Miracle-dz/UniFAR-FTUs huggingface dataset:
Photonic Dirac Waveguides

The Dirac equation is a paradigmatic model that describes a range of intriguing properties of relativistic spin-½ particles, from the existence of antiparticles to Klein tunneling. However, the Dirac-like equations have found application far beyond its original scope, and has been used to comprehend the properties of graphene and topological phases of matter. In the field of photonics, the opportunity to emulate Dirac physics has also enabled topological photonic insulators. In this paper, we demonstrate that judiciously engineered synthetic potentials in photonic Dirac systems can offer physical properties beyond both the elementary/quasi-particles’ and topological realms. Specifically, we introduce a new class of optical “Dirac waveguides”, whose guided electromagnetic modes are endowed with pseudo-spin degree of freedom. Pseudo-spin coupled with the ability to engineer synthetic gauge potentials acting on it, enables control over the guided modes which is unattainable in conventional optical waveguides. In particular, we use a silicon nanophotonic metasurface that supports pseudo-spin degree of freedom as a testing platform to predict and experimentally confirm a spin-full nature of the Dirac waveguides. We also demonstrate that, for suitable trapping potentials, the guided modes exhibit spin-dependent field distributions, which gives rise to their distinct transport and radiative properties. Thereby, the Dirac waveguides manifest spin-dependent radiative lifetimes – the non-Hermitian spin-Hall effect – and open new avenues for spin-multiplexing, controlling characteristics of guided optical modes, and tuning light-matter interactions with photonic pseudo-spins. waveguide mode. Image of real space propagation of the excited Dirac waveguide mode was captured by the NIR camera.
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification

Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and LightXML, leverage a transformer instance to achieve state-of-the-art performance. However, in this process, these approaches need to make various trade-offs between performance and computational requirements. A major shortcoming, as compared to the Bi-LSTM based AttentionXML, is that they fail to keep separate feature representations for each resolution in a label tree. We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations. CascadeXML significantly outperforms all existing approaches with non-trivial gains obtained on benchmark datasets consisting of up to three million labels. Code for CascadeXML will be made publicly available at \url{https://github.com/xmc-aalto/cascadexml}.
Singing Voice Synthesis with Vibrato Modeling and Latent Energy Representation

This paper proposes an expressive singing voice synthesis system by introducing explicit vibrato modeling and latent energy representation. Vibrato is essential to the naturalness of synthesized sound, due to the inherent characteristics of human singing. Hence, a deep learning-based vibrato model is introduced in this paper to control the vibrato's likeliness, rate, depth and phase in singing, where the vibrato likeliness represents the existence probability of vibrato and it would help improve the singing voice's naturalness. Actually, there is no annotated label about vibrato likeliness in existing singing corpus. We adopt a novel vibrato likeliness labeling method to label the vibrato likeliness automatically. Meanwhile, the power spectrogram of audio contains rich information that can improve the expressiveness of singing. An autoencoder-based latent energy bottleneck feature is proposed for expressive singing voice synthesis. Experimental results on the open dataset NUS48E show that both the vibrato modeling and the latent energy representation could significantly improve the expressiveness of singing voice. The audio samples are shown in the demo website11https://mango321321.github. io/ExpressiveSing/.
Propensity score models are better when post-calibrated

Theoretical guarantees for causal inference using propensity scores are partly based on the scores behaving like conditional probabilities. However, scores between zero and one, especially when outputted by ﬂexible statistical estimators, do not necessarily behave like probabilities. We perform a simulation study to assess the error in estimating the average treatment eﬀect before and after applying a simple and well-established post-processing method to calibrate the propensity scores. We ﬁnd that post-calibration reduces the error in eﬀect estimation for expressive uncalibrated statistical estimators, and that this improvement is not mediated by better balancing. The larger the initial lack of calibration, the larger the improvement in eﬀect estimation, with the eﬀect on already-calibrated estimators being very small. Given the improvement in eﬀect estimation and that post-calibration is computationally cheap, we recommend it will be adopted when modelling propensity scores with expressive models.
Silver Nanowires on Carbon Nanotube Aerogel Sheets for Flexible, Transparent Electrodes.

