Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender

Individuals signal aspects of their identity and beliefs through linguistic choices. Studying these choices in aggregate allows us to examine large-scale attitude shifts within a population. Here, we develop computational methods to study word choice within a sociolinguistic lexical variable—alternate words used to express the same concept—in order to test for change in the United States towards sexuality and gender. We examine two variables: i) referents to significant others, such as the word “partner” and ii) referents to an indefinite person, both of which could optionally be marked with gender. The linguistic choices in each variable allow us to study increased rates of acceptances of gay marriage and gender equality, respectively. In longitudinal analyses across Twitter and Reddit over 87M messages, we demonstrate that attitudes are changing but that these changes are driven by specific demographics within the United States. Further, in a quasi-causal analysis, we show that passages of Marriage Equality Acts in different states are drivers of linguistic change.
Tumera: Tutor of Photography Beginners

With the popularity of photographic equipment, more and more people are starting to learn photography by themselves. Although they have easy access to photographic materials, it is uneasy to obtain professional feedback or guidance that can help them improve their photography skills. Therefore, we develop an intelligently interactive system, Tumera, that provides aesthetics guidance for photography beginners. When shooting, Tumera gives timely feedback on the pictures in the view port. After shooting, scores evaluating the aesthetic quality of different aspects of the photos and corresponding improvement suggestions are given. Tumera allows users to share, rank, discuss, and learn from their works and interaction with the system based on the scores and suggestions. In the experiment, Tumera showed good accuracy, real-time computing ability, and effective guiding performance.
Energy Efficient Control of Electric Motors

This paper presents development of an optimal feedback linearization control (OFLC) for interior permanent magnet (PM) synchronous machines operating in a non steady-sate operating point, i.e., varying torque and speed, to achieve precision tracking performance and energy saving by minimizing the copper loss. An isomorphism mapping between the dq-axes phase voltages and two auxiliary control inputs over full ranges of torque and speed is established by the linearization controller using the notion of orthogonal projection. The auxiliary control inputs are defined to be exclusively responsible for torque generation and power consumption. Subsequently, an analytical solution for the optimal-linearization control is derived in a closed-form by applying the Hamiltonian of optimal control theory in conjunction with the Pontryagin’s minimum principle. The optimal controller takes the maximum voltage limit and torque tracking constraint into account while maximizing machine efficiency for non-constant operational load torque and speed. Unlike the convectional quadratic regulator-based control of electric motors, the proposed control approach does not rely on steady-state operation conditions and hence it is suitable for such applications as electric vehicles and robotics.
Non-Parametric Online Learning from Human Feedback for Neural Machine Translation

We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system. However, previous methods require online model updating or additional translation memory networks to achieve high-quality performance, making them inflexible and inefficient in practice. In this paper, we propose a novel non-parametric online learning method without changing the model structure. This approach introduces two k-nearest-neighbor (KNN) modules: one module memorizes the human feedback, which is the correct sentences provided by human translators, while the other balances the usage of the history human feedback and original NMT models adaptively. Experiments conducted on EMEA and JRC-Acquis benchmarks demonstrate that our proposed method obtains substantial improvements on translation accuracy and achieves better adaptation performance with less repeating human correction operations.
Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data

Generative models trained with Differential Privacy (DP) can be used to generate synthetic data while minimizing privacy risks. We analyze the impact of DP on these models vis-a-vis underrepresented classes/subgroups of data, specifically, studying: 1) the size of classes/subgroups in the synthetic data, and 2) the accuracy of classification tasks run on them. We also evaluate the effect of various levels of imbalance and privacy budgets. Our analysis uses three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN) and shows that DP yields opposite size distributions in the generated synthetic data. It affects the gap between the majority and minority classes/subgroups; in some cases by reducing it (a"Robin Hood"effect) and, in others, by increasing it (a"Matthew"effect). Either way, this leads to (similar) disparate impacts on the accuracy of classification tasks on the synthetic data, affecting disproportionately more the underrepresented subparts of the data. Consequently, when training models on synthetic data, one might incur the risk of treating different subpopulations unevenly, leading to unreliable or unfair conclusions.
Witnessing quantum criticality and entanglement in the triangular antiferromagnet KYbSe$_2$

