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The landscape of software developer learning resources has continuously evolved, with recent trends favoring engaging formats like video tutorials. The emergence of Large Language Models (LLMs) like ChatGPT presents a new learning paradigm. While existing research explores the potential of LLMs in software development and education, their impact on developers' learning and solution-seeking behavior remains unexplored. To address this gap, we conducted a survey targeting software developers and computer science students, gathering 341 responses, of which 268 were completed and analyzed. This study investigates how AI chatbots like ChatGPT have influenced developers' learning preferences when acquiring new skills, exploring technologies, and resolving programming issues. Through quantitative and qualitative analysis, we explore whether AI tools supplement or replace traditional learning resources such as video tutorials, written tutorials, and Q&A forums. Our findings reveal a nuanced view: while video tutorials continue to be highly preferred for their comprehensive coverage, a significant number of respondents view AI chatbots as potential replacements for written tutorials, underscoring a shift towards more interactive and personalized learning experiences. Additionally, AI chatbots are increasingly considered valuable supplements to video tutorials, indicating their growing role in the developers' learning resources. These insights offer valuable directions for educators and the software development community by shedding light on the evolving preferences toward learning resources in the era of ChatGPT., Comment: International Conference on Software Maintenance and Evolution (ICSME 2024)

Studying the dynamics of quantum many-body systems is often constrained by the limitations in probing relevant observables, especially in continuous systems. A powerful method to gain information about such systems is the reconstruction of local currents from the continuity equation. Here we extend this approach to extract the total phase fluctuation of adjacent Bose gases. We validate our technique numerically and demonstrate its effectiveness by analyzing data from selected experiments simulating 1D quantum field theories through the phase difference of two 1D Bose gases probed by interference. Our analysis reveals the previously hidden sector of the sum mode of the phase, which is important for studying long-time thermalization and out-of-equilibrium dynamics of the system, thereby expanding the scope and capabilities of cold-atomic quantum simulators., Comment: 5+9 pages, 12 figures

Landauer's principle bridges information theory and thermodynamics by linking the entropy change of a system during a process to the average energy dissipated to its environment. Although typically discussed in the context of erasing a single bit of information, Landauer's principle can be generalised to characterise irreversibility in out-of-equilibrium processes, such as those involving complex quantum many-body systems. Specifically, the relationship between the entropy change of the system and the energy dissipated to its environment can be decomposed into changes in quantum mutual information and a difference in relative entropies of the environment. Here we experimentally probe Landauer's principle in the quantum many-body regime using a quantum field simulator of ultracold Bose gases. Employing a dynamical tomographic reconstruction scheme, we track the temporal evolution of the quantum field following a global mass quench from a Klein-Gordon to a Tomonaga-Luttinger liquid model and analyse the information-theoretic contributions to Landauer's principle for various system-environment partitions of the composite system. Our results agree with theoretical predictions, interpreted using a semi-classical quasiparticle picture. Our work demonstrates the potential of ultracold atom-based quantum field simulators to experimentally investigate quantum thermodynamics.

Transport properties play a crucial role in defining materials as insulators, metals, or superconductors. A fundamental parameter in this regard is the Drude weight, which quantify the ballistic transport of charge carriers. In this work, we measure the Drude weights of an ultracold gas of interacting bosonic atoms confined to one dimension, characterising the induced atomic and energy currents in response to perturbations with an external potential. We induce currents through two distinct experimental protocols; by applying a constant force to the gas, and by joining two subsystems prepared in different equilibrium states. By virtue of integrability, dynamics of the system is governed by ballistically propagating, long-lived quasi-particle excitations, whereby Drude weights almost fully characterise large-scale transport. Indeed, our results align with predictions from a recently developed hydrodynamic theory, demonstrating almost fully dissipationless transport, even at finite temperatures and interactions. These findings not only provide experimental validation of the hydrodynamic predictions but also offer methodologies applicable to various condensed matter systems, facilitating further studies on the transport properties of strongly correlated quantum matter., Comment: 10 pages, 6 figures. Comments are welcome

