Miracle-dz/UniFAR-FTUs huggingface dataset:
A Noisy-Label-Learning Formulation for Immune Repertoire Classification and Disease-Associated Immune Receptor Sequence Identification

Immune repertoire classification, a typical multiple instance learning (MIL) problem, is a frontier research topic in computational biology that makes transformative contributions to new vaccines and immune therapies. However, the traditional instance-space MIL, directly assigning bag-level labels to instances, suffers from the massive amount of noisy labels and extremely low witness rate. In this work, we propose a noisy-label-learning formulation to solve the immune repertoire classification task. To remedy the inaccurate supervision of repertoire-level labels for a sequence-level classifier, we design a robust training strategy: The initial labels are smoothed to be asymmetric and are progressively corrected using the model's predictions throughout the training process. Furthermore, two models with the same architecture but different parameter initialization are co-trained simultaneously to remedy the known ``confirmation bias'' problem in the self-training-like schema. As a result, we obtain accurate sequence-level classification and, subsequently, repertoire-level classification. Experiments on the Cytomegalovirus (CMV) and Cancer datasets demonstrate our method's effectiveness and superior performance on sequence-level and repertoire-level tasks. Code available at https://github.com/TencentAILabHealthcare/NLL-IRC.
On Krylov subspace methods for skew-symmetric and shifted skew-symmetric linear systems

Krylov subspace methods for solving linear systems of equations involving skew-symmetric matrices have gained recent attention. Numerical equivalences among Krylov subspace methods for nonsingular skew-symmetric linear systems have been given in Greif et al. [SIAM J. Matrix Anal. Appl. , 37 (2016), pp. 1071--1087]. In this work, we extend the results of Greif et al. to singular skew-symmetric linear systems. In addition, we systematically study three Krylov subspace methods (called S$^3$CG, S$^3$MR, and S$^3$LQ) for solving shifted skew-symmetric linear systems. They all are based on Lanczos triangularization for skew-symmetric matrices, and correspond to CG, MINRES, and SYMMLQ for solving symmetric linear systems, respectively. To the best of our knowledge, this is the first work that studies S$^3$LQ. We give some new theoretical results on S$^3$CG, S$^3$MR, and S$^3$LQ. We also provide the relationship among the three methods and those based on Golub--Kahan bidiagonalization and Saunders--Simon--Yip tridiagonalization. Numerical examples are given to illustrate our theoretical findings.
Towards Univalent Reference Types: The Impact of Univalence on Denotational Semantics

We develop a denotational semantics for general reference types in an impredicative version of guarded homotopy type theory, an adaptation of synthetic guarded domain theory to Voevodsky's univalent foundations. We observe for the first time the profound impact of univalence on the denotational semantics of mutable state. Univalence automatically ensures that all computations are invariant under symmetries of the heap -- a bountiful source of program equivalences. In particular, even the most simplistic univalent model enjoys many new equations that do not hold when the same constructions are carried out in the universes of traditional set-level (extensional) type theory.
Building and Testing a General Intelligence Embodied in a Humanoid Robot

Machines with human-level intelligence should be able to do most economically valuable work. This aligns a major economic incentive with the scientific grand challenge of building a human-like mind. Here we describe our approach to building and testing such a system. Our approach comprises a physical humanoid robotic system; a software based control system for robots of this type; a performance metric, which we call g+, designed to be a measure of human-like intelligence in humanoid robots; and an evolutionary algorithm for incrementally increasing scores on this performance metric. We introduce and describe the current status of each of these. We report on current and historical measurements of the g+ metric on the systems described here.
Assimilation of SMAP Observations Over Land Improves the Simulation and Prediction of Tropical Cyclone Idai

Soil moisture conditions can influence the evolution of a tropical cyclone (TC) that is partially or completely over land. Hence, better constraining soil moisture initial conditions in a numerical weather prediction model can potentially improve predictions of TC evolution near or over land. This study examines the impact of assimilating observations from the NASA Soil Moisture Active Passive (SMAP) mission into the NASA Goddard Earth Observing System (GEOS) global weather model on the prediction of South-West Indian Ocean TC Idai (2019). Two sets of retrospective forecasts of TC Idai are compared in an Observing System Experiment framework: (i) forecasts initialized from an analysis that is comparable to the GEOS operational analysis and (ii) forecasts initialized from an analysis that additionally assimilates SMAP brightness temperature observations over land. Results indicate that SMAP assimilation leads to pronounced improvements in the representation of TC Idai structure and prediction of its intensity and track. The wind speed radius (a measure for TC compactness) is reduced by up to 18% in the analysis with SMAP assimilation relative to the control experiment without SMAP assimilation. The forecast intensity error, measured against the observed intensity, is reduced by up to 23%. The forecast along-track error is reduced by up to 34%, indicating a more accurate propagation speed, while the impact of SMAP assimilation on the forecast cross-track error is neutral. These results provide a valuable demonstration that SMAP assimilation can have a highly beneficial impact on TC prediction in global weather forecast models.
Adaptive Decision Feedback Equalization With Continuous-time Infinite Impulse Response Filters

