Search

Best-performing speech models are trained on large amounts of data in the language they are meant to work for. However, most languages have sparse data, making training models challenging. This shortage of data is even more prevalent in speech emotion recognition. Our work explores the model's performance in limited data, specifically for speech emotion recognition. Meta-learning specializes in improving the few-shot learning. As a result, we employ meta-learning techniques on speech emotion recognition tasks, accent recognition, and person identification. To this end, we propose a series of improvements over the multistage meta-learning method. Unlike other works focusing on smaller models due to the high computational cost of meta-learning algorithms, we take a more practical approach. We incorporate a large pre-trained backbone and a prototypical network, making our methods more feasible and applicable. Our most notable contribution is an improved fine-tuning technique during meta-testing that significantly boosts the performance on out-of-distribution datasets. This result, together with incremental improvements from several other works, helped us achieve accuracy scores of 83.78% and 56.30% for Greek and Romanian speech emotion recognition datasets not included in the training or validation splits in the context of 4-way 5-shot learning., Comment: 16 pages, 1 figure, Accepted by WISE 2024

With the demand for scalable cryogenic microwave circuitry continuously rising, recently developed flexible microwave striplines offer the tantalyzing perspective of increasing the cabling density by an order of magnitude without thermally overloading the cryostat. We use a superconducting quantum circuit to test the thermalization of input flex cables with integrated $60\,$dB of attenuation distributed at various temperature stages. From the measured decoherence rate of a superconducting fluxonium qubit, we estimate a residual population of the readout resonator below $3.5\cdot10^{-3}$ photons and we measure a $0.28\,$ms thermalization time for the flexible stripline attenuators. Furthermore, we confirm that the qubit reaches an effective temperature of $26.4\,$mK, close to the base temperature of the cryostat, practically the same as when using a conventional semi-rigid coaxial cable setup.

Two-dimensional (2D) semiconductors are promising for low-power complementary metal oxide semiconductor (CMOS) electronics, which require ultrathin n- and p-type transistor channels. Among 2D semiconductors, WS2 is expected to have good conduction for both electrons and holes, but p-type WS2 transistors have been difficult to realize due to the relatively deep valence band and the presence of mid-gap states with conventional metal contacts. Here, we report topological semimetal NbP as p-type electrical contacts to bilayer WS2 with up to 5.8 microamperes per micron hole current at room temperature; this is the highest to date for sub 2 nm thin WS2 and more than 50 times larger than with metals like Ni or Pd. The p-type conduction is enabled by the simultaneously high work function and low density of states of the NbP, which reduce Fermi level pinning. These contacts are sputter-deposited at room temperature, an approach compatible with CMOS fabrication, a step towards enabling ultrathin WS2 semiconductors in future nanoelectronics.

The electrical resistivity of conventional metals, such as copper, is known to increase in thinner films due to electron-surface scattering, limiting the performance of metals in nanoscale electronics. Here, we uncover an exceptional reduction of resistivity with decreasing film thickness in NbP semimetal, deposited at relatively low temperatures of 400 {\deg}C. In sub-5 nm thin films, we find a significantly lower resistivity (~34 microOhm.cm for 1.5 nm thin NbP, at room temperature) than in the bulk form, and lower than conventional metals at similar thickness. Remarkably, the NbP films are not crystalline, but display local nanocrystalline, short-range order within an amorphous matrix. Our analysis suggests that the lower resistivity is due to conduction through surface channels, together with high surface carrier density and sufficiently good mobility, as the film thickness is reduced. These results and the fundamental insights obtained here could enable ultrathin, low-resistivity wires for nanoelectronics, beyond the limitations of conventional metals.

Quantum computation with bosonic modes presents a powerful paradigm for harnessing the principles of quantum mechanics to perform complex information processing tasks. In constructing a bosonic qubit with superconducting circuits, nonlinearity is typically introduced to a cavity mode through an ancillary two-level qubit. However, the ancilla's spurious heating has impeded progress towards fully fault-tolerant bosonic qubits. The ability to in situ decouple the ancilla when not in use would be beneficial but has so far only been realized with tunable couplers or additional parametric drives. This work presents a novel architecture for quantum information processing, comprising a 3D post cavity coupled to a fluxonium ancilla via a readout resonator. This system's intricate energy level structure results in a complex landscape of interactions whose sign can be tuned in situ by the magnetic field threading the fluxonium loop without the need of additional elements. Our results could significantly advance the lifetime and controllability of bosonic qubits.

When modifying existing policies in high-risk settings, it is often necessary to ensure with high certainty that the newly proposed policy improves upon a baseline, such as the status quo. In this work, we consider the problem of safe policy improvement, where one only adopts a new policy if it is deemed to be better than the specified baseline with at least pre-specified probability. We focus on threshold policies, a ubiquitous class of policies with applications in economics, healthcare, and digital advertising. Existing methods rely on potentially underpowered safety checks and limit the opportunities for finding safe improvements, so too often they must revert to the baseline to maintain safety. We overcome these issues by leveraging the most powerful safety test in the asymptotic regime and allowing for multiple candidates to be tested for improvement over the baseline. We show that in adversarial settings, our approach controls the rate of adopting a policy worse than the baseline to the pre-specified error level, even in moderate sample sizes. We present CSPI and CSPI-MT, two novel heuristics for selecting cutoff(s) to maximize the policy improvement from baseline. We demonstrate through both synthetic and external datasets that our approaches improve both the detection rates of safe policies and the realized improvement, particularly under stringent safety requirements and low signal-to-noise conditions.

