Dynamic Tracing Research Articles

Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis

Code deobfuscation, which attempts to simplify code that has been intentionally obfuscated to prevent understanding, is a critical technique for downstream security analysis tasks like malware detection. While there has been significant prior work on code deobfuscation, most techniques either do not handle control flow obfuscations that modify control flow or they target specific classes of control flow obfuscations, making them unsuitable for handling new types of obfuscations or combinations of existing ones. In this paper, we study a new deobfuscation technique that is based on program synthesis and that can handle a broad class of control flow obfuscations. Given an obfuscated program P, our approach aims to synthesize a smallest program that is a control-flow reduction of P and that is semantically equivalent. Since our method does not assume knowledge about the types of obfuscations that have been applied to the original program, the underlying synthesis problem ends up being very challenging. To address this challenge, we propose a novel trace-informed compositional synthesis algorithm that leverages hints present in dynamic traces of the obfuscated program to decompose the synthesis problem into a set of simpler subproblems. In particular, we show how dynamic traces can be useful for inferring a suitable control-flow skeleton of the deobfuscated program and performing independent synthesis of each basic block. We have implemented this approach in a tool called Chisel and evaluate it on 546 benchmarks that have been obfuscated using combinations of six different obfuscation techniques. Our evaluation shows that our approach is effective and that it produces code that is almost identical (modulo variable renaming) to the original (non-obfuscated) program in 86% of cases. Our evaluation also shows that Chisel significantly outperforms existing techniques.

UV­B Persistent Luminescence of CaF2:Gd3+ for Radiation Labeling and Tracing

AbstractPersistent luminescence (PersL) has attracted considerable attention in the last two decades for its potential applications in the fields of emergency indicators, energy‐saving lighting, biomedical imaging, and dynamic anti‐counterfeiting. However, the wavelengths of PersL reported so far usually fall into the visible and near‐infrared range. The preparation of advanced optical materials bearing UV PersL remains elusive. Here the PersL characteristic of CaF2:Gd3+ is systematically studied. After exposure to X‐ray radiation, CaF2:Gd3+ continues to emit UV­‐B (UVB) PersL peaking at 313 nm, corresponding to the 6P7/2 → 8S7/2 transition of Gd3+. By virtue of X‐ray photoelectron spectroscopy measurements, the valence variation model is excluded to explain the UVB PersL of CaF2:Gd3+. Finally, it is demonstrated that such UVB PersL of CaF2:Gd3+ can be used for static labeling and dynamic tracing.

Artificial vision system based on porosity heterojunction for in-sensor processing and dynamic trace extraction

Artificial vision system based on porosity heterojunction for in-sensor processing and dynamic trace extraction

Improving the Selectivity and Stability of Supported Cobalt Catalysts via Static Bi-Doping and Dynamic Trace CO2 Co-Feeding During Propane Dehydrogenation.

Simultaneously enhancing selectivity and stability on supported propane dehydrogenation (PDH) catalysts remains a formidable challenge. Here, we report a combined static and dynamic strategy to address these issues synergistically. Firstly, we demonstrate a feasible sol-gel method for preparing atomically-dispersed Bi-decorated metal nanoparticle catalysts (MBi/Al2O3, M=Fe, Co, Ni, and Zn). In PDH testing, the total selectivity of by-products (CH4 and C2H6) significantly decreases to 4 % for CoBi catalysts due to the static Bi-doping, compared with 16 % for Co-supported catalysts. Secondly, to enhance catalytic stability, we introduce a dynamic trace CO2 co-feeding route. 10CoBi/Al2O3 catalysts exhibit superior durability against coke formation for 330 hours in PDH under a 40 % C3H8 atmosphere followed by pure C3H8 conditions at 600 °C while maintaining propylene selectivity at 96 %. Notably, introducing trace CO2 leads to a remarkable 6-fold decrease in the deactivation rate constant (kd). Multiple characterizations and density functional theory calculations reveal that charge transfer from atomically-distributed Bi to Co nanoparticles benefits lowering the energy of C3H6 adsorption thereby suppressing by-products. Furthermore, the dynamic co-feeding of trace CO2 facilitates coke removal, suppressing catalyst deactivation. The static Bi-doping and dynamic trace CO2 co-feeding strategy contributes simultaneously to increased selectivity and stability on supported PDH catalysts.