Flexible, free-standing transparent conducting electrodes (TCEs) with simultaneously tunable transmittances up to 98% and sheet resistances down to 11 Ω/sq were prepared by a facile spray-coating method of silver nanowires (AgNWs) onto dry-spun multiwall carbon nanotube (MWNT) aerogels. Counterintuitively, the transmittance of the hybrid electrodes can be increased as the mass density of AgNWs within the MWNT aerogels increases, however, the final achievable transmittance depends on the initial transparency of the MWNT aerogels. At the same time, a strong decrease in sheet resistance is obtained when AgNWs form a percolated network along the MWNT aerogel. Additionally, anisotropic reduction in sheet resistance and polarized transmittance of AgNW/MWNT aerogels is achieved with this method. The final AgNW/MWNT hybrid TCEs transmittance and sheet resistance can be fine-tuned by spray-coating mechanisms or by the choice of initial MWNT aerogel density. Thus, a wide range of AgNW/MWNT hybrid TCEs with optimized optoelectronic properties can be achieved depending of the requirements needed. Finally, the free-standing AgNW/MWNT hybrid TCEs can be laminated onto a wide range of substrates without the need of a bonding aid.
COUNTING UNIONS OF SCHREIER SETS

<jats:p>A subset of positive integers <jats:italic>F</jats:italic> is a Schreier set if it is nonempty and <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline1. png" /> <jats:tex-math> $|F|\leqslant \min F$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> (here <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline2.png" /> <jats:tex-math> $|F|$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> is the cardinality of <jats:italic>F</jats:italic>). For each positive integer <jats:italic>k</jats:italic>, we define <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline3. png" /> <jats:tex-math> $k\mathcal {S}$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> as the collection of all the unions of at most <jats:italic>k</jats:italic> Schreier sets. Also, for each positive integer <jats:italic>n</jats:italic>, let <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline4. png" /> <jats:tex-math> $(k\mathcal {S})^n$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> be the collection of all sets in <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline5. png" /> <jats:tex-math> $k\mathcal {S}$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> with maximum element equal to <jats:italic>n</jats:italic>. It is well known that the sequence <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline6. png" /> <jats:tex-math> $(|(1\mathcal {S})^n|)_{n=1}^\infty $ </jats:tex-math> </jats:alternatives> </jats:inline-formula> is the Fibonacci sequence. In particular, the sequence satisfies a linear recurrence. We show that the sequence <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S0004972723001326_inline7. png" /> <jats:tex-math> $(|(k\mathcal {S})^n|)_{n=1}^\infty $ </jats:tex-math> </jats:alternatives> </jats:inline-formula> satisfies a linear recurrence for every positive <jats:italic>k</jats:italic>. </jats:p>
Concrete Score Matching: Generalized Score Matching for Discrete Data

Representing probability distributions by the gradient of their density functions has proven effective in modeling a wide range of continuous data modalities. However, this representation is not applicable in discrete domains where the gradient is undefined. To this end, we propose an analogous score function called the"Concrete score", a generalization of the (Stein) score for discrete settings. Given a predefined neighborhood structure, the Concrete score of any input is defined by the rate of change of the probabilities with respect to local directional changes of the input. This formulation allows us to recover the (Stein) score in continuous domains when measuring such changes by the Euclidean distance, while using the Manhattan distance leads to our novel score function in discrete domains. Finally, we introduce a new framework to learn such scores from samples called Concrete Score Matching (CSM), and propose an efficient training objective to scale our approach to high dimensions. Empirically, we demonstrate the efficacy of CSM on density estimation tasks on a mixture of synthetic, tabular, and high-dimensional image datasets, and demonstrate that it performs favorably relative to existing baselines for modeling discrete data.
3DPG: Distributed Deep Deterministic Policy Gradient Algorithms for Networked Multi-Agent Systems