Offensive language detection is a crucial task in today's digital landscape, where online platforms grapple with maintaining a respectful and inclusive environment. However, building robust offensive language detection models requires large amounts of labeled data, which can be expensive and time-consuming to obtain. Semi-supervised learning offers a feasible solution by utilizing labeled and unlabeled data to create more accurate and robust models. In this paper, we explore a few different semi-supervised methods, as well as data augmentation techniques. Concretely, we implemented eight semi-supervised methods and ran experiments for them using only the available data in the RO-Offense dataset and applying five augmentation techniques before feeding the data to the models. Experimental results demonstrate that some of them benefit more from augmentations than others., Comment: Accepted at KES 2024

We demonstrate a qubit-readout architecture where the dispersive coupling is entirely mediated by a kinetic inductance. This allows us to engineer the dispersive shift of the readout resonator independent of the qubit and resonator capacitances. We validate the pure kinetic coupling concept and demonstrate various generalized flux qubit regimes from plasmon to fluxon, with dispersive shifts ranging from 60 kHz to 2 MHz at the half-flux quantum sweet spot. We achieve readout performances comparable to conventional architectures with quantum state preparation fidelities of 99.7 % and 92.7 % for the ground and excited states, respectively, and below 0.1 % leakage to non-computational states., Comment: 20 pages, 10 figures

We present a systematic study of the dependence of gap mode tip-enhanced Raman scattering (TERS) of mono- and bi-layer WS$_2$ and MoS$_2$ as a function of excitation laser energy. We collected consecutive TERS maps of mono-and bi-layer regions with 6 different excitation lasers. To decrease the acquisition time, we used for the first time concurrent excitation and collection with two lasers simultaneously. We found that the E$_{2g}$/A$_{1g}$ peak intensity ratio for bilayer WS$_2$@Ag and the A'/A$_{1g}$ peak intensity ratio of the out-of-plane modes for mono- and bilayer change in a significantly non-monotonous way with excitation laser energies from 1.58 to 2.62 eV. The former ratio increases at energies corresponding to A and B excitons in bilayer WS$_2$. The intensity of the A peak in the monolayer, and hence the A/A$_{1g}$ ratio, is surprisingly high at low excitation energies, dips dramatically at energy corresponding to the A exciton, and is restored partially in between A and B excitons, though still showing a descending trend with increasing energy. A similar picture was observed in mono- and bi-layer MoS$_2$, though the existing set of lasers did not match its excitonic profile as nicely as for WS$_2$. We attribute the observed behavior to intermediate (Fano resonance) or strong (Rabi splitting) coupling between the excitons in transition metal dichalcogenides (TMDs) and the plasmons in the tip-substrate nanocavity. This is akin to the so-called Fano (Rabi) transparency experimentally observed in far field scattering from TMDs between two plasmonic metals. The possibility of intermediate/strong coupling between excitonic resonances in TMDs and the nanocavity re-evaluates the role of resonances in gap-mode TERS and should become an important factor to be considered by TERS practitioners when planning experiments. Finally, we propose the ideal substrate for efficient TERS and tip enhanced photoluminescence measurements., Comment: 21 pages, 10 figures

Self-models have been a topic of great interest for decades in studies of human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we show that when artificial networks learn to predict their internal states as an auxiliary task, they change in a fundamental way. To better perform the self-model task, the network learns to make itself simpler, more regularized, more parameter-efficient, and therefore more amenable to being predictively modeled. To test the hypothesis of self-regularizing through self-modeling, we used a range of network architectures performing three classification tasks across two modalities. In all cases, adding self-modeling caused a significant reduction in network complexity. The reduction was observed in two ways. First, the distribution of weights was narrower when self-modeling was present. Second, a measure of network complexity, the real log canonical threshold (RLCT), was smaller when self-modeling was present. Not only were measures of complexity reduced, but the reduction became more pronounced as greater training weight was placed on the auxiliary task of self-modeling. These results strongly support the hypothesis that self-modeling is more than simply a network learning to predict itself. The learning has a restructuring effect, reducing complexity and increasing parameter efficiency. This self-regularization may help explain some of the benefits of self-models reported in recent machine learning literature, as well as the adaptive value of self-models to biological systems. In particular, these findings may shed light on the possible interaction between the ability to model oneself and the ability to be more easily modeled by others in a social or cooperative context.

This research dissects financial equity research reports (ERRs) by mapping their content into categories. There is insufficient empirical analysis of the questions answered in ERRs. In particular, it is not understood how frequently certain information appears, what information is considered essential, and what information requires human judgment to distill into an ERR. The study analyzes 72 ERRs sentence-by-sentence, classifying their 4940 sentences into 169 unique question archetypes. We did not predefine the questions but derived them solely from the statements in the ERRs. This approach provides an unbiased view of the content of the observed ERRs. Subsequently, we used public corporate reports to classify the questions' potential for automation. Answers were labeled "text-extractable" if the answers to the question were accessible in corporate reports. 78.7% of the questions in ERRs can be automated. Those automatable question consist of 48.2% text-extractable (suited to processing by large language models, LLMs) and 30.5% database-extractable questions. Only 21.3% of questions require human judgment to answer. We empirically validate using Llama-3-70B and GPT-4-turbo-2024-04-09 that recent advances in language generation and information extraction enable the automation of approximately 80% of the statements in ERRs. Surprisingly, the models complement each other's strengths and weaknesses well. The research confirms that the current writing process of ERRs can likely benefit from additional automation, improving quality and efficiency. The research thus allows us to quantify the potential impacts of introducing large language models in the ERR writing process. The full question list, including the archetypes and their frequency, will be made available online after peer review., Comment: JEL classes: C45; G11; G12; G14

High kinetic inductance superconductors are gaining increasing interest for the realisation of qubits, amplifiers and detectors. Moreover, thanks to their high impedance, quantum buses made of such materials enable large zero-point fluctuations of the voltage, boosting the coupling rates to spin and charge qubits. However, fully exploiting the potential of disordered or granular superconductors is challenging, as their inductance and, therefore, impedance at high values are difficult to control. Here we have integrated a granular aluminium resonator, having a characteristic impedance exceeding the resistance quantum, with a germanium double quantum dot and demonstrate strong charge-photon coupling with a rate of $g_\text{c}/2\pi= (566 \pm 2)$ MHz. This was achieved due to the realisation of a wireless ohmmeter, which allows \emph{in situ} measurements during film deposition and, therefore, control of the kinetic inductance of granular aluminium films. Reproducible fabrication of circuits with impedances (inductances) exceeding 13 k$\Omega$ (1 nH per square) is now possible. This broadly applicable method opens the path for novel qubits and high-fidelity, long-distance two-qubit gates.