Improving the Selectivity and Stability of Supported Cobalt Catalysts via Static Bi‐Doping and Dynamic Trace CO2 Co‐feeding during Propane Dehydrogenation

Simultaneously enhancing selectivity and stability on supported propane dehydrogenation (PDH) catalysts remains a formidable challenge. Here, we report a combined static and dynamic strategy to address these issues synergistically. Firstly, we demonstrate a feasible sol‐gel method for preparing atomically‐dispersed Bi‐decorated metal nanoparticle catalysts (MBi/Al2O3, M= Fe, Co, Ni, and Zn). In PDH testing, the total selectivity of by‐products (CH4 and C2H6) significantly decreases to 4% for CoBi catalysts due to the static Bi‐doping, compared with 16% for Co‐supported catalysts. Secondly, to enhance catalytic stability, we introduce a dynamic trace CO2 co‐feeding route. 10CoBi/Al2O3 catalysts exhibit superior durability against coke formation for 330 hours in PDH under a 40% C3H8 atmosphere followed by pure C3H8 conditions at 600 °C while maintaining propylene selectivity at 96%. Notably, introducing trace CO2 leads to a remarkable 6‐fold decrease in the deactivation rate constant (kd). Multiple characterizations and density functional theory calculations reveal that charge transfer from atomically‐distributed Bi to Co nanoparticles benefits lowering the energy of C3H6 adsorption thereby suppressing by‐products. Furthermore, the dynamic co‐feeding of trace CO2 facilitates coke removal, suppressing catalyst deactivation. The static Bi‐doping and dynamic trace CO2 co‐feeding strategy contributes simultaneously to increased selectivity and stability on supported PDH catalysts.

Near-Infrared Response Organic Synaptic Transistor for Dynamic Trace Extraction.

The development of neuromorphic hardware capable of detecting and recognizing moving targets through an in-sensor computing strategy is considered to be an important component of the construction of edge computing systems with distributed computation. In addition to responsiveness to visible light, the implementation of neuromorphic hardware should also demonstrate the ability to sense and process nonvisible light, which is essential for tracking target object trajectories in specialized environments. In this work, we fabricated an organic synaptic transistor with a near-infrared (NIR) response by incorporating doped LaF3: Yb/Ho upconversion quantum dots (UCQDs) into the channel of a Poly3-hexylthiophene (P3HT)-based organic field effect transistor (FET), serving as charge trapping and infrared sensing sites. The obtained synaptic transistor not only replicates common synaptic behaviors when exposed to NIR illumination but also demonstrates potential applications for the dynamic trajectory recognition of animals in the dark. Compared to other monitoring technologies, P3HT transistors doped with LaF3: Yb/Ho UCQDs exhibit distinct advantages, including a NIR response, high-efficiency computing, and sensitivity, which provide an experimental foundation and a design reference for the development of next-generation intelligent dynamic image recognition systems.

One- and Two-Photon Photophysical Properties, Ultrafast Dynamics, and DFT Study of Three Modified Zinc Phthalocyanines.

As two-photon absorption (TPA) materials, phthalocyanine molecules have promising application prospects due to their large TPA absorption cross-section, high third-order nonlinear optical susceptibility, and ultrafast response characteristics. In this work, optical properties and the ultrafast response of three modified zinc phthalocyanine molecules (P-HPcZn, Pc-P-Pc, and (DR1)4PcZn) were analyzed. No obvious side-shoulder absorption peaks in the Q-band can be observed from the steady-state absorption spectra of the three molecules, confirming the lack of aggregation products in the solutions of our measurement. Open-aperture Z-scan results show relatively large TPA cross-section values of 136.4 and 55.3 GM for Pc-P-Pc and (DR1)4PcZn, respectively. The nonlinear optical results show that the absorption process observed under the excitation of femtosecond pulses is a reverse saturable absorption (RSA) mechanism. Up-conversion fluorescence spectra of (DR1)4PcZn in THF solution indicate that the fluorescence emission mechanism is TPA. In the study of ultrafast dynamics, the transient absorption spectra were investigated and the decay lifetime of the dynamic traces corresponding to some representative probe wavelengths was obtained through data fitting with a multi-exponential function. Finally, the charge transfer and excited state properties of the modified zinc phthalocyanine molecules were discussed in depth by the DFT method. The energy gaps of P-HPcZn, Pc-P-Pc, and (DR1)4PcZn are 2.16, 1.39, and 2.13 eV, respectively. The results indicate that the Pc-P-Pc of donor-acceptor-donor (D-A-D) structure has the smallest energy gap as well as the best charge transfer properties.

