Reviewerly/exHarmony huggingface dataset:
User-Centric Conversational Recommendation with Multi-Aspect User Modeling

Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understanding of the current session, ignoring other rich multi-aspect information of the central subjects (i.e., users) in recommendation. In this work, we highlight that the user's historical dialogue sessions and look-alike users are essential sources of user preferences besides the current dialogue session in CRS. To systematically model the multi-aspect information, we propose a User-Centric Conversational Recommendation (UCCR) model, which returns to the essence of user preference learning in CRS tasks. Specifically, we propose a historical session learner to capture users' multi-view preferences from knowledge, semantic, and consuming views as supplements to the current preference signals. A multi-view preference mapper is conducted to learn the intrinsic correlations among different views in current and historical sessions via self-supervised objectives. We also design a temporal look-alike user selector to understand users via their similar users. The learned multi-aspect multi-view user preferences are then used for the recommendation and dialogue generation. In experiments, we conduct comprehensive evaluations on both Chinese and English CRS datasets. The significant improvements over competitive models in both recommendation and dialogue generation verify the superiority of UCCR.
Distinguishing between Quantum and Classical Markovian Dephasing Dissipation

Understanding whether dissipation in an open quantum system is truly quantum is a question of both fundamental and practical interest. We consider n qubits subject to correlated Markovian dephasing and present a sufficient condition for when bath-induced dissipation can generate system entanglement and hence must be considered quantum. Surprisingly, we find that the presence or absence of time-reversal symmetry plays a crucial role: broken time-reversal symmetry is required for dissipative entanglement generation. Further, simply having nonzero bath susceptibilities is not enough for the dissipation to be quantum. We also present an explicit experimental protocol for identifying truly quantum dephasing dissipation and lay the groundwork for studying more complex dissipative systems and finding optimal noise mitigating strategies.
Quantum key distribution integrating with ultra-high-power classical optical communications based on ultra-low-loss fiber

The demand for the integration of quantum key distribution (QKD) and classical optical communication in the same optical fiber medium greatly increases as fiber resources and the flexibility of practical applications are taken into consideration. To satisfy the needs of the mass deployment of ultra-high power required for classical optical networks integrating QKD, we implement the discrete variable quantum key distribution (DV-QKD) under up to 25 dBm launch power from classical channels over 75 km on an ultra-low-loss (ULL) fiber by combining a finite-key security analysis method with the noise model of classical signals. To the best of our knowledge, this is the highest power launched by classical signals on the coexistence of DV-QKD and classical communication. The results exhibit the feasibility and tolerance of our QKD system for use in ultra-high-power classical communications.
BEV-SGD: Best Effort Voting SGD Against Byzantine Attacks for Analog-Aggregation-Based Federated Learning Over the Air

As a promising distributed learning technology, analog aggregation-based federated learning over the air (FLOA) provides high communication efficiency and privacy provisioning under the edge computing paradigm. When all edge devices (workers) simultaneously upload their local updates to the parameter server (PS) through commonly shared time-frequency resources, the PS obtains the averaged update only rather than the individual local ones. While such a concurrent transmission and aggregation scheme reduces the latency and communication costs, it unfortunately renders FLOA vulnerable to Byzantine attacks. Aiming at Byzantine-resilient FLOA, this article starts from analyzing the channel inversion (CI) mechanism that is widely used for power control in FLOA. Our theoretical analysis indicates that although CI can achieve good learning performance in the benign scenarios, it fails to work well with limited defensive capability against Byzantine attacks. Then, we propose a novel scheme called the best effort voting (BEV) power control policy that is integrated with stochastic gradient descent (SGD). Our BEV-SGD enhances the robustness of FLOA to Byzantine attacks, by allowing all the workers to send their local updates at their maximum transmit power. Under worst-case attacks, we derive the expected convergence rates of FLOA with CI and BEV power control policies, respectively. The rate comparison reveals that our BEV-SGD outperforms its counterpart with CI in terms of better convergence behavior, which is verified by experimental simulations.
Attention-based neural network for polarimetric image denoising