Driven by a need for ever increasing chip performance and inclusion of innovative features, a growing number of semiconductor companies are opting for all-inclusive System-on-Chip (SoC) architectures. Although Moore's Law has been able to keep up with the demand for more complex logic, manufacturing large dies still poses a challenge. Increasingly the solution adopted to minimize the impact of silicon defects on manufacturing yield has been to split a design into multiple smaller dies called chiplets which are then brought together on a silicon interposer. Advanced 2.5D and 3D packaging techniques that enable this kind of integration also promise increased power efficiency and opportunities for heterogeneous integration. However, despite their advantages, chiplets are not without issues. Apart from manufacturing challenges that come with new packaging techniques, disaggregating a design into multiple logically and physically separate dies introduces new threats, including the possibility of tampering with and probing exposed data lines. In this paper we evaluate the exposure of chiplets to probing by applying laser contactless probing techniques to a chiplet-based AMD/Xilinx VU9P FPGA. First, we identify and map interposer wire drivers and show that probing them is easier compared to probing internal nodes. Lastly, we demonstrate that delay-based sensors, which can be used to protect against physical probes, are insufficient to protect against laser probing as the delay change due to laser probing is only 0.792ps even at 100\% laser power.

The security of integrated circuits (ICs) can be broken by sophisticated physical attacks relying on failure analysis methods. Optical probing is one of the most prominent examples of such attacks, which can be accomplished in a matter of days, even with limited knowledge of the IC under attack. Unfortunately, few countermeasures are proposed in the literature, and none has been fabricated and tested in practice. These countermeasures usually require changing the standard cell libraries and, thus, are incompatible with digital and programmable platforms, such as field programmable gate arrays (FPGAs). In this work, we shift our attention from preventing the attack to detecting and responding to it. We introduce LaserEscape, the first fully digital and FPGA-compatible countermeasure to detect and mitigate optical probing attacks. LaserEscape incorporates digital delay-based sensors to reliably detect the physical alteration on the fabric caused by laser beam irradiations in real time. Furthermore, as a response to the attack, LaserEscape deploys real-time hiding approaches using randomized hardware reconfigurability. It realizes 1) moving target defense (MTD) to physically move the sensitive circuity under attack out of the probing field of focus to protect secret keys and 2) polymorphism to logically obfuscate the functionality of the targeted circuit to counter function extraction and reverse engineering attempts. We demonstrate the effectiveness and resiliency of our approach by performing optical probing attacks on protected and unprotected designs on a 28-nm FPGA. Our results show that optical probing attacks can be reliably detected and mitigated without interrupting the chip's operation.

Printed circuit boards (PCBs) are an integral part of electronic systems. Hence, verifying their physical integrity in the presence of supply chain attacks (e.g., tampering and counterfeiting) is of utmost importance. Recently, tamper detection techniques grounded in impedance characterization of PCB's Power Delivery Network (PDN) have gained prominence due to their global detection coverage, non-invasive, and low-cost nature. Similar to other physical verification methods, these techniques rely on the existence of a physical golden sample for signature comparisons. However, having access to a physical golden sample for golden signature extraction is not feasible in many real-world scenarios. In this work, we assess the feasibility of eliminating a physical golden sample and replacing it with a simulated golden signature obtained by the PCB design files. By performing extensive simulation and measurements on an in-house designed PCB, we demonstrate how the parasitic impedance of the PCB components plays a major role in reaching a successful verification. Based on the obtained results and using statistical metrics, we show that we can mitigate the discrepancy between collected signatures from simulation and measurements.