In high-speed (10+Gb/s) chip-to-chip links, the primary impairments to signal integrity are noise, crosstalk, and a smooth tail in the pulse response resulting in intersymbol interference (ISI) sometimes spanning more than 10 unit intervals (UIs). Although often simple in their implementation, continuous time linear equalizers amplify high-frequency noise and crosstalk and consume extra power. A conventional discretetime (DT) decision feedback equalizer (DFE) is well-suited and power efficient for channels with a few dominant post-cursor ISI terms, however, the power can become prohibitive for channels with many post-cursor ISI terms. Infinite impulse response (IIR) DFEs can equalize post-cursor ISI persisting 10 or more UIs while consuming low-power comparable to just one DT tap. DFE architectures with varying numbers of DT and IIR taps are compared for use in typical wireline channels, and it is found that 2 IIR taps can offer an excellent compromise between power consumption and performance. However, an IIR DFE’s performance degrades significantly as the feedback loop delay increases. Fortunately, adding a single DT tap can eliminate the degradation. The first ever hybrid DFE combining 1 DT and multiple (2) IIR taps is presented equalizing 24dB loss at half the bitrate while consuming 4.1mW at 10Gb/s. A novel edge based adaptation algorithm ii is also presented for DT DFEs which converges faster than previous algorithms while using the same high-speed circuitry and signals required for clock recovery. The edge based algorithm is extended to work for a 1 DT + 1 IIR DFE. The 1 DT + 1 IIR DFE along with integrated clock recovery and adaptation is demonstrated in 28nm FD-SOI CMOS. At 16Gb/s with a 30dB-loss channel, a BER below 10 is measured over a 0.3UI timing window. The novel edge-based algorithm adapts both IIR and discrete-tap equalizer coefficients using the same high-speed circuitry and signals required for clock recovery. The adaptive DFE converges within 5us and is robust in the presence of poorly-conditioned data.
The effect of liquid target on a nonthermal plasma jet—imaging, electric fields, visualization of gas flow and optical emission spectroscopy

The article describes the complex study of the interaction of a helium plasma jet with distilled water and saline. The discharge development, spatial distribution of the excited species, electric field measurement results and the results of the Schlieren imaging are presented. The results of the experiments showed that the plasma–liquid interaction could be prolonged with the proper choice of the gas composition between the jet nozzle and the target. This depends on the gas flow and the target distance. Increased conductivity of the liquid does not affect the discharge properties significantly. An increase of the gas flow enables an extension of the plasma duration on the liquid surface up to 10 µs, but with a moderate electric field strength in the ionization wave. In contrast, there is a significant enhancement of the electric field on the liquid surface, up to 30 kV cm−1 for low flows, but with a shorter time of the overall plasma liquid interaction. Ignition of the plasma jet induces a gas flow modification and may cause turbulences in the gas flow. A significant influence of the plasma jet causing a mixing in the liquid is also recorded and it is found that the plasma jet ignition changes the direction of the liquid circulation.
Synaptic Plasticity Models and Bio-Inspired Unsupervised Deep Learning: A Survey

Recently emerged technologies based on Deep Learning (DL) achieved outstanding results on a variety of tasks in the field of Artificial Intelligence (AI). However, these encounter several challenges related to robustness to adversarial inputs, ecological impact, and the necessity of huge amounts of training data. In response, researchers are focusing more and more interest on biologically grounded mechanisms, which are appealing due to the impressive capabilities exhibited by biological brains. This survey explores a range of these biologically inspired models of synaptic plasticity, their application in DL scenarios, and the connections with models of plasticity in Spiking Neural Networks (SNNs). Overall, Bio-Inspired Deep Learning (BIDL) represents an exciting research direction, aiming at advancing not only our current technologies but also our understanding of intelligence.
Spatiotemporal Patterns Corresponding to Phase Synchronization and Generalized Synchronization States of Thermoacoustic Instability