In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of semi-realistic role-playing exercises designed to elicit such behaviors, we explore the response dynamics of LLMs post fine-tuning interventions. Our methodology involves prompting models for Chain-of-Thought (CoT) reasoning and analyzing the coherence between the reasoning traces and the resultant outputs. Notably, we identify a pervasive phenomenon we term \emph{reason-based deception}, where models either stop producing reasoning traces or produce seemingly ethical reasoning traces that belie the unethical nature of their final outputs. We further examine the efficacy of response strategies (polite refusal versus explicit rebuttal) in curbing the occurrence of undesired behavior in subsequent outputs of multi-turn interactions. Our findings reveal that explicit rebuttals significantly outperform polite refusals in preventing the continuation of undesired outputs and nearly eliminate reason-based deception, challenging current practices in model fine-tuning. Accordingly, the two key contributions of this paper are (1) defining and studying reason-based deception, a new type of hidden behavior, and (2) demonstrating that rebuttals provide a more robust response model to harmful requests than refusals, thereby highlighting the need to reconsider the response strategies in fine-tuning approaches., Comment: ICLR 2024 AGI Workshop Poster

To reduce the source of potential exploits, binary debloating or specialization tools are used to remove unnecessary code from binaries. This paper presents a new binary debloating and specialization tool, LeanBin, that harnesses lifting and recompilation, based on observed execution traces. The dynamically recorded execution traces capture the required subset of instructions and control flow of the application binary for a given set of inputs. This initial control flow is subsequently augmented using heuristic-free static analysis to avoid excessively restricting the input space. The further structuring of the control flow and translation of binary instructions into a subset of C enables a lightweight generation of the code that can be recompiled, obtaining LLVM IR and a new debloated binary. Unlike most debloating approaches, LeanBin enables both binary debloating of the application and shared libraries, while reusing the existing compiler infrastructure. Additionally, unlike existing binary lifters, it does not rely on potentially unsound heuristics used by static lifters, nor suffers from long execution times, a limitation of existing dynamic lifters. Instead, LeanBin combines both heuristic-free static and dynamic analysis. The run time of lifting and debloating SPEC CPU2006 INT benchmarks has a geomean of 1.78$\times$, normalized to the native execution, and the debloated binary runs with a geomean overhead of 1.21$\times$. The percentage of gadgets, compared to the original binary, has a geomean between 24.10% and 30.22%, depending on the debloating strategy; and the code size can be as low as 53.59%. For the SQLite use-case, LeanBin debloats a binary including its shared library and generates a debloated binary that runs up to 1.24$\times$ faster with 3.65% gadgets., Comment: Accepted to ASE 2024; 14 pages, 7 figures

Multichannel quantum defect theory is applied in the treatment of the dissociative recombination and vibrational excitation processes for the BeD$^+$ ion in the twenty four vibrational levels of its ground electronic state ($\textrm{X}\,{^{1}\Sigma^{+}},v_{i}^{+}=0\ldots 23$). Three electronic symmetries of BeD$^{**}$ states (\ensuremath{^{2}\Pi}, \ensuremath{^{2}\Sigma^{+}}, and \ensuremath{^{2}\Delta}), are considered in the calculation of cross sections and the corresponding rate coefficients. The incident electron energy range is $10^{-5}$--2.7 eV and the electron temperature range is 100--5000~K. The vibrational dependence of these collisional processes is highlighted. The resulting data are useful in magnetic confinement fusion edge plasma modelling and spectroscopy, in devices with beryllium based main chamber materials, such as ITER and JET, and operating with the deuterium-tritium fuel mix. An extensive rate coefficients database is presented in graphical form and also by analytic fit functions whose parameters are tabulated in the supplementary material., Comment: 7 pages, 5 figures, 3 tables

Strain engineering has played a key role in modern silicon electronics, having been introduced as a mobility booster in the 1990s and commercialized in the early 2000s. Achieving similar advances with two-dimensional (2D) semiconductors in a CMOS (complementary metal oxide semiconductor) compatible manner would radically improve the industrial viability of 2D transistors. Here, we show silicon nitride capping layers can impart strain to monolayer MoS2 transistors on conventional silicon substrates, enhancing their electrical performance with a low thermal budget (350 {\deg}C), CMOS-compatible approach. Strained back-gated and dual-gated MoS2 transistors demonstrate median increases up to 60% and 45% in on-state current, respectively. The greatest improvements are found when both transistor channels and contacts are reduced to ~200 nm, reaching saturation currents of 488 uA/um, higher than any previous reports at such short contact pitch. Simulations reveal that most benefits arise from tensile strain lowering the contact Schottky barriers, and that further reducing device dimensions (including contacts) will continue to offer increased strain and performance improvements.

A series of Multichannel Quantum Defect Theory-based computations have been performed, in order to produce the cross sections of rotational transitions (excitations $N_{i}^{+}-2 \rightarrow$ $N_{i}^{+}$, de-excitations $N_{i}^{+}$ $\rightarrow$ $N_{i}^{+}-2$, with $N_{i}^{+}=2$ to $10$) and of their competitive process, the dissociative recombination, induced by collisions of HD$^+$ ions with electrons in the energy range $10^{-5}$ to 0.3 eV. Maxwell anisotropic rate coefficients, obtained from these cross sections in the conditions of the Heidelberg Test Storage Ring (TSR) experiments ($k_{B}T_{t}=2.8$ meV and $k_{B}T_{l}=45$ $\mu$eV), have been reported for those processes in the same electronic energy range. Maxwell isotropic rate coefficients have been as well presented for electronic temperatures up to a few hundreds of Kelvins. Very good overall agreement is found between our results for rotational transitions and the former theoretical computations as well as with experiment. Furthermore, owing to the full rotational computations performed, the accuracy of the resulting dissociative recombination cross sections is considerably improved., Comment: 9 pages, 8 figures, 1 table. arXiv admin note: text overlap with arXiv:1107.5267

This article extends the known restricted isometric projection of sparse datasets in Euclidean spaces $\mathbb{R}^N$ down into low-dimensional subspaces $\mathbb{R}^k, k \ll N,$ to the case of low-dimensional varieties $\mathcal{M} \subset \mathbb{R}^N,$ of codimension $N - k = \omega(N)$. Applications to structured/hierarchical datasets are considered.