The Heisenberg triangular lattice quantum spin liquid and the phase transitions to nearby magnetic orders have received much theoretical attention, but clear experimental manifestations of these states are rare. This work investigates a new spin-half Yb$^{3+}$ delafossite material, KYbSe$_2$, whose inelastic neutron scattering spectra reveal a diffuse continuum with a sharp lower bound. Applying entanglement witnesses to the data reveals significant multipartite entanglement spread between its neighbors, and analysis of its magnetic exchange couplings shows close proximity to the triangular lattice Heisenberg quantum spin liquid. Key features of the data are reproduced by Schwinger-boson theory and tensor network calculations with a significant second-neighbor coupling $J_2$. The strength of the dynamical structure factor at the $K$ point shows a scaling collapse in $\hbar\omega/k_\mathrm{B}T$ down to 0.3 K, indicating a second-order quantum phase transition. Comparing this to previous theoretical work suggests that the proximate phase at larger $J_2$ is a gapped $\mathbb{Z}_2$ spin liquid, resolving a long-debated issue. We thus show that KYbSe$_2$ is close to a spin liquid phase, which in turn sheds light on the theoretical phase diagram itself.
A Profile-Based Binary Feature Extraction Method Using Frequent Itemsets for Improving Coronary Artery Disease Diagnosis

Recent years have seen growing interest in the diagnosis of Coronary Artery Disease (CAD) with machine learning methods to reduce the cost and health implications of conventional diagnosis. This paper introduces a CAD diagnosis method with a novel feature extraction technique called the Profile-Based Binary Feature Extraction (PBBFE). In this method, after partitioning numerical features, frequent itemsets are extracted by the Apriori algorithm and then used as features to increase the CAD diagnosis accuracy. The proposed method consists of two main phases. In the first phase, each patient is assigned a profile based on age, gender, and medical condition, and then all numerical features are discretized based on assigned profiles. All features then undergo a binarization process to become ready for feature extraction by Apriori. In the last step of this phase, frequent itemsets are extracted from the dataset by Apriori and used to build a new dataset. In the second phase, the Genetic Algorithm and the Support Vector Machine are used to identify the best subset of extracted features for classification. The proposed method was tested on the Z-Alizadeh Sani dataset, which is one the richest databases in the field of CAD. Performance comparisons conducted on this dataset showed that the proposed method outperforms all major alternative methods with 98.35% accuracy, 100% sensitivity, and 94.25% specificity. The proposed method also achieved the highest accuracy on several other datasets.
Detecting Oceans on Exoplanets with Phase-dependent Spectral Principal Component Analysis

Stable surface liquid water is a key indicator of exoplanet habitability. However, few approaches exist for directly detecting oceans on potentially Earth-like exoplanets. In most cases, specular reflection of host starlight from surface bodies of water—referred to as ocean glint—proves to be an important aspect of liquids that can enable detection of habitable conditions. Here, we propose that spectral principal component analysis (PCA) applied to orbital phase-dependent observations of Earth-like exoplanets can provide a straightforward means of detecting ocean glint and thus habitability. Using high-fidelity, orbit-resolved spectral models of Earth, and for instrument capabilities applicable to proposed exo-Earth direct imaging concept missions, the extreme reddening effect of crescent-phase ocean glint is demonstrated as the primary spectral component that explains phase-dependent variability for orbital inclinations spanning 60°–90°. At smaller orbital inclinations where more-extreme crescent phases cannot be accessed, glint can still significantly increase planetary brightness but reddening effects are less pronounced, and as a result, glint is not plainly indicated by phase-dependent spectral PCA. Using instrument models for future exoplanet direct imaging mission concepts, we show that brightness enhancements due to glint could be detected across a wide range of orbital inclinations with typical exposure times measured in hours to weeks, depending on system distance and mission architecture. Thus, brightness increases due to glint are potentially detectable for Earth-like exoplanets for most system inclinations, and phase-dependent spectral PCA could indicate reddening due to glint for a subset of these inclinations.
Position Paper: Goals of the Luau Type System

Luau is the scripting language that powers user-generated experiences on the Roblox platform. It is a statically-typed language, based on the dynamically-typed Lua language, with type inference. These types are used for providing editor assistance in Roblox Studio, the IDE for authoring Roblox experiences. Due to Roblox's uniquely heterogeneous developer community, Luau must operate in a somewhat different fashion than a traditional statically-typed language. In this paper, we describe some of the goals of the Luau type system, focusing on where the goals differ from those of other type systems.
Pairwise Emotional Relationship Recognition in Drama Videos: Dataset and Benchmark