In this Letter, we propose an attention-based neural network specially designed for the challenging task of polarimetric image denoising. In particular, the channel attention mechanism is used to effectively extract the features underlying the polarimetric images by rescaling the contributions of channels in the network. In addition, we also design the adaptive polarization loss to make the network focus on the polarization information. Experiments show that our method can well restore the details flooded by serious noise and outperforms previous methods. Moreover, the underlying mechanism of channel attention is revealed visually.
On the status of statistical reporting <i>versus</i> linguistic description in corpus linguistics: a ten-year perspective

This study investigates ( i) whether there has been a shift towards increased statistical focus in corpus linguistic research articles, and, if so, ( ii) whether this has had any repercussions for the attention paid to linguistic description. We investigate this through an analysis of the relative focus on statistical reporting versus linguistic description in the way the results are reported and discussed in research articles published in four major corpus linguistics journals in 2009 and 2019. The results display a marked change: in 2009, a clear majority of the articles exhibit a preference for linguistic description over statistical reporting; in 2019, the exact opposite is true. The number of different statistical techniques employed has also gone up. Whilst the increased statistical focus may reflect increased methodological sophistication, our results show that it has come at a cost: a diminished focus on linguistic description, evident, for example, through fewer text excerpts and linguistic examples, which appears to be symptomatic of increasing distance from the language that is the object of study. We discuss these shifts and suggest some ways of employing sophisticated statistical techniques without sacrificing a focus on language.
Single-Photon Scattering Can Account for the Discrepancies among Entangled Two-Photon Measurement Techniques

Entangled photon pairs are predicted to linearize and increase the efficiency of two-photon absorption, allowing continuous wave laser diodes to drive ultrafast time-resolved spectroscopy and nonlinear processes. Despite a range of theoretical studies and experimental measurements, inconsistencies in the value of the entanglement-enhanced interaction cross section persist. A spectrometer that can temporally and spectrally characterize the entangled photon state before, during, and after any potential two-photon excitation event is constructed. For the molecule rhodamine 6G, which has a virtual state pathway, any entangled two-photon interaction is found to be equal to or weaker than classical, single-photon scattering events. This result can account for the discrepancies among the wide variety of entangled two-photon absorption cross sections reported from different measurement techniques. The reported instrumentation can unambiguously separate classical and entangled effects and therefore is important for the growing field of nonlinear and multiphoton entangled spectroscopy.
Pathways to suicide or collections of vicious cycles? Understanding the complexity of suicide through causal mapping

Suicide is the second leading cause of death among youth ages 10-19 in the USA. While suicide has long been recognized as a multifactorial issue, there is limited understanding regarding the complexities linking adverse childhood experiences (ACEs) to suicide ideation, attempt, and fatality among youth. In this paper, we develop a map of these complex linkages to provide a decision support tool regarding key issues in policymaking and intervention design, such as identifying multiple feedback loops (e.g., involving intergenerational effects) or comprehensively examining the rippling effects of an intervention. We use the methodology of systems mapping to structure the complex interrelationships of suicide and ACEs based on the perceptions of fifteen subject matter experts. Specifically, systems mapping allows us to gain insight into the feedback loops and potential emergent properties of ACEs and youth suicide. We describe our methodology and the results of fifteen one-on-one interviews, which are transformed into individual maps that are then aggregated and simplified to produce our final causal map. Our map is the largest to date on ACEs and suicide among youth, totaling 361 concepts and 946 interrelationships. Using a previously developed open-source software to navigate the map, we are able to explore how trauma may be perpetuated through familial, social, and historical concepts. In particular, we identify connections and pathways between ACEs and youth suicide that have not been identified in prior research, and which are of particular interest for youth suicide prevention efforts.
Lightweight, Maliciously Secure Verifiable Function Secret Sharing

