Miracle-dz/UniFAR-FTUs huggingface dataset:
Fidelity of measurement-based quantum computation in a bosonic environment

We investigate the fidelity of measurement-based quantum computation (MBQC) when it is coupled with a boson environment, by measuring the cluster-state fidelity and gate fidelity. Two schemes of cluster-state preparation are studied. In the controlled-Z (CZ) creation scheme, cluster states are prepared by entangling all qubits in vertical bar+> state with CZ gates at all neighboring sites. The fidelity shows an oscillation pattern over time. The influence of the environmental temperature is evaluated, and suggestions are given to enhance the performance of MBQCs realized in this way. In the Hamiltonian creation scheme, cluster states are made by cooling a system with cluster Hamiltonians, of which the ground states are cluster states. A fidelity sudden-drop phenomenon is discovered. When the coupling is below a threshold, MBQC systems are highly robust against the noise. Our main environmental model is one with a single collective bosonic mode.
A P-local Delooping Machine

We show that for spaces $A$ that satisfy a certain smallness condition, there is a Lawvere theory $T_A$ so that a space $X$ has the structure of a $T_A$-algebra if and only if $X$ is weakly equivalent to a mapping space out of $A$. In particular, spheres localized at a set of primes satisfy this condition.
Higher order SUSY-QM for P\"oschl-Teller potentials: coherent states and operator properties

This work prolongs recent investigations by Bergeron et al [see 2012 J. Phys. A: Math. Theo. 45 244028] on new SUSYQM coherent states for P\"oschl-Teller potentials. It mainly addresses explicit computations of eigenfunctions and spectrum associated to the higher order hierarchic supersymmetric Hamiltonian. Analysis of relevant properties and normal and anti-normal forms is performed and discussed. Coherent states of the hierarchic first order differential operator $A_{m,\nu,\beta}$ of the P\"oschl-Teller Hamiltonian $H_{\nu,\beta}^{(m)}$ and their characteristics are studied.
Tomography, control, and characterization of entanglement in a three-level atomic system

We study the quantum correlations of the radiation emitted by three-level atoms (cascade type) interacting with two driving fields. In the linear regime, and in the Weisskopf-Wigner approximation, we show that the atomic and the two-photon density matrix are equivalent to each other. This facilitates the tomography of the two-mode state to be realized by measurements on either the atomic system or the emitted fields. While, in general, one needs 4{sup N} measurements for the tomography of an N-photon state, we show that one needs (N+1){sup 2}-1 observables for the tomography of photons emitted by an atomic system. Thus there is an exponential reduction in the number of observables for the reconstruction of the class of N-photon states emitted by atoms. We show that the driving field strengths and detunings provide the control parameters for the preparation of a specific target state. Finally, we study the characterization of entanglement of the two-photon state. We observe that a characterization of entanglement in terms of a single parameter is not possible when the system is in a mixed state; therefore, we provide a description in terms of the newly introduced probability distribution for entanglement, in various regimes of interest.
Uncertainty-aware Efficient Subgraph Isomorphism using Graph Topology

Subgraph isomorphism or subgraph matching is generally considered as an NP-complete problem, made more complex in practical applications where the edge weights take real values and are subject to measurement noise and possible anomalies. To the best of our knowledge, almost all subgraph matching methods utilize node labels to perform node-node matching. In the absence of such labels (in applications such as image matching and map matching among others), these subgraph matching methods do not work. We propose a method for identifying the node correspondence between a subgraph and a full graph in the inexact case without node labels in two steps -(a) extract the minimal unique topology preserving subset from the subgraph and ﬁnd its feasible matching in the full graph, and (b) implement a consensus-based algorithm to expand the matched node set by pairing unique paths based on boundary commutativity. Going beyond the existing subgraph matching approaches, the proposed method is shown to have realistically sub-linear computational eﬃciency, robustness to random measurement noise, and good statistical properties. Our method is also readily applicable to the exact matching case without loss of generality. To demonstrate the eﬀectiveness of the proposed method, a simulation and a case study is performed on the Erdos-Renyi random graphs and the image-based aﬃne covariant features dataset respectively.
Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems

