Miracle-dz/UniFAR-FTUs huggingface dataset:
Stochastic and Quantum Thermodynamics of Driven RLC Networks

We develop a general stochastic thermodynamics of RLC electrical networks built on top of a graph-theoretical representation of the dynamics commonly used by engineers. The network is: open, as it contains resistors and current and voltage sources, nonisothermal as resistors may be at different temperatures, and driven, as circuit elements may be subjected to external parametric driving. The proper description of the heat dissipated in each resistor requires care within the white noise idealization as it depends on the network topology. Our theory provides the basis to design circuits-based thermal machines, as we illustrate by designing a refrigerator using a simple driven circuit. We also derive exact results for the low temperature regime in which the quantum nature of the electrical noise must be taken into account. We do so using a semiclassical approach which can be shown to coincide with a fully quantum treatment of linear circuits for which canonical quantization is possible. We use it to generalize the Landauer-Buttiker formula for energy currents to arbitrary time-dependent driving protocols.
Deep Modular Co-Attention Networks for Visual Question Answering

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the question-guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63% overall accuracy on the test-dev set.
Temporal Discounting in Software Engineering: A Replication Study

Background: Many decisions made in Software Engineering practices are intertemporal choices: trade-offs in time between closer options with potential short-term benefit and future options with potential long-term benefit. However, how software professionals make intertemporal decisions is not well understood. Aim: This paper investigates how shifting time frames influence preferences in software projects in relation to purposefully selected background factors. Method: We investigate temporal discounting by replicating a questionnaire-based observational study. The replication uses a changed-population and -experimenter design to increase the internal and external validity of the original results. Results: The results of this study confirm the occurrence of temporal discounting in samples of both professional and student participants from different countries and demonstrate strong variance in discounting between study participants. We found that professional experience influenced discounting. Participants with broader professional experience exhibited less discounting than those with narrower experience. Conclusions: The results provide strong empirical support for the relevance and importance of temporal discounting in SE and the urgency of targeted interdisciplinary research to explore the underlying mechanisms and their theoretical and practical implications. The results suggest that technical debt management could be improved by increasing the breadth of experience available for critical decisions with long-term impact. In addition, the present study provides a methodological basis for replicating temporal discounting studies in software engineering.
Heuristic Approach Towards Countermeasure Selection using Attack Graphs

Selecting the optimal set of countermeasures is a challenging task that involves various considerations and tradeoffs such as prioritizing the risks to mitigate and costs. The vast majority of studies for selecting a countermeasure deployment are based on a limited risk assessment procedure that utilizes the common vulnerability scoring system (CVSS). Such a risk assessment procedure does not necessarily consider the prerequisites and exploitability of a specific asset, cannot distinguish insider from outsider threat actor, and does not express the consequences of exploiting a vulnerability as well as the attacker's lateral movements. Other studies applied a more extensive risk assessment procedure that relies on manual work and repeated assessment. These solutions however, do not consider the network topology and do not specify the optimal position for deploying the countermeasures, and therefore are less practical. In this paper we suggest a heuristic search approach for selecting the optimal countermeasure deployment under a given budget limitation. The proposed method expresses the risk of the system using an extended attack graph modeling, which considers the prerequisites and consequences of exploiting a vulnerability, examines the attacker's potential lateral movements, and express the physical network topology as well as vulnerabilities in network protocols. In addition, unlike previous studies which utilizes attack graph for countermeasure planning, the proposed methods does not require re-generating the attack graph at each stage of the procedure, which is computationally heavy, and therefore it provides a more accurate and practical countermeasure deployment planning process.
A Round-Collapse Theorem for Computationally-Sound Protocols; or, TFNP is Hard (on Average) in Pessiland

Consider the following two fundamental open problems in complexity theory: 1) Does a hard-on-average language in NP imply the existence of one-way functions? 2) Does a hard-on-average language in NP imply a hard problem in TFNP (i.e., the class of total NP search problem)? We show that the answer to (at least) one of these questions is yes. In other words, in Impagliazzo's Pessiland (where NP is hard-on-average, but one-way functions do not exist), TFNP is unconditionally hard (on average). This result follows from a more general theory of interactive average-case complexity, and in particular, a novel round-collapse theorem for computationally-sound protocols, analogous to Babai-Moran's celebrated round-collapse theorem for information-theoretically sound protocols. As another consequence of this treatment, we show that the existence of O(1)-round public-coin non-trivial arguments (i.e., argument systems that are not proofs) imply the existence of a hard-on-average problem in NP/poly.
A limited-range Calder\'on-Zygmund theorem