Physical side-channel attacks can compromise the security of integrated circuits. Most physical side-channel attacks (e.g., power or electromagnetic) exploit the dynamic behavior of a chip, typically manifesting as changes in current consumption or voltage fluctuations where algorithmic countermeasures, such as masking, can effectively mitigate them. However, as demonstrated recently, these mitigation techniques are not entirely effective against backscattered side-channel attacks such as impedance analysis. In the case of an impedance attack, an adversary exploits the data-dependent impedance variations of the chip power delivery network (PDN) to extract secret information. In this work, we introduce RandOhm, which exploits a moving target defense (MTD) strategy based on the partial reconfiguration (PR) feature of mainstream FPGAs and programmable SoCs to defend against impedance side-channel attacks. We demonstrate that the information leakage through the PDN impedance could be significantly reduced via runtime reconfiguration of the secret-sensitive parts of the circuitry. Hence, by constantly randomizing the placement and routing of the circuit, one can decorrelate the data-dependent computation from the impedance value. Moreover, in contrast to existing PR-based countermeasures, RandOhm deploys open-source bitstream manipulation tools on programmable SoCs to speed up the randomization and provide real-time protection. To validate our claims, we apply RandOhm to AES ciphers realized on 28-nm FPGAs. We analyze the resiliency of our approach by performing non-profiled and profiled impedance analysis attacks and investigate the overhead of our mitigation in terms of delay and performance.

We discuss Hamiltonian learning in quantum field theories as a protocol for systematically extracting the operator content and coupling constants of effective field theory Hamiltonians from experimental data. Learning the Hamiltonian for varying spatial measurement resolutions gives access to field theories at different energy scales, and allows to learn a flow of Hamiltonians reminiscent of the renormalization group. Our method, which we demonstrate in both theoretical studies and available data from a quantum gas experiment, promises new ways of addressing the emergence of quantum field theories in quantum simulation experiments., Comment: 22 pages, 8 figures

In this work, we study the heat transfer from electron to phonon system within a five monolayer thin epitaxial Pb film on Si(111) upon fs-laser excitation. The response of the electron system is determined using time-resolved photoelectron spectroscopy while the lattice excitation is measured by means of the Debye-Waller effect in time-resolved reflection high-energy electron diffraction. The electrons lose their heat within 0.5 ps while the lattice temperature rises slowly in 3.5 to 8 ps, leaving a gap of 3-7 ps. We propose that the hidden energy is transiently stored in high-frequency phonon modes for which diffraction is insensitive and which are excited in 0.5 ps. Within a three-temperature model we use three heat baths, namely electrons, high-frequency and low-frequency phonon modes to simulate the observations. The excitation of low-frequency acoustic phonons, i.e., thermalization of the lattice is facilitated through anharmonic phonon-phonon interaction.

The COVID-19 pandemic has forced many people to limit their social activities, which has resulted in a rise in mental illnesses, particularly depression. To diagnose these illnesses with accuracy and speed, and prevent severe outcomes such as suicide, the use of machine learning has become increasingly important. Additionally, to provide precise and understandable diagnoses for better treatment, AI scientists and researchers must develop interpretable AI-based solutions. This article provides an overview of relevant articles in the field of machine learning and interpretable AI, which helps to understand the advantages and disadvantages of using AI in psychiatry disorder detection applications., Comment: 12 pages

Upon impulsive optical excitation of solid-state materials, the non-equilibrium flow of energy from the excited electronic system to the lattice degrees of freedom typically happens in a few picoseconds. Here we identified the surface of thin Bi films grown on Si(001) as an additional subsystem which is excited much slower on a 100 ps timescale that is caused by decoupling due to mismatched phonon dispersions relations of bulk and surface. Anharmonic coupling among the phonon systems provides pathways for excitations which exhibits a 1/T-dependence causing a speed-up of surface excitation at higher temperatures. A quantitative justification is provided by phonon Umklapp processes from lattice thermal conductivity of the Bi bulk. Three-temperature model simulations reveal a pronounced non-equilibrium situation up to nanoseconds: initially, the surface is colder than the bulk, that situation is then inverted during cooling and the surface feeds energy back into the bulk phonon system.