The latest molecular data - potential energy curves and Rydberg-valence interactions - characterising the super-excited electronic states of BF are reviewed in order to provide the input for the study of their fragmentation dynamics. Starting from this input, the main paths and mechanisms of BF$^+$ dissociative recombination and vibrational excitation are analysed. Their cross sections are computed for the first time using a method based on the multichannel quantum defect theory (MQDT), and Maxwellian rate-coefficients are calculated and displayed in ready-to-be-used format for low temperature plasma kinetics simulations., Comment: 8 pages, 4 figures, 4 tables

We reobtain and often refine prior criteria due to Kaplansky, McGovern, Roitman, Shchedryk, Wiegand, and Zabavsky--Bilavska and obtain new criteria for a Hermite ring to be an \textsl{EDR}. We mention three criteria: (1) a Hermite ring $R$ is an \textsl{EDR} iff for all pairs $(a,c)\in R^2$, the product homomorphism $U(R/Rac)\times U\bigl(R/Rc(1-a)\bigr)\to U(R/Rc)$ between groups of units is surjective; (2) a reduced Hermite ring is an \textsl{EDR} iff it is a pre-Schreier ring and for each $a\in R$, every zero determinant unimodular $2\times 2$ matrix with entries in $R/Ra$ lifts to a zero determinant matrix with entries in $R$; (3) a B\'{e}zout domain $R$ is an \textsl{EDD} iff for all triples $(a,b,c)\in R^3$ there exists a unimodular pair $(e,f)\in R^2$ such that $(a,e)$ and $(be+af,1-a-bc)$ are unimodular pairs. We use these criteria to show that each B\'{e}zout ring $R$ that is an $(SU)_2$ ring (as introduced by Lorenzini) such that for each nonzero $a\in R$ there exists no nontrivial self-dual projective $R/Ra$-module of rank $1$ generated by $2$ elements (e.g., all its elements are squares), is an \textsl{EDR}., Comment: 19 pages. arXiv admin note: substantial text overlap with arXiv:2303.08413

The mobility of emerging (e.g., two-dimensional, oxide, organic) semiconductors is commonly estimated from transistor current-voltage measurements. However, such devices often experience contact gating, i.e., electric fields from the gate modulate the contact resistance during measurements, which can lead conventional extraction techniques to estimate mobility incorrectly even by a factor >2. This error can be minimized by measuring transistors at high gate-source bias, |$V_\mathrm{gs}$|, but this regime is often inaccessible in emerging devices that suffer from high contact resistance or early gate dielectric breakdown. Here, we propose a method of extracting mobility in transistors with gate-dependent contact resistance that does not require operation at high |$V_\mathrm{gs}$|, enabling accurate mobility extraction even in emerging transistors with strong contact gating. Our approach relies on updating the transfer length method (TLM) and can achieve <10% error even in regimes where conventional techniques overestimate mobility by >2$\times$., Comment: Corrected values tabulated in Figure 2d (surrounding discussion/conclusions unchanged); updated discussion surrounding Monte Carlo approach for error propagation; corrected typos

A unimodular $2\times 2$ matrix $A$ with entries in a commutative ring $R$ is called weakly determinant liftable if there exists a matrix $B$ congruent to $A$ modulo $R\det(A)$ and $\det(B)=0$; if we can choose $B$ to be unimodular, then $A$ is called determinant liftable. If $A$ is extendable to an invertible $3\times 3$ matrix $A^+$, then $A$ is weakly determinant liftable. If $A$ is simple extendable (i.e., we can choose $A^+$ such that its $(3,3)$ entry is $0$), then $A$ is determinant liftable. We present necessary and/or sufficient criteria for $A$ to be (weakly) determinant liftable and we use them to show that if $R$ is a $\Pi_2$ ring in the sense of Part I (resp.\ is a pre-Schreier domain), then $A$ is simply extendable (resp.\ extendable) iff it is determinant liftable (resp.\ weakly determinant liftable). As an application we show that each $J_{2,1}$ domain (as defined by Lorenzini) is an elementary divisor domain., Comment: 13 pages. Part II of the splitting of arXiv:2303.08413

The paper presents the methodology used for accuracy and repeatability measurements of the experimental model of a parallel robot developed for surgical applications. The experimental setup uses a motion tracking system (for accuracy) and a high precision measuring arm for position (for repeatability). The accuracy was obtained by comparing the trajectory data from the experimental measurement with a baseline trajectory defined with the kinematic models of the parallel robotic system. The repeatability was experi-mentally determined by moving (repeatedly) the robot platform in predefined points.

The present study focuses on the intrinsic properties of the SmFe10Co2-xVx (x = 0-2) alloys, which includes the SmFe10Co2 alloy, one of the most promising permanent magnets with the ThMn12 type of structure due to its large saturation magnetization (1.78 T), high Curie temperature (Tc = 859 K), and anisotropy field (12 T) experimentally obtained. Unfortunately, its low coercivity (<0.4 T) hinders its use in permanent magnet applications. The effect of V-doping on magnetization, magnetocrystalline anisotropy energy, and Curie temperature is investigated by electronic band structure calculations. The spin-polarized fully relativistic Korringa-Kohn-Rostoker (SPR-KKR) band structure method, which employs the coherent potential approximation (CPA) to deal with substitutional disorder, has been used. The Hubbard-U correction to local spin density approximation (LSDA +U) was used to account for the large correlation effects due to the 4f electronic states of Sm. The computed magnetic moments and magnetocrystalline anisotropy energies were compared with existing experimental data to validate the theoretical approach's reliability. The exchange-coupling parameters from the Heisenberg model were used for obtaining the mean-field estimated Curie temperature. The magnetic anisotropy energy was separated into contributions from transition metals and Sm, and its relationships with the local environment, interatomic distances, and valence electron delocalization were analyzed. The suitability of the hypothetical SmFe10CoV alloy for permanent magnet manufacture was assessed using the calculated anisotropy field, magnetic hardness, and intrinsic magnetic properties., Comment: 21 pages, 5 figures