We work with singular integral operators whose kernels satisfy a condition weaker than the typical Hörmander smoothness estimate. We give two proofs of a weaktype (q, q) inequality for these operators and, via interpolation, obtain L(R) estimates for the operators for a certain range of p. One proof of the weak-type estimate uses the Calderón-Zygmund decomposition while the other proof uses ideas first given by Nazarov, Treil, and Volberg.
Improving the use of the randomized singular value decomposition for the inversion of gravity and magnetic data

The focusing inversion of gravity and magnetic potential-field data using the randomized singular value decomposition (RSVD) method is considered. This approach facilitates tackling the computational challenge that arises in the solution of the inversion problem that uses the standard and accurate approximation of the integral equation kernel. We have developed a comprehensive comparison of the developed methodology for the inversion of magnetic and gravity data. The results verify that there is an important difference between the application of the methodology for gravity and magnetic inversion problems. Specifically, RSVD is dependent on the generation of a rank [Formula: see text] approximation to the underlying model matrix, and the results demonstrate that [Formula: see text] needs to be larger, for equivalent problem sizes, for the magnetic problem compared to the gravity problem. Without a relatively large [Formula: see text], the dominant singular values of the magnetic model matrix are not well approximated. We determine that this is due to the spectral properties of the matrix. The comparison also shows us how the use of the power iteration embedded within the randomized algorithm improves the quality of the resulting dominant subspace approximation, especially in magnetic inversion, yielding acceptable approximations for smaller choices of [Formula: see text]. Further, we evaluate how the differences in spectral properties of the magnetic and gravity input matrices also affect the values that are automatically estimated for the regularization parameter. The algorithm is applied and verified for the inversion of magnetic data obtained over a portion of the Wuskwatim Lake region in Manitoba, Canada.
Orbital Angular Momentum States Enabling Fiber-based High-dimensional Quantum Communication

Quantum networks are the ultimate target in quantum communication, where many connected users can share information carried by quantum systems. The keystones of such structures are the reliable generation, transmission and manipulation of quantum states. Two-dimensional quantum states, qubits, are steadily adopted as information units. However, high-dimensional quantum states, qudits, constitute a richer resource for future quantum networks, exceeding the limitations imposed by the ubiquitous qubits. The generation and manipulation of such $D$-level systems have been improved over the last ten years, but their reliable transmission between remote locations remains the main challenge. Here, we show how a recent air-core fiber supporting orbital angular momentum (OAM) modes can be exploited to faithfully transmit $D$-dimensional states. Four OAM quantum states and their superpositions are created, propagated in a 1.2 km long fiber and detected with high fidelities. In addition, three quantum key distribution (QKD) protocols are implemented as concrete applications to assert the practicality of our results. This experiment enhances the distribution of high-dimensional quantum states, attesting the orbital angular momentum as vessel for the future quantum network.
Multi-Span Acoustic Modelling using Raw Waveform Signals

Traditional automatic speech recognition (ASR) systems often use an acoustic model (AM) built on handcrafted acoustic features, such as log Mel-filter bank (FBANK) values. Recent studies found that AMs with convolutional neural networks (CNNs) can directly use the raw waveform signal as input. Given sufficient training data, these AMs can yield a competitive word error rate (WER) to those built on FBANK features. This paper proposes a novel multi-span structure for acoustic modelling based on the raw waveform with multiple streams of CNN input layers, each processing a different span of the raw waveform signal. Evaluation on both the single channel CHiME4 and AMI data sets show that multi-span AMs give a lower WER than FBANK AMs by an average of about 5% (relative). Analysis of the trained multi-span model reveals that the CNNs can learn filters that are rather different to the log Mel filters. Furthermore, the paper shows that a widely used single span raw waveform AM can be improved by using a smaller CNN kernel size and increased stride to yield improved WERs.
A unifying computations of Whittle's Index for Markovian bandits

The multi-armed restless bandit framework allows to model a wide variety of decision-making problems in areas as diverse as industrial engineering, computer communication, operations research, financial engineering, communication networks etc. In a seminal work, Whittle developed a methodology to derive well-performing (Whittle's) index policies that are obtained by solving a relaxed version of the original problem. However, the computation of Whittle's index itself is a difficult problem and hence researchers focused on calculating Whittle's index numerically or with a problem dependent approach. In our main contribution we derive an analytical expression for Whittle's index for any Markovian bandit with both finite and infinite transition rates. We derive sufficient conditions for the optimal solution of the relaxed problem to be of threshold type, and obtain conditions for the bandit to be indexable, a property assuring the existence of Whittle's index. Our solution approach provides a unifying expression for Whittle's index, which we highlight by retrieving known indices from literature as particular cases. The applicability of finite rates is illustrated with the machine repairmen problem, and that of infinite rates by an example of communication networks where transmission rates react instantaneously to packet losses.
Asymptotic behavior for a Schrödinger equation with nonlinear subcritical dissipation