Recognizing the emotional state of people is a basic but challenging task in video understanding. In this paper, we propose a new task in this field, named Pairwise Emotional Relationship Recognition (PERR). This task aims to recognize the emotional relationship between the two interactive characters in a given video clip. It is different from the traditional emotion and social relation recognition task. Varieties of information, consisting of character appearance, behaviors, facial emotions, dialogues, background music as well as subtitles contribute differently to the final results, which makes the task more challenging but meaningful in developing more advanced multi-modal models. To facilitate the task, we develop a new dataset called Emotional RelAtionship of inTeractiOn (ERATO) based on dramas and movies. ERATO is a large-scale multi-modal dataset for PERR task, which has 31,182 video clips, lasting about 203 video hours. Different from the existing datasets, ERATO contains interaction-centric videos with multi-shots, varied video length, and multiple modalities including visual, audio and text. As a minor contribution, we propose a baseline model composed of Synchronous Modal-Temporal Attention (SMTA) unit to fuse the multi-modal information for the PERR task. In contrast to other prevailing attention mechanisms, our proposed SMTA can steadily improve the performance by about 1%. We expect the ERATO as well as our proposed SMTA to open up a new way for PERR task in video understanding and further improve the research of multi-modal fusion methodology.
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces

Many real world scientific and industrial applications require optimizing multiple competing black-box objectives. When the objectives are expensive-to-evaluate, multi-objective Bayesian optimization (BO) is a popular approach because of its high sample efficiency. However, even with recent methodological advances, most existing multi-objective BO methods perform poorly on search spaces with more than a few dozen parameters and rely on global surrogate models that scale cubically with the number of observations. In this work we propose MORBO, a scalable method for multi-objective BO over high-dimensional search spaces. MORBO identifies diverse globally optimal solutions by performing BO in multiple local regions of the design space in parallel using a coordinated strategy. We show that MORBO significantly advances the state-of-the-art in sample efficiency for several high-dimensional synthetic problems and real world applications, including an optical display design problem and a vehicle design problem with 146 and 222 parameters, respectively. On these problems, where existing BO algorithms fail to scale and perform well, MORBO provides practitioners with order-of-magnitude improvements in sample efficiency over the current approach.
A Validated Privacy-Utility Preserving Recommendation System with Local Differential Privacy

This paper proposes a new recommendation system preserving both privacy and utility. It relies on the local differential privacy (LDP) for the browsing user to transmit his noisy preference profile, as perturbed Bloom filters, to the service provider. The originality of the approach is multifold. First, as far as we know, the approach is the first one including at the user side two perturbation rounds - PRR (Permanent Randomized Response) and IRR (Instantaneous Randomized Response) - over a complete user profile. Second, a full validation experimentation chain is set up, with a machine learning decoding algorithm based on neural network or XGBoost for decoding the perturbed Bloom filters and the clustering Kmeans tool for clustering users. Third, extensive experiments show that our method achieves good utility-privacy trade-off, i.e. a 90% clustering success rate, resp. 80.3% for a value of LDP $\epsilon=0.8$, resp. $\epsilon=2$. Fourth, an experimental and theoretical analysis gives concrete results on the resistance of our approach to the plausible deniability and resistance against averaging attacks.
Bridging the gap between topological non-Hermitian physics and open quantum systems

We relate topological properties of non-Hermitian systems and observables of quantum open systems by using the Keldysh path-integral method. We express Keldysh Green's functions in terms of effective non-Hermitian Hamiltonians that contain all the relevant topological information. We arrive at a frequency dependent topological index that is linked to the response of the system to perturbations at a given frequency. We show how to detect a transition between different topological phases by measuring the response to local perturbations. Our formalism is exemplified in a 1D Hatano-Nelson model, highlighting the difference between the bosonic and fermionic cases
Reinforcement Learning Under Algorithmic Triage

Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other. Under algorithmic triage, a supervised learning model predicts a fraction of the instances and humans predict the remaining ones. In this work, we take a first step towards developing reinforcement learning models that are optimized to operate under algorithmic triage. To this end, we look at the problem through the framework of options and develop a two-stage actor-critic method to learn reinforcement learning models under triage. The first stage performs offline, off-policy training using human data gathered in an environment where the human has operated on their own. The second stage performs on-policy training to account for the impact that switching may have on the human policy, which may be difficult to anticipate from the above human data. Extensive simulation experiments in a synthetic car driving task show that the machine models and the triage policies trained using our two-stage method effectively complement human policies and outperform those provided by several competitive baselines.
An Efficient and Scalable Collection of Fly-Inspired Voting Units for Visual Place Recognition in Changing Environments