We present Distributed Deep Deterministic Policy Gradient (3DPG), a multi-agent actor-critic (MAAC) algorithm for Markov games. Unlike previous MAAC algorithms, 3DPG is fully distributed during both training and deployment. 3DPG agents calculate local policy gradients based on the most recently available local data (states, actions) and local policies of other agents. During training, this information is exchanged using a potentially lossy and delaying communication network. The network therefore induces Age of Information (AoI) for data and policies. We prove the asymptotic convergence of 3DPG even in the presence of potentially unbounded Age of Information (AoI). This provides an important step towards practical online and distributed multi-agent learning since 3DPG does not assume information to be available deterministically. We analyze 3DPG in the presence of policy and data transfer under mild practical assumptions. Our analysis shows that 3DPG agents converge to a local Nash equilibrium of Markov games in terms of utility functions expressed as the expected value of the agents local approximate action-value functions (Q-functions). The expectations of the local Q-functions are with respect to limiting distributions over the global state-action space shaped by the agents' accumulated local experiences. Our results also shed light on the policies obtained by general MAAC algorithms. We show through a heuristic argument and numerical experiments that 3DPG improves convergence over previous MAAC algorithms that use old actions instead of old policies during training. Further, we show that 3DPG is robust to AoI; it learns competitive policies even with large AoI and low data availability.
VIINTER: View Interpolation with Implicit Neural Representations of Images

We present VIINTER, a method for view interpolation by interpolating the implicit neural representation (INR) of the captured images. We leverage the learned code vector associated with each image and interpolate between these codes to achieve viewpoint transitions. We propose several techniques that significantly enhance the interpolation quality. VIINTER signifies a new way to achieve view interpolation without constructing 3D structure, estimating camera poses, or computing pixel correspondence. We validate the effectiveness of VIINTER on several multi-view scenes with different types of camera layout and scene composition. As the development of INR of images (as opposed to surface or volume) has centered around tasks like image fitting and super-resolution, with VIINTER, we show its capability for view interpolation and offer a promising outlook on using INR for image manipulation tasks.
Evaluating Impact of Social Media Posts by Executives on Stock Prices

Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.
Venus Long-life Surface Package

Measurements in the atmosphere and at the surface of Venus are required to understand fundamental processes of how terrestrial planets evolve and how they work today. While the European Venus community is unified in its support of the EnVision orbiter proposal for the M5 opportunity, many scientific questions also require in situ Venus exploration. ESA has already explored Venus entry / descent probe science in its Planetary Entry Probe (PEP) study [ESA PEP study, 2010], and Venus balloon science in its Venus Entry Probe Study [ESA VEP study, 2005]; Venus balloons were also explored in detail by the European Venus Explorer (EVE) M1/M2 and M3 proposals [Chassefiere et al., 2009; Wilson et al., 2012]. While those in situ mission concepts remain scientifically compelling and technically feasible, the present call requests new scientific concepts. Therefore, in the present document, we suggest a long-duration lander at Venus, which would be capable of undertaking a seismometry mission, operating in the 460{\deg}C surface conditions of Venus.
Data Assimilation Networks

Data Assimilation aims at estimating the posterior conditional probability density functions based on error statistics of the noisy observations and the dynamical system. State of the art methods are sub‐optimal due to the common use of Gaussian error statistics and the linearization of the non‐linear dynamics. To achieve a good performance, these methods often require case‐by‐case fine‐tuning by using explicit regularization techniques such as inflation and localization. In this paper, we propose a fully data driven deep learning framework generalizing recurrent Elman networks and data assimilation algorithms. Our approach approximates a sequence of prior and posterior densities conditioned on noisy observations using a log‐likelihood cost function. By construction our approach can then be used for general nonlinear dynamics and non‐Gaussian densities. As a first step, we evaluate the performance of the proposed approach by using fully and partially observed Lorenz‐95 system in which the outputs of the recurrent network are fitted to Gaussian densities. We numerically show that our approach, without using any explicit regularization technique, achieves comparable performance to the state‐of‐the‐art methods, IEnKF‐Q and LETKF, across various ensemble size.
A Model-Constrained Tangent Slope Learning Approach for Dynamical Systems

Real-time accurate solutions of large-scale complex dynamical systems are in critical need for control, optimisation, uncertainty quantification, and decision-making in practical engineering and science applications, especially digital twin applications. This paper contributes in this direction a model-constrained tangent slope learning (mcTangent) approach. At the heart of mcTangent is the synergy of several desirable strategies: (i) a tangent slope learning to take advantage of the neural network speed and the time-accurate nature of the method of lines; (ii) a model-constrained approach to encode the neural network tangent slope with the underlying governing equations; (iii) sequential learning strategies to promote long-time stability and accuracy; and (iv) data randomisation approach to implicitly enforce the smoothness of the neural network tangent slope and its likeliness to the truth tangent slope up second order derivatives in order to further enhance the stability and accuracy of mcTangent solutions. Rigorous results are provided to analyse and justify the proposed approach. Several numerical results for transport equation, viscous Burgers equation, and Navier–Stokes equation are presented to study and demonstrate the robustness and long-time accuracy of the proposed mcTangent learning approach.
Discrete and Continuous Ejection Models of the Radio Source Associated with GW170817