We study the time-asymptotic behavior of solutions of the Schrodinger equation with nonlinear dissipation \begin{equation*} \partial _t u = i \Delta u + \lambda |u|^\alpha u \end{equation*} in ${\mathbb R}^N $, $N\geq1$, where $\lambda\in {\mathbb C}$, $\Re \lambda <0$ and $0<\alpha<\frac2N$. We give a precise description of the behavior of the solutions (including decay rates in $L^2$ and $L^\infty $, and asymptotic profile), for a class of arbitrarily large initial data, under the additional assumption that $\alpha $ is sufficiently close to $\frac2N$.
(Theta, triangle)‐free and (even hole, 
K
4 )‐free graphs—Part 1: Layered wheels

We present a construction called layered wheel. Layered wheels are graphs of arbitrarily large treewidth and girth. They might be an outcome for a possible theorem characterizing graphs with large treewidth in terms of their induced subgraphs (while such a characterization is well‐understood in terms of minors). They also provide examples of graphs of large treewidth and large rankwidth in well‐studied classes, such as (theta, triangle)‐free graphs and even‐hole‐free graphs with no K 4 (where a hole is a chordless cycle of length at least four, a theta is a graph made of three internally vertex disjoint paths of length at least two linking two vertices, and K 4 is the complete graph on four vertices).
Investigating “Dark” Energy in the Solar Corona Using Forward Modeling of MHD Waves

It is now well established that Alfvénic waves are ubiquitous in the solar corona. However, the Alfvénic wave energy estimated from Doppler velocity measurements in the corona was found to be four orders of magnitude less than that estimated from nonthermal line widths. McIntosh & De Pontieu suggested that this discrepancy in energy might be due to the line-of-sight (LOS) superposition of several oscillating structures, which can lead to an underestimation of the Alfvénic wave amplitudes and energies. McIntosh & De Pontieu termed this coronal “dark” or “hidden” energy. However, their simulations required the use of an additional, unknown source of Alfvénic wave energy to obtain agreement with measurements of the coronal nonthermal line widths. In this study, we investigate the requirement of this unknown source of additional “dark” energy in the solar corona using gravitationally stratified 3D magnetohydrodynamic (MHD) simulations of propagating waves. We excite the transverse MHD waves and generate synthetic observations of the Fe xiii emission line. We establish that LOS superposition greatly reduces the Doppler velocity amplitudes and increases the nonthermal line widths. Importantly, our model generates the observed wedge-shape correlation between Doppler velocities and nonthermal line widths. We find that the observed wave energy is only 0.2%–1% of the true wave energy, which explains the 2–3 order-of-magnitude energy discrepancy. We conclusively establish that true wave energies are hidden in nonthermal line widths. Hence, our results rule out the requirement for an additional “dark” energy in the solar corona.
Preliminary test estimation in ULAN models

Preliminary test estimation, which is a natural procedure when it is suspected a priori that the parameter to be estimated might take value in a submodel of the model at hand, is a classical topic in estimation theory. In the present paper, we establish general results on the asymptotic behavior of preliminary test estimators. More precisely, we show that, in uniformly locally asymptotically normal (ULAN) models, a general asymptotic theory can be derived for preliminary test estimators based on estimators admitting generic Bahadur-type representations. This allows for a detailed comparison between classical estimators and preliminary test estimators in ULAN models. Our results, that, in standard linear regression models, are shown to reduce to some classical results, are also illustrated in more modern and involved setups, such as the multisample one where $m$ covariance matrices ${\pmb\Sigma}_1, \ldots, {\pmb\Sigma}_m$ are to be estimated when it is suspected that these matrices might be equal, might be proportional, or might share a common "scale". Simulation results confirm our theoretical findings.
Automatic Hierarchical Classification of Kelps Using Deep Residual Features

Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas. Exploiting this data is presently limited by the time it takes for experts to identify organisms found in these images. With this limitation in mind, a large effort has been made globally to introduce automation and machine learning algorithms to accelerate both classification and assessment of marine benthic biota. One major issue lies with organisms that move with swell and currents, such as kelps. This paper presents an automatic hierarchical classification method local binary classification as opposed to the conventional flat classification to classify kelps in images collected by autonomous underwater vehicles. The proposed kelp classification approach exploits learned feature representations extracted from deep residual networks. We show that these generic features outperform the traditional off-the-shelf CNN features and the conventional hand-crafted features. Experiments also demonstrate that the hierarchical classification method outperforms the traditional parallel multi-class classifications by a significant margin (90.0% vs. 57.6% and 77.2% vs. 59.0%) on Benthoz15 and Rottnest datasets respectively. Furthermore, we compare different hierarchical classification approaches and experimentally show that the sibling hierarchical training approach outperforms the inclusive hierarchical approach by a significant margin. We also report an application of our proposed method to study the change in kelp cover over time for annually repeated AUV surveys.
The pentagram map, Poncelet polygons, and commuting difference operators