The critical behavior of the order-disorder phase transition in the buckled dimer structure of the Si(001) surface is investigated both theoretically by means of first-principles calculations and experimentally by spot profile analysis low-energy electron diffraction (SPA-LEED). We use density functional theory (DFT) with three different functionals commonly used for Si to determine the coupling constants of an effective lattice Hamiltonian describing the dimer interactions. Experimentally, the phase transition from the low-temperature $c(4 {\times} 2)$- to the high-temperature $p(2 {\times} 1)$-reconstructed surface is followed through the intensity and width of the superstructure spots within the temperature range 78-400 K. Near the critical temperature $T_c = 190.6\,\mathrm{K}$, we observe universal critical behavior of spot intensities and correlation lengths which falls into the universality class of the two-dimensional (2D) Ising model. From the ratio of correlation lengths along and across the dimer rows we determine effective nearest-neighbor couplings of an anisotropic 2D Ising model, $J_\parallel = (-24.9 \pm 0.9_\mathrm{stat} \pm 1.3_\mathrm{sys})\,\mathrm{meV}$ and $J_\perp = (-0.8 \pm 0.1_\mathrm{stat})\,\mathrm{meV}$. We find that the experimentally determined coupling constants of the Ising model can be reconciled with those of the more complex lattice Hamiltonian from DFT when the critical behavior is of primary interest. The anisotropy of the interactions derived from the experimental data via the 2D Ising model is best matched by DFT calculations using the PBEsol functional. The trends in the calculated anisotropy are consistent with the surface stress anisotropy predicted by the DFT functionals, pointing towards the role of surface stress reduction as a driving force for establishing the $c(4 {\times} 2)$-reconstructed ground state.

Solid-state quantum emitters embedded in circular Bragg resonators are attractive due to their ability to emit quantum states of light with high brightness and low multi-photon probability. As for any emitter-microcavity system, fabrication imperfections limit the spatial and spectral overlap of the emitter with the cavity mode, thus limiting their coupling strength. Here, we show that an initial spectral mismatch can be corrected after device fabrication by repeated wet chemical etching steps. We demonstrate ~16 nm wavelength tuning for optical modes in AlGaAs resonators on oxide, leading to a 4-fold Purcell enhancement of the emission of single embedded GaAs quantum dots. Numerical calculations reproduce the observations and suggest that the achievable performance of the resonator is only marginally affected in the explored tuning range. We expect the method to be applicable also to circular Bragg resonators based on other material platforms, thus increasing the device yield of cavity-enhanced solid-state quantum emitters.

Solar and stellar magnetic patches (i.e., magnetic fluxes that reach the surface from the interior) are believed to be the primary sources of a star's atmospheric conditions. Hence, detecting and identifying these features (also known as magnetic elements) are among the essential topics in the community. Here, we apply the complex network approach to recognize the solar magnetic patches. For this purpose, we use the line-of-sight magnetograms provided by the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory. We construct the magnetic network following a specific visibility graph condition between pairs of pixels with opposite polarities and search for possible links between these regions. The complex network approach also provides the ability to rank the patches based on their connectivity (i.e., degree of nodes) and importance (i.e., PageRank). The use of the developed algorithm in the identification of magnetic patches is examined by tracking the features in consecutive frames, as well as making a comparison with the other approaches to identification. We find that this method could conveniently identify features regardless of their sizes. For small-scale (one or two pixels) features, we estimate the average of 8% false-positive and 1% false-negative errors.

The threats of physical side-channel attacks and their countermeasures have been widely researched. Most physical side-channel attacks rely on the unavoidable influence of computation or storage on current consumption or voltage drop on a chip. Such data-dependent influence can be exploited by, for instance, power or electromagnetic analysis. In this work, we introduce a novel non-invasive physical side-channel attack, which exploits the data-dependent changes in the impedance of the chip. Our attack relies on the fact that the temporarily stored contents in registers alter the physical characteristics of the circuit, which results in changes in the die's impedance. To sense such impedance variations, we deploy a well-known RF/microwave method called scattering parameter analysis, in which we inject sine wave signals with high frequencies into the system's power distribution network (PDN) and measure the echo of the signals. We demonstrate that according to the content bits and physical location of a register, the reflected signal is modulated differently at various frequency points enabling the simultaneous and independent probing of individual registers. Such side-channel leakage challenges the $t$-probing security model assumption used in masking, which is a prominent side-channel countermeasure. To validate our claims, we mount non-profiled and profiled impedance analysis attacks on hardware implementations of unprotected and high-order masked AES. We show that in the case of the profiled attack, only a single trace is required to recover the secret key. Finally, we discuss how a specific class of hiding countermeasures might be effective against impedance leakage.