A unimodular $2\times 2$ matrix with entries in a commutative $R$ is called extendable (resp.\ simply extendable) if it extends to an invertible $3\times 3$ matrix (resp.\ invertible $3\times 3$ matrix whose $(3,3)$ entry is $0$). We obtain necessary and sufficient conditions for a unimodular $2\times 2$ matrix to be extendable (resp.\ simply extendable) and use them to study the class $E_2$ (resp.\ $SE_2$) of rings $R$ with the property that all unimodular $2\times 2$ matrices with entries in $R$ are extendable (resp.\ simply extendable). We also study the larger class $\Pi_2$ of rings $R$ with the property that all unimodular $2\times 2$ matrices of determinant $0$ and with entries in $R$ are (simply) extendable (e.g., rings with trivial Picard groups or pre-Schreier domains). Among Dedekind domains, polynomial rings over $\mathbb Z$ and Hermite rings, only the EDRs belong to the class $E_2$ or $SE_2$. If $as(R)\le 2$, then $R$ is an $E_2$ ring iff it is an $SE_2$ ring., Comment: 17 pages in the original format (18 pages in the archive format). arXiv admin note: substantial text overlap with arXiv:2303.08413

We consider the homogenisation of a coupled Stokes flow and advection-reaction-diffusion problem in a perforated domain with an evolving microstructure of size $\varepsilon$. Reactions at the boundaries of the microscopic interfaces lead to the formation of a solid layer having a variable, a priori unknown thickness. This results in a growth or shrinkage of the solid phase and, thus, the domain evolution is not known a priori but induced by the advection-reaction-diffusion process. The achievements of this work are the existence and uniqueness of a weak microscopic solution and the rigorous derivation of an effective model for $\varepsilon \to 0$, based on $\varepsilon$-uniform a priori estimates. As a result of the limit passage, the processes on the macroscale are described by an advection-reaction-diffusion problem coupled to Darcy's equation with effective coefficients (porosity, diffusivity and permeability) depending on local cell problems. These local problems are formulated on cells, which depend on the macroscopic position and evolve in time. In particular, the evolution of these cells depends on the macroscopic concentration. Thus, the cell problems (respectively the effective coefficients) are coupled to the macroscopic unknowns and vice versa, leading to a strongly coupled micro-macro model. For pure reactive-diffusive transport coupled with microscopic domain evolution but without advective transport, homogenisation results have recently been presented. We extend these models by advective transport which is driven by the Stokes equation in the a priori unknown evolving pore domain., Comment: 44 pages

We propose and analyse numerical schemes for a system of quasilinear, degenerate evolution equations modelling biofilm growth as well as other processes such as flow through porous media and the spreading of wildfires. The first equation in the system is parabolic and exhibits degenerate and singular diffusion, while the second is either uniformly parabolic or an ordinary differential equation. First, we introduce a semi-implicit time discretisation that has the benefit of decoupling the equations. We prove the positivity, boundedness, and convergence of the time-discrete solutions to the time-continuous solution. Then, we introduce an iterative linearisation scheme to solve the resulting nonlinear time-discrete problems. Under weak assumptions on the time-step size, we prove that the scheme converges irrespective of the space discretisation and mesh. Moreover, if the problem is non-degenerate, the convergence becomes faster as the time-step size decreases. Finally, employing the finite element method for the spatial discretisation, we study the behaviour of the scheme, and compare its performance to other commonly used schemes. These tests confirm that the proposed scheme is robust and fast., Comment: 40 pages,7 figures, journal submission

Josephson junction parametric amplifiers have become essential tools for microwave quantum circuit readout with minimal added noise. Even after improving at an impressive rate in the last decade, they remain vulnerable to magnetic field, which limits their use in many applications such as spin qubits, Andreev and molecular magnet devices, dark matter searches, etc. Kinetic inductance materials, such as granular aluminum (grAl), offer an alternative source of non-linearity with innate magnetic field resilience. We present a non-degenerate amplifier made of two coupled grAl resonators resilient to in-plane magnetic field up to 1 T. It offers 20 dB of gain close to the quantum limit of added noise, with a gain-bandwidth product of 28 MHz and -110 dBm input saturation power.

Two-dimensional (2D) semiconducting transition-metal dichalcogenides (TMDCs) are an exciting platform for new excitonic physics and next-generation electronics, creating a strong demand to understand their growth, doping, and heterostructures. Despite significant progress in solid-source (SS-) and metal-organic chemical vapor deposition (MOCVD), further optimization is necessary to grow highly crystalline 2D TMDCs with controlled doping. Here, we report a hybrid MOCVD growth method that combines liquid-phase metal precursor deposition and vapor-phase organo-chalcogen delivery to leverage the advantages of both MOCVD and SS-CVD. Using our hybrid approach, we demonstrate WS$_2$ growth with tunable morphologies - from separated single-crystal domains to continuous monolayer films - on a variety of substrates, including sapphire, SiO$_2$, and Au. These WS$_2$ films exhibit narrow neutral exciton photoluminescence linewidths down to 33 meV and room-temperature mobility up to 34 - 36 cm$^2$V$^-$$^1$s$^-$$^1$). Through simple modifications to the liquid precursor composition, we demonstrate the growth of V-doped WS$_2$, MoxW$_1$$_-$$_x$S$_2$ alloys, and in-plane WS$_2$-MoS$_2$ heterostructures. This work presents an efficient approach for addressing a variety of TMDC synthesis needs on a laboratory scale.