State-of-the-art visual place recognition performance is currently being achieved utilizing deep learning based approaches. Despite the recent efforts in designing lightweight convolutional neural network based models, these can still be too expensive for the most hardware restricted robot applications. Low-overhead visual place recognition techniques would not only enable platforms equipped with low-end, cheap hardware but also reduce computation on more powerful systems, allowing these resources to be allocated for other navigation tasks. In this work, our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations. Our first contribution is DrosoNet, an exceptionally compact model inspired by the odor processing abilities of the fruit fly, Drosophila melanogaster. Our second and main contribution is a voting mechanism that leverages multiple small and efficient classifiers to achieve more robust and consistent visual place recognition compared to a single one. We use DrosoNet as the baseline classifier for the voting mechanism and evaluate our models on five benchmark datasets, assessing moderate to extreme appearance changes and small to moderate viewpoint variations. We then compare the proposed algorithms to state-of-the-art methods, both in terms ofarea under the precision-recall curve results and computational efficiency.
A new threshold model based on tropical mathematics reveals network backbones

Maxmin-ω is a new threshold model, where each node in a network waits for the arrival of states from a fraction ω of neighborhood nodes before processing its own state, and subsequently transmitting it to downstream nodes. Repeating this sequence of events leads to periodic behavior. We show that maxmin-ω reduces to a smaller, simpler, system represented by tropical mathematics, which forges a useful link between the nodal state update times and circuits in the network. Thus, the behavior of the system can be analyzed directly from the smaller network structure and is computationally faster. We further show that these reduced networks: (i) are not unique; they are dependent on the initialisation time, (ii) tend towards periodic orbits of networks – “attractor networks. " In light of these features, we vary the initial condition and ω to obtain statistics on types of attractor networks. The results suggest the case ω = 0.5 to give the most stable system. We propose that the most prominent attractor networks of nodes and edges may be seen as a ‘backbone’ of the original network. We subsequently provide an algorithm to construct attractor networks, the main result being that they must necessarily contain circuits that are deemed critical and that can be identified without running the maxmin-ω system. Finally, we apply this work to the C. elegans neuron network, giving insight into its function and similar networks. This novel area of application for tropical mathematics adds to its theory, and provides a new way to identify important edges and circuits of a network.
The Matching Process and Independent Process in Random Regular Graphs and Hypergraphs

In this note, we analyze two random greedy processes on sparse random graphs and hypergraphs with a given degree sequence. First we analyze the matching process, which builds a set of disjoint edges one edge at a time; then we analyze the independent process, which builds an independent set of vertices one vertex at a time. We use the differential equations method and apply a general theorem of Warnke. Our main contribution is to significantly reduce the associated systems of differential equations and simplify the expression for the final size of the matching or independent set.
Overexpression of IRF3 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma

Background Growing findings have demonstrated that interferon regulatory transcription factor (IRF) family members are linked to the progression of various cancers. However, the roles of IRFs in clear cell renal cell carcinoma (ccRCC) remain undefined. Herein, we conducted a comprehensive analysis using the bioinformatics method to evaluate the expression patterns, clinical significance, and regulation of IRFs-related mechanisms in patients with ccRCC. Methods Data from the Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGA), and Gene Expression Omnibus (GEO) databases were used for investigation comprehensively. Specifically, we carried out a series of analyses to identify the candidate IRF and to explore its potential action mechanisms using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. What is more, we emphatically investigate the association of candidate IRF with tumor immunity in ccRCC through the CIBERSORT algorithm, TIMER and GEPIA databases. Results Herein, IRF3 was identified as candidate IRF, which was highly expressed in ccRCC, and its overexpression was significantly associated with worse clinical outcomes and adverse overall survival. Uni- and multi-variate Cox regression analysis demonstrated that IRF3 overexpression was an independent predictor of worse prognosis. Functional enrichment analysis showed that IRF3 might participate in several cancer-related biological processes and signaling pathways, thereby promoting the progression of ccRCC. Additionally, we found that IRF3 was remarkably associated with tumor-infiltrating immune cells (TIICs) and various immune-related genes. Conclusion Herein, we identified IRF3 from the IRF gene family members, which could serve as promising prognostic marker and therapeutic target in ccRCC.