In this work, we present a lightweight construction of verifiable two-party function secret sharing (FSS) for point functions and multi-point functions. Our verifiability method is lightweight in two ways. Firstly, it is concretely efficient, making use of only symmetric key operations and no public key or MPC techniques are involved. Our performance is comparable with the state-of-the-art non-verifiable DPF constructions, and we outperform all prior DPF verification techniques in both computation and communication complexity, which we demonstrate with an implementation of our scheme. Secondly, our verification procedure is essentially unconstrained. It will verify that distributed point function (DPF) shares correspond to some point function irrespective of the output group size, the structure of the DPF output, or the set of points on which the DPF must be evaluated. This is in stark contrast with prior works, which depend on at least one and often all three of these constraints. In addition, our construction is the first DPF verification protocol that can verify general DPFs while remaining secure even if one server is malicious. Prior work on maliciously secure DPF verification could only verify DPFs where the non-zero output is binary and the output space is a large field. As an additional feature, our verification procedure can be batched so that verifying a polynomial number of DPF shares requires the exact same amount of communication as verifying one pair of DPF shares. We combine this packed DPF verification with a novel method for packing DPFs into shares of a multi-point function where the evaluation time, verification time, and verification communication are independent of the number of non-zero points in the function. An immediate corollary of our results are two-server protocols for PIR and PSI that remain secure when any one of the three parties is malicious (either the client or one of the servers).
Is Deep Learning on Tabular Data Enough? An Assessment

It is critical to select the model that best fits the situation while analyzing the data.Many scholars on classification and regression issues have offered ensemble techniques on tabular data, as well as other approaches to classification and regression problems (Like Boosting and Logistic Model tree ensembles).Furthermore, various deep learning algorithms have recently been implemented on tabular data, with the authors claiming that deep models outperform Boosting and Model tree approaches.On a range of datasets including historical geographical data, this study compares the new deep models (TabNet, NODE, and DNF-net) against the boosting model (XGBoost) to see if they should be regarded a preferred choice for tabular data.We look at how much tweaking and computation they require, as well as how well they perform based on the metrics evaluation and statistical significance test.According to our study, XGBoost outperforms these deep models across all datasets, including the datasets used in the journals that presented the deep models.We further show that, when compared to deep models, XGBoost requires considerably less tweaking.In addition, we can also confirm that a combination of deep models with XGBoost outperforms XGBoost alone on almost all datasets.
IndicBART: A Pre-trained Model for Indic Natural Language Generation

In this paper, we study pre-trained sequenceto-sequence models for a group of related languages, with a focus on Indic languages.We present IndicBART, a multilingual, sequenceto-sequence pre-trained model focusing on 11 Indic languages and English.IndicBART utilizes the orthographic similarity between Indic scripts to improve transfer learning between similar Indic languages.We evaluate In-dicBART on two NLG tasks: Neural Machine Translation (NMT) and extreme summarization.Our experiments on NMT and extreme summarization show that a model specific to related languages like IndicBART is competitive with large pre-trained models like mBART50 despite being significantly smaller.It also performs well on very low-resource translation scenarios where languages are not included in pre-training or fine-tuning.Script sharing, multilingual training, and better utilization of limited model capacity contribute to the good performance of the compact IndicBART model.
LEAP: Scaling Numerical Optimization Based Synthesis Using an Incremental Approach

While showing great promise, circuit synthesis techniques that combine numerical optimization with search over circuit structures face scalability challenges due to a large number of parameters, exponential search spaces, and complex objective functions. The LEAP algorithm improves scaling across these dimensions using iterative circuit synthesis, incremental re-optimization, dimensionality reduction, and improved numerical optimization. LEAP draws on the design of the optimal synthesis algorithm QSearch by extending it with an incremental approach to determine constant prefix solutions for a circuit. By narrowing the search space, LEAP improves scalability from four to six qubit circuits. LEAP was evaluated with known quantum circuits such as QFT and physical simulation circuits like the VQE, TFIM, and QITE. LEAP can compile four qubit unitaries up to $59\times$ faster than QSearch and five and six qubit unitaries with up to $1.2\times$ fewer CNOTs compared to the QFAST package. LEAP can reduce the CNOT count by up to $36\times$, or $7\times$ on average, compared to the CQC Tket compiler. Despite its heuristics, LEAP has generated optimal circuits for many test cases with a priori known solutions. The techniques introduced by LEAP are applicable to other numerical-optimization-based synthesis approaches.
An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.
Complaint De-Escalation Strategies on Social Media