Sequence differences between the strains of bacteria comprising host-associated and environmental microbiota may play a role in community assembly and influence the resilience of microbial communities to disturbances. Tools for characterizing strain-level variation within microbial communities, however, are limited in scope, focusing on just single nucleotide polymorphisms, or relying on reference-based analyses that miss complex functional and structural variants. Here, we demonstrate the power of assembly graph analysis to detect and characterize structural variants in almost 1,000 metagenomes generated as part of the Human Microbiome Project. We identify over nine million variants comprising insertion/deletion events, repeat copy-number changes, and mobile elements such as plasmids. We highlight some of the potential functional roles of these genomic changes. Our analysis revealed striking differences in the rate of variation across body sites, highlighting niche-specific mechanisms of bacterial adaptation. The structural variants we detect also include potentially novel prophage integration events, highlighting the potential use of graph-based analyses for phage discovery.

Metagenomic studies have primarily relied on de novo assembly for reconstructing genes and genomes from microbial mixtures. While reference-guided approaches have been employed in the assembly of single organisms, they have not been used in a metagenomic context. Here we describe the first effective approach for reference-guided metagenomic assembly that can complement and improve upon de novo metagenomic assembly methods for certain organisms. Such approaches will be increasingly useful as more genomes are sequenced and made publicly available., Comment: 18 pages, 6 figures, 3 tables, one supplementary material

Based on the existing experimental data for A-A collisions starting from the AGS energies up to the LHC ones, various systematics related to strange hadrons and anti-hadrons are presented. The ratio between the average transverse momentum and the square root of the total particle multiplicity per unit rapidity and unit transverse overlap area decreases with collision energy for a given centrality or with centrality for a given collision energy, thus supporting the predictions of CGC and percolation based approaches. The dependence on the square root of the total multiplicity per unit rapidity and unit transverse overlap area of the slope and offset extracted from the $\langle p_T\rangle$-particle mass correlation and of the average transverse expansion velocity and kinetic freeze-out temperature parameters obtained from BGBW fits of the pT spectra for strange hadrons is compared to that for pions, kaons and protons. The detailed study of the entropy density dependence of the ratio of strange hadron yields per unit rapidity to the total particle multiplicity per unit rapidity, at different collision energies and centralities, reveals the necessity to study separately strange hadrons and anti-hadrons. The correlation between the ratio of the single- and multi- strange anti-hadron yield per unit rapidity to the total particle multiplicity per unit rapidity and the entropy density is presented as a function of the fireball size. A maximum is evidenced in the similar correlation established for combined and separate species of strange hadrons, at different centralities, in the region where a transition from the baryon-dominated matter to the meson-dominated one takes place. The position of this maximum does not depend on the mass of the corresponding strange hadron. Comparison with pp experimental data reveals another similarity between pp and Pb-Pb collisions at the LHC energies., Comment: 13 pages, 26 figures

Confinement of monolayers into quasi-one-dimensional atomically-thin nanoribbons could lead to novel quantum phenomena beyond those achieved in their bulk and monolayer counterparts. However, current experimental availability of nanoribbon species beyond graphene has been limited to bottom-up synthesis or top-down patterning. In this study, we introduce a versatile and direct lithography-free approach to exfoliate a variety of bulk van der Waals (vdW) crystals into nanoribbons. Akin to the Scotch tape exfoliation in producing monolayers, this technique provides convenient access to a wide range of nanoribbons derived from their corresponding bulk crystals, including MoS2, WS2, MoSe2, WSe2, MoTe2, WTe2, ReS2, and hBN. The nanoribbons are single-crystalline, parallel-aligned, flat, and have high aspect ratio. We demonstrated the electrical, magnetic, and optical properties from the confinement, strain, and edge configurations of these nanoribbons. This versatile preparation technique will pave the way for future experimental investigation and broad applications in optoelectronic, sensing, electronic and quantum devices., Comment: 18 pages, 5 figures

Semiconducting transition metal dichalcogenides (TMDs) are promising for high-specific-power photovoltaics due to desirable band gaps, high absorption coefficients, and ideally dangling-bond-free surfaces. Despite their potential, the majority of TMD solar cells are fabricated in a non-scalable fashion using exfoliated materials due to the absence of high-quality, large-area, multilayer TMDs. Here, we present the scalable, thickness-tunable synthesis of multilayer tungsten diselenide (WSe$_{2}$) films by selenizing pre-patterned tungsten with either solid source selenium or H$_{2}$Se precursors, which leads to smooth, wafer-scale WSe$_{2}$ films with a layered van der Waals structure. The films have charge carrier lifetimes up to 144 ns, over 14x higher than large-area TMD films previously demonstrated. Such high carrier lifetimes correspond to power conversion efficiency of ~22% and specific power of ~64 W g$^{-1}$ in a packaged solar cell, or ~3 W g$^{-1}$ in a fully-packaged solar module. This paves the way for the mass-production of high-efficiency multilayer WSe$_{2}$ solar cells at low cost.

The detection and classification of diseases in Robusta coffee leaves are essential to ensure that plants are healthy and the crop yield is kept high. However, this job requires extensive botanical knowledge and much wasted time. Therefore, this task and others similar to it have been extensively researched subjects in image classification. Regarding leaf disease classification, most approaches have used the more popular PlantVillage dataset while completely disregarding other datasets, like the Robusta Coffee Leaf (RoCoLe) dataset. As the RoCoLe dataset is imbalanced and does not have many samples, fine-tuning of pre-trained models and multiple augmentation techniques need to be used. The current paper uses the RoCoLe dataset and approaches based on deep learning for classifying coffee leaf diseases from images, incorporating the pix2pix model for segmentation and cycle-generative adversarial network (CycleGAN) for augmentation. Our study demonstrates the effectiveness of Transformer-based models, online augmentations, and CycleGAN augmentation in improving leaf disease classification. While synthetic data has limitations, it complements real data, enhancing model performance. These findings contribute to developing robust techniques for plant disease detection and classification., Comment: Accepted by ICAART 2024

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demand and the timing of their usage. In our view grid management should leverage on EVs scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging Deep Q-Learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman Equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising showing that the proposed solution can effectively schedule the EVs charging and discharging actions to align with the target profile with a Person coefficient of 0.99, handling effective EVs scheduling situations that involve dynamicity given by the e-mobility features, relying only on data with no knowledge of EVs and microgrid dynamics., Comment: Submitted to journal