Color-reversible fluorescence tracking for the dynamic interaction of SO2 with Hg2+ in living cells

Mercury ion (Hg2+) is one of the most threatening substances to human health, and the mercury poisoning can damage physiological homeostasis severely in human, even cause death. Intriguingly, Sulfur dioxide (SO2), a gas signal molecule in human, can specifically interact with Hg2+ for relieving mercury poisoning. However, the dynamic interaction of Hg2+ with SO2 at the tempospatial level and the correlation between Hg2+ and SO2 in the pathological process of mercury poisoning are still elusive. Herein, we rationally designed a reversible and dual color fluorescent probe (CCS) for dynamically visualizing Hg2+ and SO2 and deciphering their interrelationship in mercury poisoning. CCS held good sensitivity, selectivity and reversibility to Hg2+ and SO2, that enabled CCS to specifically detect SO2 and Hg2+ via cyan fluorescence channel (centered around 485 nm) and red fluorescence channel (centered around 679 nm), respectively. Notably, the separate fluorescence signal changes of CCS realized the dynamic tracing of Hg2+ and SO2 in living cells, and presented the potential for exploring the correlation between SO2 and Hg2+ in mercury poisoning.

Asymptotic Ray Method for the Double Square Root Equation

The parabolic wave equation describes wave propagation in a preferable direction, which is usually horizontal in underwater acoustics and vertical in seismic applications. For dense receiver arrays (receiver spacing is less than signal wavelength), this equation can be used for propagating the recorded wavefield back into the medium for imaging sources and scattering objects. Similarly, for multiple source and receiver array acquisition, typical for seismic exploration and potentially beneficial for ocean acoustics, one can model data in one run using an extension of the parabolic equation—the pseudo-differential Double Square Root (DSR) equation. This extended equation allows for the modeling and imaging of multi-source data but operates in higher-dimensional space (source, receiver coordinates, and time), which makes its numerical computation time-consuming. In this paper, we apply a faster ray method for solving the DSR equation. We develop algorithms for both kinematic and dynamic ray tracing applicable to either data modeling or true-amplitude recovery. Our results can be used per se or as a basis for the future development of more elaborated asymptotic techniques that provide accurate and computationally feasible results.

High-Fidelity Sensitive Tracing Circulating Tumor Cell Telomerase Activity.

Dynamic tracing of intracellular telomerase activity plays a crucial role in cancer cell recognition and correspondingly in earlier cancer diagnosis and personalized precision therapy. However, due to the complexity of the required reaction system and insufficient loading of reaction components into cells, achieving a high-fidelity determination of telomerase activity is still a challenge. Herein, an Aptamer-Liposome mediated Telomerase activated poly-Molecular beacon Arborescent Nanoassembly(ALTMAN) approach was described for direct high-fidelity visualization of telomerase activity. Briefly, intracellular telomerase activates molecular beacons, causing their hairpin structures to unfold and produce fluorescent signals. Furthermore, multiple molecular beacons can self-assemble, forming arborescent nanostructures and leading to exponential amplification of fluorescent signals. Integrating the enzyme-free isothermal signal amplification successfully increased the sensitivity and reduced interference by leveraging the skillful design of the molecular beacon and the extension of the telomerase-activated TTAGGG repeat sequence. The proposed approach enabled ultrasensitive visualization of activated telomerase exclusively with a prominent detection limit of 2 cells·μL-1 and realized real-time imaging of telomerase activity in living cancer cells including blood samples from breast cancer patients and urine samples from bladder cancer patients. This approach opens an avenue for establishing a telomerase activity determination and in situ monitoring technique that can facilitate both telomerase fundamental biological studies and cancer diagnostics.