[Background] Selecting an appropriate task is challenging for contributors to Open Source Software (OSS), mainly for those who are contributing for the first time. Therefore, researchers and OSS projects have proposed various strategies to aid newcomers, including labeling tasks. [Aims] In this research, we investigate the automatic labeling of open issues strategy to help the contributors to pick a task to contribute. We label the issues with APIdomains- categories of APIs parsed from the source code used to solve the issues. We plan to add social network analysis metrics gathered from the issues conversations as new predictors. By identifying the skills, we claim the contributor candidates should pick a task more suitable to their skill. [Method] We are employing mixed methods. We qualitatively analyzed interview transcripts and the survey's open-ended questions to comprehend the strategies communities use to assist in onboarding contributors and contributors used to pick up an issue. We applied quantitative studies to analyze the relevance of the API-domain labels in a user experiment and compare the strategies' relative importance for diverse contributor roles. We also mined project and issue data from OSS repositories to build the ground truth and predictors able to infer the API-domain labels with comparable precision, recall, and F-measure with the state-of-art. We also plan to use a skill ontology to assist the matching process between contributors and tasks. By quantitatively analyzing the confidence level of the matching instances in ontologies describing contributors' skills and tasks, we might recommend issues for contribution. In addition, we will measure the effectiveness of the API-domain labels by evaluating the issues solving time and the rate among the labeled and unlabelled ones. [Results] So far, the results showed that organizing the issues? which includes assigning labels is seen as an essential strategy for diverse roles in OSS communities. The API-domain labels are relevant, mainly for experienced practitioners. The predicted labels have an average precision of 75.5%. [Conclusions] Labeling the issues with the API-domain labels indicates the skills involved in an issue. The labels represent possible libraries (aggregated into domains) used in the source code related to an issue. By investigating this research topic, we expect to assist the new contributors in finding a task, helping OSS communities to attract and retain more contributors.
Motif-topology improved Spiking Neural Network for the Cocktail Party Effect and McGurk Effect

Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficient learning paradigms. Network architectures are also considered embodying the function of the network. Here, we propose a Motif-topology improved SNN (M-SNN) for the efficient multi-sensory integration and cognitive phenomenon simulations. The cognitive phenomenon simulation we simulated includes the cocktail party effect and McGurk effect, which are discussed by many researchers. Our M-SNN constituted by the meta operator called network motifs. The source of 3-node network motifs topology from artificial one pre-learned from the spatial or temporal dataset. In the single-sensory classification task, the results showed the accuracy of M-SNN using network motif topologies was higher than the pure feedforward network topology without using them. In the multi-sensory integration task, the performance of M-SNN using artificial network motif was better than the state-of-the-art SNN using BRP (biologically-plausible reward propagation). Furthermore, the M-SNN could better simulate the cocktail party effect and McGurk effect with lower computational cost. We think the artificial network motifs could be considered as some prior knowledge that would contribute to the multi-sensory integration of SNNs and provide more benefits for simulating the cognitive phenomenon.
Time-varying Bayesian Network Meta-Analysis

The presence of methicillin-resistant \textit{Staphylococus Aureus} (MRSA) in complicated skin and soft structure infections (cSSSI) is associated with greater health risks and economic costs to patients. There is concern that MRSA is becoming resistant to other"gold standard"treatments such as vancomycin, and there is disagreement about the relative efficacy of vancocymin compared to linezolid. There are several review papers employing Bayesian Network Meta-Analyses (BNMAs) to investigate which treatments are best for MRSA related cSSSIs, but none address time-based design inconsistencies. This paper proposes a time-varying BNMA (tBNMA), which models time-varying treatment effects across studies using a Gaussian Process kernel. A dataset is compiled from nine existing MRSA cSSSI NMA review papers containing 58 studies comparing 19 treatments over 19 years. tBNMA finds evidence of a non-linear trend in the treatment effect of vancomycin - it became less effective than linezolid between 2002 and 2007, but has since recovered statistical equivalence.
Describing emotions with acoustic property prompts for speech emotion recognition

Emotions lie on a broad continuum and treating emotions as a discrete number of classes limits the ability of a model to capture the nuances in the continuum. The challenge is how to describe the nuances of emotions and how to enable a model to learn the descriptions. In this work, we devise a method to automatically create a description (or prompt) for a given audio by computing acoustic properties, such as pitch, loudness, speech rate, and articulation rate. We pair a prompt with its corresponding audio using 5 different emotion datasets. We trained a neural network model using these audio-text pairs. Then, we evaluate the model using one more dataset. We investigate how the model can learn to associate the audio with the descriptions, resulting in performance improvement of Speech Emotion Recognition and Speech Audio Retrieval. We expect our ﬁndings to motivate research describing the broad continuum of emotion.
Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models