To date, the literature offers multiple suggestions for how to recover from service failures, albeit without explicitly addressing customers’ negative, high-arousal states evoked by the failure. The few studies that do address ways to improve negative emotions after failures focus on face-to-face interactions only. Because many customers today prefer to complain on social media, firms must learn how to effectively de-escalate negative, high-arousal emotions through text-based exchanges to achieve successful service recoveries. With three field studies using natural language processing tools and three preregistered controlled experiments, the current research identifies ways to mitigate negative arousal in text-based social media complaining, specifically, active listening and empathy. In detail, increasing active listening and empathy in the firm response evokes gratitude among customers in high-arousal states, even if the actual failure is not (yet) recovered. These findings provide a new theoretical perspective on the role of customer arousal in service failures and recoveries as well as managerially relevant implications for dealing with public social media complaints.
Importance of kernel bandwidth in quantum machine learning

Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is expected to be crucial given the central role hyperparameters play in determining the performance of classical machine learning methods. In this work we introduce the hyperparameter controlling the bandwidth of a quantum kernel and show that it controls the expressivity of the resulting model. We use extensive numerical experiments with multiple quantum kernels and classical datasets to show consistent change in the model behavior from underfitting (bandwidth too large) to overfitting (bandwidth too small), with optimal generalization in between. We draw a connection between the bandwidth of classical and quantum kernels and show analogous behavior in both cases. Furthermore, we show that optimizing the bandwidth can help mitigate the exponential decay of kernel values with qubit count, which is the cause behind recent observations that the performance of quantum kernel methods decreases with qubit count. We reproduce these negative results and show that if the kernel bandwidth is optimized, the performance instead improves with growing qubit count and becomes competitive with the best classical methods.
Quantum optimization of complex systems with a quantum annealer

We perform an in-depth comparison of quantum annealing with several classical optimization techniques, namely, thermal annealing, Nelder-Mead, and gradient descent. The focus of our study is large quasicontinuous potentials that must be encoded using a domain wall encoding. To do this, it is important to first understand the properties of a system that is discretely encoded onto an annealer, in terms of its quantum phases, and the importance of thermal versus quantum effects. We therefore begin with a direct study of the 2D Ising model on a quantum annealer, and compare its properties directly with those of the thermal 2D Ising model. These properties include an Ising-like phase transition that can be induced by either a change in ``quantumness'' of the theory (by way of the transverse field component on the annealer), or by scaling the Ising couplings up or down. This behavior is in accord with what is expected from the physical understanding of the quantum system. We then go on to demonstrate the efficacy of the quantum annealer at minimizing several increasingly hard two-dimensional potentials. For all potentials, we find the general behavior that Nelder-Mead and gradient descent methods are very susceptible to becoming trapped in false minima, while the thermal anneal method is somewhat better at discovering the true minimum. However, and despite current limitations on its size, the quantum annealer performs a minimization very markedly better than any of these classical techniques. A quantum anneal can be designed so the system almost never gets trapped in a false minimum, and rapidly and successfully minimizes the potentials.
Interactions between text content and emoji types determine perceptions of both messages and senders

Emoji increasingly feature alongside written language in interpersonal communication. Boutet et al. (2021) showed that negative-face emoji led to a negativity effect on perceptions of message tone and senders' mood. We extended their design, considering the role of non-face emoji and the impact of text content and emoji on message clarity. We utilised a 3 (sentence valence: negative, neutral, positive) × 5 (emoji type: no emoji, negative face, neutral face, positive face, object emoji) quasi-experimental design and online survey method. Sixty participants each processed 60 stimuli counterbalanced across conditions, rating messages' emotional tone and clarity, and senders’ warmth and emotional state. Cumulative link mixed models were used to analyse responses. We found that sentence valence and emoji type interact, influencing message emotionality and clarity, and perceived sender warmth and state. The congruency of text and emoji was particularly important; results showed that incongruent emoji detracted from message clarity vs. no emoji (or congruent emoji). Congruent emoji typically amplified emotional perceptions of messages and senders. Object emoji were most influential when text was either neutral or positive. Results were consistent with models such as the EASI framework (Van Kleef, 2009), and suggest that compositionality extends to representations of text + emoji.