In Situ Imaging of Dynamic Current Paths in a Neuromorphic Nanoparticle Network with Critical Spiking Behavior

AbstractIn the strive for energy efficient computing, many different neuromorphic computing and engineering schemes have been introduced. Nanoparticle networks (NPNs) at the percolation threshold have been established as one of the promising candidates, e.g., for reservoir computing because among other useful properties they show self‐organization and brain‐like avalanche dynamics. The dynamic resistance changes trace back to spatio‐temporal reconfigurations in the connectivity upon resistive switching in distributed memristive nano‐junctions and nano‐gaps between neighboring nanoparticles. Until now, however, there has not yet been any direct imaging or monitoring of current paths in NPN. In this study, an NPN comprising of Ag/CxOyHzcore/shell and Ag nanoparticles at the percolation threshold is reported. It is shown that this NPN is within a critical regime, exhibiting avalanche dynamics. To monitor in situ the evolving current paths in this NPN, active voltage contrast and resistive contrast imaging are used complementarily. Including simulations, the results provide experimental insight toward understanding the complex current response of the memristive NPN. As such, this study paves the way toward an experimental characterization of dynamic reorganizations in current paths inside NPN, which is highly relevant for validating and improving simulations and finally establishing a deeper understanding of switching dynamics in NPNs.

Investigation of the accuracy of dynamic condylar position: A model study

ObjectivesTo evaluate dynamic condylar positions by integrating mandibular movement recording data and cone-beam computed tomography (CBCT) and to investigate its accuracy via dynamic model experiments. MethodsA polyvinyl chloride skull model was utilized. A robot arm was used to operate the mandible to perform mouth opening, closing, protrusion, and lateral movements. A recording device, worn on the skull, was used to record the dynamic process and an optical position tracking (OPT) system was used to simultaneously trace the movements. A self-developed software module was used to evaluate the dynamic condylar position by integrating the dynamic tracing data and a virtual skull model derived from CBCT images. Errors were defined as differences between the dynamic coordinates of six landmarks around the condylar area derived from the software module (test) and OPT system (gold standard). ResultsThe condylar position errors were 0.76 ± 0.31, 0.55 ± 0.15, and 0.68 ± 0.23 mm for mouth opening, bilateral, and protrusion movements, respectively. Furthermore, the errors for small, moderate, and large mouth opening movements were 0.62 ± 0.19, 0.69 ± 0.29, and 0.94 ± 0.31 mm, respectively. The errors for all movements, except for large mouth opening, were significantly less than 1 mm (P < 0.05). The error was not different from 1 mm in the large mouth opening movement (P > 0.05). ConclusionsOur developed method of achieving dynamic condylar position by integrating mandibular movement recording data and CBCT images is clinically reliable. Clinical significanceThis study proved the reliability of evaluating dynamic condylar position using a commercial dynamic recording instrument and CBCT images.

Two dimensional elastic dynamically focused beam migration in transversely isotropic (TI) media

The anisotropic ray tracing equations of elastic waves are derived for calculating the central ray's travel time, trajectory, and parameters P and Q. Based on the anisotropic ray tracing algorithm, we proposed a transversely isotropic elastic dynamically focused beam migration for two dimensional media. Based on the two dimensional Kirchhoff-Helmholtz integral of elastic waves, the elastic imaging weight coefficients are used to reduce the crosstalk noise. Then, based on the previous research, we introduced a sign function to eliminate the effect of polarity reversal in qPqSV wave migration. Through model tests, it can be found that the proposed method can process the multicomponent seismic data and image the complex structures for anisotropic media. The optimized dynamic ray tracing algorithm is superior to the conventional one in computational efficiency. Furthermore, the proposed method eliminates the limitations of the Gaussian beam imaging. This method not only does not affect the accuracy of shallow imaging, but also enhances the energy focusing and the amplitude of deep layers. The model tests have shown the accuracy and effectiveness of our proposed method.