Disentangling facial movements from other facial characteristics, particularly from facial identity, remains a chal-lenging task, as facial movements display great variation between individuals. In this paper, we aim to characterize individual-speciﬁc facial movements. We present a novel training approach to learn facial movements independently of other facial characteristics, focusing on each individual separately. We propose self-supervised Person-Speciﬁc Models (PSMs), in which one model per individual can learn to extract an embedding of the facial movements independently of the person’s identity and other structural facial characteristics from unlabeled facial video. These models are trained using encoder-decoder-like architectures. We provide quantitative and qualitative evidence that a PSM learns a meaningful facial embedding that discovers ﬁne-grained movements otherwise not characterized by a General Model (GM), which is trained across individuals and characterizes general patterns of facial movements. We present quantitative and qualitative evidence that this approach is easily scalable and generalizable for new individuals: facial movements knowledge learned on a person can quickly and effectively be transferred to a new person. Lastly, we propose a novel PSM using curriculum temporal learning to leverage the temporal contiguity between video frames. Our code, analysis details, and all pretrained models are available in Github and Supplementary Materials.
Flowgrad: Using Motion for Visual Sound Source Localization

Most recent work in visual sound source localization relies on semantic audio-visual representations learned in a self-supervised manner and, by design, excludes temporal information present in videos. While it proves to be effective for widely used benchmark datasets, the method falls short for challenging scenarios like urban traffic. This work introduces temporal context into the state-of-the-art methods for sound source localization in urban scenes using optical flow to encode motion information. An analysis of the strengths and weaknesses of our methods helps us better understand the problem of visual sound source localization and sheds light on open challenges for audio-visual scene understanding. The code and pretrained models are publicly available at https://github.com/rrrajjjj/flowgrad
Weighted Sum-Rate Maximization with Causal Inference for Latent Interference Estimation

The paper investigates the weighted sum-rate maximization (WSRM) problem with latent interfering sources outside the known network, whose power allocation policy is hidden from and uncontrollable to optimization. The paper extends the famous alternate optimization algorithm weighted minimum mean square error (WMMSE) [1] under a causal inference framework to tackle with WSRM. Specifically, with the possibility of power policy shifting in the hidden network, computing an iterating direction based only on the observed interference inherently implies that counterfactual is ignored in decision making. A method called synthetic control (SC) is used to estimate the counterfactual. For any link in the known network, SC constructs a convex combination of the interference on other links and uses it as an estimate for the counterfactual. Power iteration in the proposed SC-WMMSE is performed taking into account both the observed interference and its counterfactual. SC-WMMSE requires no more information than the original WMMSE in the optimization stage. To our best knowledge, this is the first paper explores the potential of SC in assisting mathematical optimization in addressing classic wireless optimization problems. Numerical results suggest the superiority of the SC-WMMSE over the original in both convergence and objective.
Robust estimation for Threshold Autoregressive Moving-Average models

Threshold autoregressive moving-average (TARMA) models are popular in time series analysis due to their ability to parsimoniously describe several complex dynamical features. However, neither theory nor estimation methods are currently available when the data present heavy tails or anomalous observations, which is often the case in applications. In this paper, we provide the first theoretical framework for robust M-estimation for TARMA models and also study its practical relevance. Under mild conditions, we show that the robust estimator for the threshold parameter is super-consistent, while the estimators for autoregressive and moving-average parameters are strongly consistent and asymptotically normal. The Monte Carlo study shows that the M-estimator is superior, in terms of both bias and variance, to the least squares estimator, which can be heavily affected by outliers. The findings suggest that robust M-estimation should be generally preferred to the least squares method. Finally, we apply our methodology to a set of commodity price time series; the robust TARMA fit presents smaller standard errors and leads to superior forecasting accuracy compared to the least squares fit. The results support the hypothesis of a two-regime, asymmetric nonlinearity around zero, characterised by slow expansions and fast contractions.
QuDiet: A classical simulation platform for qubit-qudit hybrid quantum systems

In the recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state-of-the-art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher-dimensional quantum computing systems. Through recent developments in higher-dimensional quantum computing systems, it is realized that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, we introduce \textbf{QuDiet}, a state-of-the-art user-friendly python-based higher-dimensional quantum computing simulator. \textbf{QuDiet} offers multi-valued logic operations by utilizing generalized quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. We simulate various benchmark quantum circuits in \textbf{QuDiet} and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, \textbf{QuDiet} provides a full qubit-qudit hybrid quantum simulator package with quantum circuit templates of well-known quantum algorithms for fast prototyping and simulation. The complete code and packages of \textbf{QuDiet} is available at https://github.com/LegacYFTw/QuDiet so that other platforms can incorporate it as a classical simulation option for qubit-qudit hybrid systems to their platforms.
Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation

Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based on U-Net model have been proposed in the literature. However, there are relatively few improvements for the advanced UNet++ model. In our paper, we propose an encoding feature supervised UNet++(ES-UNet++) and apply it to the liver and tumor segmentation. ES-UNet++ consists of an encoding UNet++ and a segmentation UNet++. The well-trained encoding UNet++ can extract the encoding features of label map which are used to additionally supervise the segmentation UNet++. By adding supervision to the each encoder of segmentation UNet++, U-Nets of different depths that constitute UNet++ outperform the original version by average 5.7% in dice score and the overall dice score is thus improved by 2.1%. ES-UNet++ is evaluated with dataset LiTS, achieving 95.6% for liver segmentation and 67.4% for tumor segmentation in dice score. In this paper, we also concluded some valuable properties of ES-UNet++ by conducting comparative anaylsis between ES-UNet++ and UNet++:(1) encoding feature supervision can accelerate the convergence of the model. (2) encoding feature supervision enhances the effect of model pruning by achieving huge speedup while providing pruned models with fairly good performance.
Classification of local ultraluminous infrared galaxies and quasars with kernel principal component analysis

We present a new diagnostic diagram for local ultraluminous infrared galaxies (ULIRGs) and quasars, analysing particularly the Spitzer Space Telescope's Infrared Spectrograph (IRS) spectra of 102 local ULIRGs and 37 Palomar Green quasars. Our diagram is based on a special non-linear mapping of these data, employing the Kernel Principal Component Analysis method. The novelty of this map lies in the fact that it distributes the galaxies under study on the surface of a well-defined ellipsoid, which, in turn, links basic concepts from geometry to physical properties of the galaxies. Particularly, we have found that the equatorial direction of the ellipsoid corresponds to the evolution of the power source of ULIRGs, starting from the pre-merger phase, moving through the starburst-dominated coalescing stage towards the active galactic nucleus (AGN)-dominated phase, and finally terminating with the post-merger quasar phase. On the other hand, the meridian directions distinguish deeply obscured power sources of the galaxies from unobscured ones. These observations have also been verified by comparison with simulated ULIRGs and quasars using radiative transfer models. The diagram correctly identifies unique galaxies with extreme features that lie distinctly away from the main distribution of the galaxies. Furthermore, special two-dimensional projections of the ellipsoid recover almost monotonic variations of the two main physical properties of the galaxies, the silicate and PAH features. This suggests that our diagram naturally extends the well-known Spoon diagram and it can serve as a diagnostic tool for existing and future infrared spectroscopic data, such as those provided by the James Webb Space Telescope.
On Penalization in Stochastic Multi-armed Bandits

We study an important variant of the stochastic multi-armed bandit (MAB) problem, which takes penalization into consideration. Instead of directly maximizing cumulative expected reward, we need to balance between the total reward and fairness level. In this paper, we present some new insights in MAB and formulate the problem in the penalization framework, where rigorous penalized regret can be well defined and more sophisticated regret analysis is possible. Under such a framework, we propose a hard-threshold UCB-like algorithm, which enjoys many merits including asymptotic fairness, nearly optimal regret, better tradeoff between reward and fairness. Both gap-dependent and gap-independent regret bounds have been established. Multiple insightful comments are given to illustrate the soundness of our theoretical analysis. Numerous experimental results corroborate the theory and show the superiority of our method over other existing methods.
Giant Magnetoelectric coupling in Single Phase Pb(Zr0.20Ti0.80)0.70Pd0.30O3-{\delta} Multiferroics

During the last fifteen years, multiferroic (MF) research communities have been searching for an alternative room temperature MF material with large magnetoelectric (ME) coupling for possible applications in high density electronic components, low heat dissipation memory and logic devices. We have studied Pb(Zr0.20Ti0.80)0.70Pd0. 30O3-{\delta} (PZTP30) system with an unusually large (30%) palladium occupancy in B site of PZT. This material exhibited a giant ME coupling coefficient ~0.36 mV/cm.Oe. Interestingly, this is the first time any room temperature single phase compound that showed ME trends, and magnitude similar to those in the well established mechanical strain-mediated ferroelectric and ferromagnetic composites; the latter ones are already in the commercial stage as nT/pT magnetic field sensors due to their large ME values. The presence of Pd in PZTP30 has been confirmed by XPS and XRF studies and assigned with related binding energies of Pd+2 and Pd+4 ions as 336.37 eV, 342.9 eV, and 337.53 eV, 343.43 eV, respectively, which may be the origin of room temperature magnetism in Pd substituted PZT ceramics. A sharp first order ferroelectric phase transition was observed at ~569 K (+/-5 K) that is confirmed from dielectric, Raman, and thermal analysis. Both ferromagnetic and ferroelectric orderings with large ME coupling were found above room temperature, a significant step forward in the development of single phase ME material with enhanced functionalities.