The pentagram map takes a planar polygon $P$ to a polygon $P'$ whose vertices are the intersection points of consecutive shortest diagonals of $P$. This map is known to interact nicely with Poncelet polygons, that is, polygons which are simultaneously inscribed in a conic and circumscribed about a conic. A theorem of Schwartz states that if $P$ is a Poncelet polygon, then the image of $P$ under the pentagram map is projectively equivalent to $P$. In the present paper, we show that in the convex case this property characterizes Poncelet polygons: if a convex polygon is projectively equivalent to its pentagram image, then it is Poncelet. The proof is based on the theory of commuting difference operators, as well as on properties of real elliptic curves and theta functions.
The Hamiltonian approach to the problem of derivation of production functions in economic growth theory

We introduce a general Hamiltonian framework that appears to be a natural setting for the derivation of various production functions in economic growth theory, starting with the celebrated Cobb-Douglas function. Employing our method, we investigate some existing models and propose a new one as special cases of the general $n$-dimensional Lotka-Volterra system of eco-dynamics.
The ESO supernovae type Ia progenitor survey (SPY)

Close double degenerate binaries are one of the favoured progenitor channels for type Ia supernovae, but it is unclear how many suitable systems there are in the Galaxy. We report results of a large radial velocity survey for double degenerate (DD) binaries using the UVES spectrograph at the ESO VLT (ESO SN Ia Progenitor surveY - SPY). Exposures taken at different epochs are checked for radial velocity shifts indicating close binary systems. We observed 689 targets classified as DA (displaying hydrogen-rich atmospheres), of which 46 turned out to possess a cool companion. We measured radial velocities (RV) of the remaining 643 DA white dwarfs. We managed to secure observations at two or more epochs for 625 targets, supplemented by eleven objects meeting our selection criteria from literature. The data reduction and analysis methods applied to the survey data are described in detail. The sample contains 39 double degenerate binaries, only four of which were previously known. 20 are double-lined systems, in which features from both components are visible, the other 19 are single-lined binaries. We provide absolute RVs transformed to the heliocentric system suitable for kinematic studies. Our sample is large enough to sub-divide by mass: 16 out of 44 low mass targets ( 0.45 Msun are double. Although the detected fraction amongst the low mass objects (36.4 +/- 7.3%) is significantly higher than for the higher-mass, carbon/oxygen-core dominated part of the sample (3.9 +/- 0.8%), it is lower than the detection efficiency based upon companion star masses >= 0.05 Msun. This suggests either companion stars of mass < 0.05 Msun, or that some of the low mass white dwarfs are single.
Heat-Bath Algorithmic Cooling with optimal thermalization strategies

Heat-Bath Algorithmic Cooling is a set of techniques for producing highly pure quantum systems by utilizing a surrounding heat-bath and unitary interactions. These techniques originally used the thermal environment only to fully thermalize ancillas at the environment temperature. Here we extend HBAC protocols by optimizing over the thermalization strategy. We find, for any d-dimensional system in an arbitrary initial state, provably optimal cooling protocols with surprisingly simple structure and exponential convergence to the ground state. Compared to the standard ones, these schemes can use fewer or no ancillas and exploit memory effects to enhance cooling. We verify that the optimal protocols are robusts to various deviations from the ideal scenario. For a single target qubit, the optimal protocol can be well approximated with a Jaynes-Cummings interaction between the system and a single thermal bosonic mode for a wide range of environmental temperatures. This admits an experimental implementation close to the setup of a micromaser, with a performance competitive with leading proposals in the literature. The proposed protocol provides an experimental setup that illustrates how non-Markovianity can be harnessed to improve cooling. On the technical side we 1. introduce a new class of states called maximallyactivestates and discuss their thermodynamic significance in terms of optimal unitary control, 2. introduce a new set of thermodynamic processes, called β-permutations, whose access is sufficient to simulate a generic thermalization process, 3. show how to use abstract toolbox developed within the resource theory approach to thermodynamics to perform challenging optimizations, while combining it with open quantum system dynamics tools to approximate optimal solutions within physically realistic setups.