Thermoacoustic instability in turbulent combustion systems emerges from the complex interplay among the flame, flow, and acoustic subsystems. While the onset of thermoacoustic instability exhibits global order in system dynamics, the characteristics of local interactions between subsystems responsible for this order are not well understood. In this study, we utilize the framework of synchronization to elucidate the spatiotemporal interactions among heat release rate fluctuations in the flame, velocity fluctuations in the flow, and acoustic pressure fluctuations in a turbulent combustor. We examine two forms of thermoacoustic instability, characterized by phase synchronization and generalized synchronization of the acoustic pressure and global heat release rate oscillations. Despite the presence of global synchrony, we uncover a coexistence of frequency synchrony and desynchrony in the local interaction of these oscillations within the reaction field. In regions of frequency-locked oscillations, various phase-locking patterns occur, including phase synchrony and partial phase synchrony. We discover that the local development of small pockets of phase synchrony and strong amplitude correlation between these oscillations is sufficient to trigger global phase synchronization in the system. Furthermore, as the global dynamics approach generalized synchronization, these local regions of synchrony expand throughout the reaction field. Additionally, through coupled analysis of acoustic pressure and local flow velocity fluctuations, we infer that the spatial region of flow-acoustic synchrony plays a significant role in governing thermoacoustic instabilities. Our findings imply that, in turbulent combustors, an intrinsic local balance between order, partial order, and disorder within the coupled subsystems sustains the global order during thermoacoustic instability.
InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer

Artistic style transfer aims to transfer the style of an artwork to a photograph while maintaining its original overall content. Many prior works focus on designing various transfer modules to transfer the style statistics to the content image. Although effective, ignoring the clear disentanglement of the content features and the style features from the first beginning, they have difficulty in balancing between content preservation and style transferring. To tackle this problem, we propose a novel information disentanglement method, named InfoStyler, to capture the minimal sufficient information for both content and style representations from the pre-trained encoding network. InfoStyler formulates the disentanglement representation learning as an information compression problem by eliminating style statistics from the content image and removing the content structure from the style image. Besides, to further facilitate disentanglement learning, a cross-domain Information Bottleneck (IB) learning strategy is proposed by reconstructing the content and style domains. Extensive experiments demonstrate that our InfoStyler can synthesize high-quality stylized images while balancing content structure preservation and style pattern richness.
Non-isothermal axial flow of a rarefied gas between two coaxial cylinders

A stationary problem of heat transfer in a rarefied gas confined between two coaxial cylinders is presented. The two cylinders are maintained at two different constant temperatures, so that a radial temperature gradient is created. The external cylinder is at rest, while the internal one moves in the axial direction with a constant velocity. A flow of the gas in the z -direction, orthogonal to the temperature gradient, is created by the motion of the internal boundary. Instead of the classical Navier-Stokes and Fourier theory, the field equations of extended thermodynamics with 13 moments are used in order to describe this physical problem. It turns out that, although only a radial temperature difference is imposed, the heat flux presents also an axial component. Moreover, some components of the stress tensor do not vanish, even though the axial velocity of the gas depends only on the radial coordinate r . The solution here obtained is compared with the classical one. Furthermore, the dependence of the solution on the boundary velocity is investigated.
Characteristics of nonlinear optical absorption and refraction for KDP and DKDP crystals

High-quality KDP and DKDP crystals with 12%, 70% and 80%-deuterium content were grown by the conventional temperature cooling method. The nonlinear optical characteristics of K(DxH1-x)2PO4 crystals, including nonlinear absorption and refraction, were systematically investigated through the Z-scan method at λ = 532 nm. The results demonstrate the existence of nonlinear absorption and refraction, and a nonlinear refractive index with a positive sign indicates the generation of self-focusing effect. Moreover, the results indicate that nonlinear absorption and refraction were associated with crystal direction and structure, especially the distribution of H2PO4 - or D2PO4 - groups. For K(DxH1-x)2PO4 crystals, a similar rule of β was z > I > II, while n2 (or χR(3)) was z > II ≈I. These results will evoke more attention to KDP and DKDP applications, especially frequency conversion and electro-optic switch.
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech

Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space. Aiming to improve those assumptions, Normalizing Flows and Diffusion Probabilistic Models were recently proposed as alternatives. In this paper, we compare traditional L1/L2-based approaches to diffusion and flow-based approaches for the tasks of prosody and mel-spectrogram prediction for text-to-speech synthesis. We use a prosody model to generate log-f0 and duration features, which are used to condition an acoustic model that generates mel-spectrograms. Experimental results demonstrate that the flow-based model achieves the best performance for spectrogram prediction, improving over equivalent diffusion and L1 models. Meanwhile, both diffusion and flow-based prosody predictors result in significant improvements over a typical L2-trained prosody models.
A multiscale and multicriteria Generative Adversarial Network to synthesize 1-dimensional turbulent fields