The gravity wave source, GW170817, and associated gamma-ray burst (GRB), GRB 170817A, produced radio emission that was detected in multiple epochs of Very Long Baseline Interferometry (VLBI) and with broadband radio photometry. Three unique pieces of observational evidence were determined: a discrete radio-emitting region that moves with an apparent velocity of ≈4c, a discrete region that includes all of the radio flux, and a likely synchrotron self absorption (SSA) spectral turnover on day ∼110 and day ∼160 after ejection. This unprecedented wealth of data for a GRB provides a unique opportunity to understand the radio-emitting plasma that was ejected by the putative merger event. The velocity can constrain the kinematics, and the SSA turnover has been used to constrain the size to one that is much smaller than can be done with an unresolved VLBI image, allowing one to estimate the associated plasmoid size directly from the data and improve estimates of the energetics. Models of the radio emission for both a turbulent, protonic, discrete ballistic ejection and a high dissipation region within an otherwise invisible Poynting flux dominated positron–electron jet are considered. On days ∼110 and ∼160 post-merger, for the range of models presented, the jet power is 2 × 1039–8 × 1040 erg s−1 and the ballistic plasmoid kinetic energy is 3 × 1045–1.5 × 1047 erg. Even though only valid after day 110, this independent analysis augments traditional GRB light curve studies, providing additional constraints on the merger event.
Genetic variation among sea turtle life stages and species suggests connectivity among ocean basins

Abstract Regional genetic differentiation of mitochondrial lineages occurs in migratory species with natal philopatry such as sea turtles. However, early juvenile dispersal represents a key opportunity for gene flow and colonization of new regions through founder events, making it an important yet under‐studied life stage. To assess connectivity among sea turtle life stages and ocean basins, we sequenced mitochondrial DNA (mtDNA) fragments from 35 juveniles sampled in the Gulf of Mexico from the rarely observed dispersal stage across three species: green turtles (Chelonia mydas; n = 30), hawksbills (Eretmochelys imbricata; n = 3), and loggerheads (Caretta caretta; n = 2). We estimated green turtle rookery contributions using a many‐to‐many Bayesian mixed stock analysis that incorporated dispersal probabilities based on rookery size and transport via ocean currents. We assembled a gene tree including 709 distinct mtDNA control region haplotypes from the literature for all seven extant sea turtle species to assess gaps in life‐stage data across ocean basins, as well as contextualize the lineages we sampled from dispersing juveniles. Our results indicate a high likelihood that green turtles sampled in the Gulf of Mexico originated from rookeries along the coast of Mexico, with smaller contributions from Costa Rica and Suriname. The gene tree analysis yielded species‐level relationships consistent with those presented previously, while intra‐species relationships between lineages and ocean basins differed, particularly within loggerhead and green turtle clades. Our results highlight the lack of genetic data from juvenile sea turtles, especially the early dispersal stage, and the potential for these data to answer broader questions of connectivity and diversification across species and lineages.
Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

The current study attempts to compare anti-Asian discourse before and during the COVID-19 pandemic by analyzing big data on Quora, one of the most frequently used community-driven knowledge sites. We created two datasets regarding “Asians” and “anti-Asians” from Quora questions and answers between 2010 and 2021. A total of 1,477 questions and 5,346 answers were analyzed, and the datasets were divided into two time periods: before and during the COVID-19 pandemic. We conducted machine-learning-based topic modeling and deep-learning-based word embedding (Word2Vec). Before the pandemic, the topics of physical difference and racism were prevalent, whereas, after the pandemic, the topics of hate crime, the need to stop Asian hate crimes, and the need for the Asian solidarity movement emerged. Above all, the semantic similarity between Asian and Black people became closer, while the similarity between Asian people and other racial/ethnic groups was diminished. The emergence of negative and radical language, which increased saliently after the outbreak of the pandemic, and the considerably wider semantic distance between Asian and White people indicates that the relationship between the two races has been weakened. The findings suggest a long-term campaign or education system to reduce racial tensions during the pandemic.