This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models' robustness against adversarial attacks is enhanced through adversarial training, ensuring accurate classification even in the presence of threats. Leveraging explainability techniques, we gain insights into the model's decision-making process, improving trust and transparency. Additionally, we explore model compression techniques to optimize computational efficiency while maintaining classification performance. Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests. We also demonstrate that a student model can be 15-25 times more computationally efficient for a slight performance reduction, distilling the knowledge of more complex models., Comment: 10 pages, 8 figures, Accepted by ICAART 2024

Time-of-Flight (ToF) cameras are becoming popular in a wide span of areas ranging from consumer-grade electronic devices to safety-critical industrial robots. This is mainly due to their high frame rate, relative good precision and the lowered costs. Although ToF cameras are in continuous development, especially pulse-based variants, they still face different problems, including spurious noise over the points or multipath inference (MPI). The latter can cause deformed surfaces to manifest themselves on curved surfaces instead of planar ones, making standard spatial data preprocessing, such as plane extraction, difficult. In this paper, we focus on the MPI reduction problem using Feature Pyramid Networks (FPN) which allow the mitigation of this type of artifact for pulse-based ToF cameras. With our end-to-end network, we managed to attenuate the MPI effect on planar surfaces using a learning-based method on real ToF data. Both the custom dataset used for our model training as well as the code is available on the author's Github homepage.

We consider a problem in which the trajectory of a mobile 3D sensor must be optimized so that certain objects are both found in the overall scene and covered by the point cloud, as fast as possible. This problem is called target search and coverage, and the paper provides an end-to-end deep reinforcement learning (RL) solution to solve it. The deep neural network combines four components: deep hierarchical feature learning occurs in the first stage, followed by multi-head transformers in the second, max-pooling and merging with bypassed information to preserve spatial relationships in the third, and a distributional dueling network in the last stage. To evaluate the method, a simulator is developed where cylinders must be found by a Kinect sensor. A network architecture study shows that deep hierarchical feature learning works for RL and that by using farthest point sampling (FPS) we can reduce the amount of points and achieve not only a reduction of the network size but also better results. We also show that multi-head attention for point-clouds helps to learn the agent faster but converges to the same outcome. Finally, we compare RL using the best network with a greedy baseline that maximizes immediate rewards and requires for that purpose an oracle that predicts the next observation. We decided RL achieves significantly better and more robust results than the greedy strategy.

Our previous studies of dissociative recombination, and vibrational excitation/de-excitation of the BeH$^+$ ion, based on the multichannel quantum defect theory, are extended to collision energies above the dissociation threshold, taking into account the vibrational continua of the BeH$^+$ ion and, consequently, its dissociative excitation. We have also significantly increased the number of dissociative states of $^2{\Pi}$, $^2{\Sigma}^+$ and $^2\Delta$ symmetry included in our cross section calculations, generating the most excited-ones by using appropriate scaling laws. Our results are suitable for modeling the kinetics of BeH+ in edge fusion plasmas for collision energies up to 12 eV., Comment: 9 pages, 9 figures

The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage leading to a need for more coordinated and grid aware EVs charging and discharging strategies. However, coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies as the complexity increases with more control variables and EVs, necessitating large optimization and decision search spaces. In this paper, we propose an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid, by utilizing EVs to store surplus energy and discharge it during periods of energy deficit. The optimization problem is addressed using Harris Hawks Optimization (HHO) considering criteria related to energy grid balancing, time usage preference, and the location of EV drivers. The EVs schedules, associated with the position of individuals from the population, are adjusted through exploration and exploitation operations, and their technical and operational feasibility is ensured, while the rabbit individual is updated with a non-dominated EV schedule selected per iteration using a roulette wheel algorithm. The solution is evaluated within the framework of an e-mobility service in Terni city. The results indicate that coordinated charging and discharging of EVs not only meet balancing service requirements but also align with user preferences with minimal deviations., Comment: Submitted to Elsevier journal

In a superconducting weak link, the supercurrent is carried by Andreev bound states (ABSs) formed by the phase-coherent reflection of electrons and their time-reversed partners. A single, highly transmissive ABS can serve as an ideal, compact two-level system, due to a potentially large energy difference to the next ABS. While the coherent manipulation of such Andreev levels qubits (ALQs) has been demonstrated, a long-range coupling between two ALQs, necessary for advanced qubit architectures, has not been achieved, yet. Here, we demonstrate a coherent remote coupling between two ALQs, mediated by a microwave photon in a novel superconducting microwave cavity coupler. The latter hosts two modes with different coupling rates to an external port. This allows us to perform fast readout of each qubit using the strongly coupled mode, while the weakly coupled mode is utilized to mediate the coupling between the qubits. When both qubits are tuned into resonance with the latter mode, we find excitation spectra with avoided-crossings, in very good agreement with the Tavis-Cummings model. Based on this model, we identify highly entangled two-qubit states for which the entanglement is mediated over a distance of six millimeters. This work establishes ALQs as compact and scalable solid-state qubits., Comment: 13 pages, 7 figures

Lip reading or visual speech recognition has gained significant attention in recent years, particularly because of hardware development and innovations in computer vision. While considerable progress has been obtained, most models have only been tested on a few large-scale datasets. This work addresses this shortcoming by analyzing several architectures and optimizations on the underrepresented, short-scale Romanian language dataset called Wild LRRo. Most notably, we compare different backend modules, demonstrating the effectiveness of adding ample regularization methods. We obtain state-of-the-art results using our proposed method, namely cross-lingual domain adaptation and unlabeled videos from English and German datasets to help the model learn language-invariant features. Lastly, we assess the performance of adding a layer inspired by the neural inhibition mechanism., Comment: 7 pages, 4 figures, Accepted by WI-IAT 2023

We aim at development white-box machine learning algorithms. We focus here on algorithms for learning axioms in description logic. We extend the Class Expression Learning for Ontology Engineering (CELOE) algorithm contained in the DL-Learner tool. The approach uses multiple search trees and a shared pool of refinements in order to split the search space in smaller subspaces. We introduce the conjunction operation of best class expressions from each tree, keeping the results which give the most information. The aim is to foster exploration from a diverse set of starting classes and to streamline the process of finding class expressions in ontologies. %, particularly in large search spaces. The current implementation and settings indicated that the Forest Mixing approach did not outperform the traditional CELOE. Despite these results, the conceptual proposal brought forward by this approach may stimulate future improvements in class expression finding in ontologies. % and influence. % the way we traverse search spaces in general.

