonon dynamics both within the dots and in the region surrounding them. The experimental results are interpreted within the theoretical framework of a non-Markovian decoherence, according to which the optical excitation creates a localized polaron within the dot and a travelling phonon wavepacket that leaves the dot at the speed of sound. These findings indicate that integration of a phononic emitter in opto-electronic devices based on quantum dots for controlled communication processes can be fundamentally feasible

Carbon dots-based dual-emission ratiometric fluorescence sensor for dopamine detection

The detection of Dopamine (DA) is significant for disease surveillance and prevention. However, the development of the precise and simple detection techniques is still at a preliminary stage due to their high tester requirements, time-consuming process, and low accuracy. In this work, we present a novel dual-emission ratiometric fluorescence sensing system based on a hybrid of carbon dots (CDs) and 7-amino-4-methylcoumarin (AMC) to quickly monitor the DA concentration. Linked via amide bonds, the CDs and AMC offered dual-emissions with peaks located at 455 and 505 nm, respectively, under a single excitation wavelength of 300 nm. Attributed to the fluorescence of the CDs and AMC in the nanohybrid system can be quenched by DA, the concentration of DA could be quantitatively detected by monitoring the ratiometric ratio change in fluorescent intensity. More importantly, the CDs-AMC-based dual-emission ratiometric fluorescence sensing system demonstrated a remarkable linear relationship in the range of 0–33.6 μM to detection of DA, and a low detection limit of 5.67 nM. Additionally, this sensor successfully applied to the detection of DA in real samples. Therefore, the ratiometric fluorescence sensing system may become promising to find potential applications in biomedical dopamine detection

Stability of SARS-CoV-2 in cold-chain transportation environments and the efficacy of disinfection measures

BackgroundLow temperature is conducive to the survival of COVID-19. Some studies suggest that cold-chain environment may prolong the survival of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and increase the risk of transmission. However, the effect of cold-chain environmental factors and packaging materials on SARS-CoV-2 stability remains unclear.MethodsThis study aimed to reveal cold-chain environmental factors that preserve the stability of SARS-CoV-2 and further explore effective disinfection measures for SARS-CoV-2 in the cold-chain environment. The decay rate of SARS-CoV-2 pseudovirus in the cold-chain environment, on various types of packaging material surfaces, i.e., polyethylene plastic, stainless steel, Teflon and cardboard, and in frozen seawater was investigated. The influence of visible light (wavelength 450 nm-780 nm) and airflow on the stability of SARS-CoV-2 pseudovirus at -18°C was subsequently assessed.ResultsExperimental data show that SARS-CoV-2 pseudovirus decayed more rapidly on porous cardboard surfaces than on nonporous surfaces, including polyethylene (PE) plastic, stainless steel, and Teflon. Compared with that at 25°C, the decay rate of SARS-CoV-2 pseudovirus was significantly lower at low temperatures. Seawater preserved viral stability both at -18°C and with repeated freeze−thaw cycles compared with that in deionized water. Visible light from light-emitting diode (LED) illumination and airflow at -18°C reduced SARS-CoV-2 pseudovirus stability.ConclusionOur studies indicate that temperature and seawater in the cold chain are risk factors for SARS-CoV-2 transmission, and LED visible light irradiation and increased airflow may be used as disinfection measures for SARS-CoV-2 in the cold-chain environment

A Return Mapping Algorithm for Nonlinear Yield Criteria with the Equivalent Mohr–Coulomb Strength Parameters

This paper proposes a modified return mapping algorithm for a series of nonlinear yield criteria. The algorithm is established in the principal stress space and ignores the effect of the intermediate principal stress. Three stress return schemes are derived in this paper: return to the yield surface, return to the curve, and return to the apex point. The conditions used for determining the correct stress return type are also constructed. After the proposed algorithm is programmed in the finite element software, we merely need the equivalent Mohr–Coulomb (M-C) strength parameters, the derivatives of their functions, and the tensile strength of these nonlinear yield criteria. In addition, the Hoek–Brown (H-B) yield criterion is taken as an example to validate the proposed method. The results show that the updated stresses and the final principal stresses obtained by the proposed method are in good agreement with those obtained by other methods. Furthermore, the proposed method is more suitable for the associated plastic-flow rule