This article introduces a new Neural Network stochastic model to generate a 1-dimensional stochastic field with turbulent velocity statistics. Both the model architecture and training procedure ground on the Kolmogorov and Obukhov statistical theories of fully developed turbulence, so guaranteeing descriptions of 1) energy distribution, 2) energy cascade and 3) intermittency across scales in agreement with experimental observations. The model is a Generative Adversarial Network with multiple multiscale optimization criteria. First, we use three physics-based criteria: the variance, skewness and flatness of the increments of the generated field that retrieve respectively the turbulent energy distribution, energy cascade and intermittency across scales. Second, the Generative Adversarial Network criterion, based on reproducing statistical distributions, is used on segments of different length of the generated field. Furthermore, to mimic multiscale decompositions frequently used in turbulence's studies, the model architecture is fully convolutional with kernel sizes varying along the multiple layers of the model. To train our model we use turbulent velocity signals from grid turbulence at Modane wind tunnel.
Cascade of polarized Compton scattering and Breit-Wheeler pair production

Cascaded Compton scattering and Breit-Wheeler (BW) processes play fundamental roles in high-energy astrophysical sources and laser-driven quantum electrodynamics (QED) plasmas. A thorough comprehension of the polarization transfer in these cascaded processes is essential for elucidating the polarization mechanism of high-energy cosmic gamma rays and laser-driven QED plasmas. In this study, we employ analytical cross-sectional calculations and Monte Carlo (MC) numerical simulations to investigate the polarization transfer in the cascade of electron-seeded inverse Compton scattering (ICS) and BW process. Theoretical analysis indicates that the polarization of background photons can effectively transfer to final-state particles in the first-generation cascade due to helicity transfer. Through MC simulations involving polarized background photons and non-polarized seed electrons, we reveal the characteristic polarization curves as a function of particle energy produced by the cascaded processes of ICS and BW pair production. Our results demonstrate that the first-generation photons from ICS exhibit the non-decayed stair-shape polarization curves, in contrast to the linearly decayed ones of the first-generation electrons. Interestingly, this polarization curve trend can be reversed in the second-generation cascade, facilitated by the presence of polarized first-generation BW pairs with fluctuant polarization curves. The cascade culminates with the production of second-generation BW pairs, due to diminished energy of second-generation photons below the threshold of BW process. Our findings provide crucial insights into the cascaded processes of Compton scattering and BW process, significantly contributing to the understanding and further exploration of laser-driven QED plasma creation in laboratory settings and high-energy astrophysics research.
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction

Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-consuming. As a result, models developed and validated on small-scale datasets often suffer from overfitting and do not generalize well to unseen scenarios. At the same time, large amounts of imprecise (silver-standard) labeled data, annotated by approximate methods with the help of modern wearables and in the absence of ground truth validation, are starting to emerge. However, due to measurement differences, this data displays significant label distribution shifts, which motivates the use of domain adaptation. To this end, we introduce UDAMA, a method with two key components: Unsupervised Domain Adaptation and Multidiscriminator Adversarial Training, where we pre-train on the silver-standard data and employ adversarial adaptation with the gold-standard data along with two domain discriminators. In particular, we showcase the practical potential of UDAMA by applying it to Cardio-respiratory fitness (CRF) prediction. CRF is a crucial determinant of metabolic disease and mortality, and it presents labels with various levels of noise (goldand silver-standard), making it challenging to establish an accurate prediction model. Our results show promising performance by alleviating distribution shifts in various label shift settings. Additionally, by using data from two free-living cohort studies (Fenland and BBVS), we show that UDAMA consistently outperforms up to 12% compared to competitive transfer learning and state-of-the-art domain adaptation models, paving the way for leveraging noisy labeled data to improve fitness estimation at scale.
Bose-Einstein Condensation and Black Holes in Dark Matter and Dark Energy

The main aim of this study is to reveal curved space and particle physics effects on the formation of Bose-Einstein condensate (BEC) scalar fields in cosmology and around a black hole. Cosmological scalar fields for dark energy and dark matter may be considered as a result of Bose-Einstein condensation. In this regard, our main attention will be devoted to BECs in curved space. By considering the dynamics of a BEC scalar field at a microscopic level, we first study the initial phase of the formation of condensation in cosmology. To this end, we initially introduce an effective Minkowski space formulation that enables considering only the effect of particle physics processes, excluding the effect of gravitational particle production and enabling us to see cosmological evolution more easily. Then, by using this formulation, we study a model with a trilinear coupling that induces the processes. After considering the phase evolution of the produced particles, we find that they evolve towards the formation of a BEC if some specific conditions are satisfied. In principle, the effective Minkowski space formulation introduced in this study can be applied to particle physics processes in any sufficiently smooth spacetime. In this regard, we also analyse if a BEC scalar field is realized in the spacetime around a Reissner - Nordstr\"om black hole. We find that the produced particles of particle physics processes are localized in a region around the black hole and have a tendency toward condensation if the emerged particles are much heavier than ingoing particles. We also find that such a configuration is phenomenologically viable only if the scalars and the black hole have dark electric charges. Finally, we consider gravitational collapse around Schwarzschild black holes and form a first step towards a study in future about the effects of gravitational collapse on Bose-Einstein condensation.