Privacy is important when dealing with sensitive personal information in machine learning models, which require large data sets for training. In the energy field, access to household prosumer energy data is crucial for energy predictions to support energy grid management and large-scale adoption of renewables however citizens are often hesitant to grant access to cloud-based machine learning models. Federated learning has been proposed as a solution to privacy challenges however report issues in generating the global prediction model due to data heterogeneity, variations in generation patterns, and the high number of parameters leading to even lower prediction accuracy. This paper addresses these challenges by introducing FedWOA a novel federated learning model that employs the Whale Optimization Algorithm to aggregate global prediction models from the weights of local LTSM neural network models trained on prosumer energy data. The proposed solution identifies the optimal vector of weights in the search spaces of the local models to construct the global shared model and then is subsequently transmitted to the local nodes to improve the prediction quality at the prosumer site while for handling non-IID data K-Means was used for clustering prosumers with similar scale of energy data. The evaluation results on prosumers energy data have shown that FedWOA can effectively enhance the accuracy of energy prediction models accuracy by 25% for MSE and 16% for MAE compared to FedAVG while demonstrating good convergence and reduced loss.

Strain engineering can modulate the material properties of two-dimensional (2D) semiconductors for electronic and optoelectronic applications. Recent theory and experiments have found that uniaxial tensile strain can improve the electron mobility of monolayer MoS$_2$, a 2D semiconductor, but the effects of biaxial strain on charge transport are not well-understood in 2D semiconductors. Here, we use biaxial tensile strain on flexible substrates to probe the electron mobility in monolayer WS$_2$ and MoS$_2$ transistors. This approach experimentally achieves ~2x higher on-state current and mobility with ~0.3% applied biaxial strain in WS$_2$, the highest mobility improvement at the lowest strain reported to date. We also examine the mechanisms behind this improvement through density functional theory simulations, concluding that the enhancement is primarily due to reduced intervalley electron-phonon scattering. These results underscore the role of strain engineering 2D semiconductors for flexible electronics, sensors, integrated circuits, and other optoelectronic applications., Comment: Corrected author list

Lately, the energy communities have gained a lot of attention as they have the potential to significantly contribute to the resilience and flexibility of the energy system, facilitating widespread integration of intermittent renewable energy sources. Within these communities the prosumers can engage in peer-to-peer trading, fostering local collaborations and increasing awareness about energy usage and flexible consumption. However, even under these favorable conditions, prosumer engagement levels remain low, requiring trading mechanisms that are aligned with their social values and expectations. In this paper, we introduce an innovative hedonic game coordination and cooperation model for P2P energy trading among prosumers which considers the social relationships within an energy community to create energy coalitions and facilitate energy transactions among them. We defined a heuristic that optimizes the prosumers coalitions, considering their social and energy price preferences and balancing the energy demand and supply within the community. We integrated the proposed hedonic game model into a state-of-the-art blockchain-based P2P energy flexibility market and evaluated its performance within an energy community of prosumers. The evaluation results on a blockchain-based P2P energy flexibility market show the effectiveness in considering social factors when creating coalitions, increasing the total amount of energy transacted in a market session by 5% compared with other game theory-based solutions. Finally, it shows the importance of the social dimensions of P2P energy transactions, the positive social dynamics in the energy community increasing the amount of energy transacted by more than 10% while contributing to a more balanced energy demand and supply within the community., Comment: to be submitted to journal

In this paper we define a numerical shape invariant of a continuous map called shape dimension of a map, which generalizes the shape dimension of a topological space. Some basic properties and applications of this invariant are given. The question of raising the shape dimension by shape finite-dimensional maps is solved. An example of a shape finite-dimensional surjective map between shape infinite-dimensional spaces is given., Comment: 8 pages

The growing sophistication of Business Email Compromise (BEC) and spear phishing attacks poses significant challenges to organizations worldwide. The techniques featured in traditional spam and phishing detection are insufficient due to the tailored nature of modern BEC attacks as they often blend in with the regular benign traffic. Recent advances in machine learning, particularly in Natural Language Understanding (NLU), offer a promising avenue for combating such attacks but in a practical system, due to limitations such as data availability, operational costs, verdict explainability requirements or a need to robustly evolve the system, it is essential to combine multiple approaches together. We present CAPE, a comprehensive and efficient system for BEC detection that has been proven in a production environment for a period of over two years. Rather than being a single model, CAPE is a system that combines independent ML models and algorithms detecting BEC-related behaviors across various email modalities such as text, images, metadata and the email's communication context. This decomposition makes CAPE's verdicts naturally explainable. In the paper, we describe the design principles and constraints behind its architecture, as well as the challenges of model design, evaluation and adapting the system continuously through a Bayesian approach that combines limited data with domain knowledge. Furthermore, we elaborate on several specific behavioral detectors, such as those based on Transformer neural architectures.

Skin cancer is a treatable disease if discovered early. We provide a production-specific solution to the skin cancer classification problem that matches human performance in melanoma identification by training a vision transformer on melanoma medical images annotated by experts. Since inference cost, both time and memory wise is important in practice, we employ knowledge distillation to obtain a model that retains 98.33% of the teacher's balanced multi-class accuracy, at a fraction of the cost. Memory-wise, our model is 49.60% smaller than the teacher. Time-wise, our solution is 69.25% faster on GPU and 97.96% faster on CPU. By adding classification heads at each level of the transformer and employing a cascading distillation process, we improve the balanced multi-class accuracy of the base model by 2.1%, while creating a range of models of various sizes but comparable performance. We provide the code at https://github.com/Longman-Stan/SkinDistilVit., Comment: Accepted at ICANN 2023