A transient three-dimensional wheel-rail adhesion model under wet condition considering starvation and surface roughness

The objective of this paper is to propose a new three-dimensional wheel-rail adhesion model under wet condition which considers insufficient water supply, the measured surface roughness and dynamic effect of the vehicle and track coupling system. Firstly, a longitudinal vehicle/track coupling dynamic model (LVTDM) was developed to obtain the dynamic parameters and contact points trace in curved track. Then, an improved numerical method was used to solve the transient general starved Reynolds equations, utilizing the input dynamic parameters acquired from the LVTDM. The numerical model was validated under the starved steady condition for the smooth surface and subsequently applied to investigate wheel and rail contact behaviors under wet condition. The results show that the effect of starvation on the wheel-rail rolling contact behaviours becomes significant when the inlet film thickness is less than 1000 nm for smooth surface. The numerical results were also compared with experimental results conducted by a high-speed tribology facility. In addition, the time-dependent characteristics of the wheel-rail rolling contact were obtained at different time instants. The transient wheel-rail adhesion characteristics under wet condition for different train speeds were obtained. The transient wheel-rail adhesion modeling results shows that the train speed has a significant effect on the adhesion ability.

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

Side-Channel Attack Analysis on In-Memory Computing Architectures

In-memory computing (IMC) systems have great potential for accelerating data-intensive tasks such as deep neural networks (DNNs). As DNN models are generally highly proprietary, the neural network architectures become valuable targets for attacks. In IMC systems, since the whole model is mapped on chip and weight memory read can be restricted, the pre-mapped DNN model acts as a “black box” for users. However, the localized and stationary weight and data patterns may subject IMC systems to other attacks. In this paper, we propose a side-channel attack methodology on IMC architectures. We show that it is possible to extract model architectural information from power trace measurements without any prior knowledge of the neural network. We first developed a simulation framework that can emulate the dynamic power traces of the IMC macros. We then performed side-channel leakage analysis to reverse engineer model information such as the stored layer type, layer sequence, output channel/feature size and convolution kernel size from power traces of the IMC macros. Based on the extracted information, full networks can potentially be reconstructed without any knowledge of the neural network. Finally, we discuss potential countermeasures for building IMC systems that offer resistance to these model extraction attack.

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

Prove that Weak Management elements are lean waste that threatens Socio-economic stability using dynamic statistical tracing actions via Third Organizational Efficiency Theory.

Prove that Weak Management elements are lean waste that threatens Socio-economic stability using dynamic statistical tracing actions via Third Organizational Efficiency Theory.

Point cloud-based environment reconstruction and ray tracing simulations for railway tunnel channels

Radio propagation environment plays a critical role in the performance of wireless communication systems, and understanding channel characteristics is vital for ensuring reliable communication links and optimizing system performance. Ray tracing is an effective method to investigate propagation characteristics in a complex environment, and how to quickly and accurately obtain environmental information needs to be solved. This paper presents dynamic environment reconstruction and ray tracing simulation in railway tunnel environment based on Simultaneous Localization and Mapping (SLAM) algorithm and Poisson reconstruction algorithm. Accurate channel parameters are obtained and analyzed based on ray tracing simulation. Both straight and curved tunnels are considered and investigated, and the results show the channel characteristics in complex railway tunnel environments.

Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction

Neuro-inspired vision systems hold great promise to address the growing demands of mass data processing for edge computing, a distributed framework that brings computation and data storage closer to the sources of data. In addition to the capability of static image sensing and processing, the hardware implementation of a neuro-inspired vision system also requires the fulfilment of detecting and recognizing moving targets. Here, we demonstrated a neuro-inspired optical sensor based on two-dimensional NbS2/MoS2 hybrid films, which featured remarkable photo-induced conductance plasticity and low electrical energy consumption. A neuro-inspired optical sensor array with 10 × 10 NbS2/MoS2 phototransistors enabled highly integrated functions of sensing, memory, and contrast enhancement capabilities for static images, which benefits convolutional neural network (CNN) with a high image recognition accuracy. More importantly, in-sensor trajectory registration of moving light spots was experimentally implemented such that the post-processing could yield a high restoration accuracy. Our neuro-inspired optical sensor array could provide a fascinating platform for the implementation of high-performance artificial vision systems.