Miracle-dz/UniFAR-FTUs huggingface dataset:
Sequential Subset Matching for Dataset Distillation

Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in training deep neural networks (DNNs) for reducing data storage and model training costs. The synthetic datasets are expected to capture the essence of the knowledge contained in real-world datasets such that the former yields a similar performance as the latter. Recent advancements in distillation methods have produced notable improvements in generating synthetic datasets. However, current state-of-the-art methods treat the entire synthetic dataset as a unified entity and optimize each synthetic instance equally. This static optimization approach may lead to performance degradation in dataset distillation. Specifically, we argue that static optimization can give rise to a coupling issue within the synthetic data, particularly when a larger amount of synthetic data is being optimized. This coupling issue, in turn, leads to the failure of the distilled dataset to extract the high-level features learned by the deep neural network (DNN) in the latter epochs. In this study, we propose a new dataset distillation strategy called Sequential Subset Matching (SeqMatch), which tackles this problem by adaptively optimizing the synthetic data to encourage sequential acquisition of knowledge during dataset distillation. Our analysis indicates that SeqMatch effectively addresses the coupling issue by sequentially generating the synthetic instances, thereby enhancing its performance significantly. Our proposed SeqMatch outperforms state-of-the-art methods in various datasets, including SVNH, CIFAR-10, CIFAR-100, and Tiny ImageNet. Our code is available at https://github.com/shqii1j/seqmatch.
Sentiment Analysis through LLM Negotiations

A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers the key disadvantage that the single-turn output generated by a single LLM might not deliver the perfect decision, just as humans sometimes need multiple attempts to get things right. This is especially true for the task of sentiment analysis where deep reasoning is required to address the complex linguistic phenomenon (e.g., clause composition, irony, etc) in the input. To address this issue, this paper introduces a multi-LLM negotiation framework for sentiment analysis. The framework consists of a reasoning-infused generator to provide decision along with rationale, a explanation-deriving discriminator to evaluate the credibility of the generator. The generator and the discriminator iterate until a consensus is reached. The proposed framework naturally addressed the aforementioned challenge, as we are able to take the complementary abilities of two LLMs, have them use rationale to persuade each other for correction. Experiments on a wide range of sentiment analysis benchmarks (SST-2, Movie Review, Twitter, yelp, amazon, IMDB) demonstrate the effectiveness of proposed approach: it consistently yields better performances than the ICL baseline across all benchmarks, and even superior performances to supervised baselines on the Twitter and movie review datasets.
Diatom Seasonality and Sedimentation in a Subtropical Alpine Lake (Lugu Hu, Yunnan-Sichuan, Southwest China)

Abstract The diatom succession and sedimentation during 2011 in a subtropical, deep alpine lake (Lugu Hu, Southwest China) were studied. The seasonal patterns in diatom assemblages were related to light, nutrient availability, and thermal regime. Peaks in the concentration of the planktonic diatoms (Asterionella formosa and Cyclotella ocellata) occurred in the metalimnion during autumn when light penetration was greater and silica concentrations were higher. The spring peak of Cyclostephanos dubius appeared to be a result of the combination of a well-mixed water column, higher nutrient concentration, and low light availability. Its sedimentation in late spring suggests that the sinking rates of C. dubius increased rapidly with the onset of stratification. However, the minimal catches of A. formosa and C. ocellata in sediment traps during autumn suggest they were still actively growing with very low sedimentation losses. The catch of benthic Fragilaria species in sediment traps during early spring and autumn reflected their resuspension from shallow water and transfer into pelagic zone as thermal stratification breaks down. The results provide important information for the interpretation of paleolimnological records from the lakes in Southwest China.
Nebular dominated galaxies in the early Universe with top-heavy stellar initial mass functions

The stellar initial mass function (IMF) impacts nearly all observable properties of galaxies, controls the production rate of heavy elements, and governs how much energy is available to regulate galaxy growth. Theoretical work predicts that the high-redshift IMF may be more top-heavy compared to the local Universe, due to higher gas pressures, a higher Cosmic Microwave Background temperature, and lower metallicities. However, direct observational evidence for a top-heavy IMF at high-redshift remains elusive. Here we report the detection of two Lyman-$\alpha$-emitting galaxies at redshift $5.9$ and $7.9$ that show evidence for exceptionally top-heavy IMFs. Our analysis of JWST/NIRSpec data demonstrates that these galaxies exhibit spectra which are completely dominated by the nebular continuum. Alongside a clear Balmer jump, we observe a steep turnover in the ultraviolet continuum. Although this feature can be produced by an extremely thick damped Lyman-$\alpha$ system with holes, we show instead that this turnover is two-photon emission from neutral hydrogen. Two-photon emission can only dominate if the ionizing emissivity is $\gtrsim10\times$ that of a typical star-forming galaxy. While weak He~{\sc~II} emission disfavours ionizing contributions from AGN or X-ray binaries, such radiation fields can be produced in star clusters dominated by low-metallicity stars of $\gtrsim50\ {\rm M_{\odot}}$, where the IMF is $10-30\times$ more top-heavy than typically assumed. Such a top-heavy IMF implies our understanding of star formation in the early Universe and the sources of reionization may need revision.
Attention Based Dual-Branch Complex Feature Fusion Network for Hyperspectral Image Classification

This research work presents a novel dual-branch model for hyperspectral image classification that combines two streams: one for processing standard hyperspectral patches using Real-Valued Neural Network (RVNN) and the other for processing their corresponding Fourier transforms using Complex-Valued Neural Network (CVNN). The proposed model is evaluated on the Pavia University and Salinas datasets. Results show that the proposed model outperforms state-of-the-art methods in terms of overall accuracy, average accuracy, and Kappa. Through the incorporation of Fourier transforms in the second stream, the model is able to extract frequency information, which complements the spatial information extracted by the first stream. The combination of these two streams improves the overall performance of the model. Furthermore, to enhance the model performance, the Squeeze and Excitation (SE) mechanism has been utilized. Experimental evidence show that SE block improves the models overall accuracy by almost 1%. The project can be accessed at https://github.com/mqalkhatib/Real_Complex_Classification
VVV-WIT-12 and Its Fashionable Nebula: A 4 yr Long-period Young Stellar Object with a Light Echo?

We report the serendipitous discovery of VVV-WIT-12, an unusual variable source that seems to induce variability in its surrounding nebula. The source belongs to the rare objects that we call WITs (short for What Is This? ) discovered within the VISTA Variables in the Vía Láctea (VVV) survey. VVV-WIT-12 was discovered during a pilot search for light echoes from distant supernovae in the Milky Way using the near-IR images of the VVV survey. This source has an extremely red spectral energy distribution, consistent with a very reddened (A V ∼ 100 mag) long-period variable star (P ∼ 1525 days). Furthermore, it is enshrouded in a nebula that changes brightness and color with time, apparently in sync with the central source variations. The near-IR light curve and complementary follow-up spectroscopy observations are consistent with a variable young stellar object illuminating its surrounding nebula. In this case the source periodic variation along the cycles produces an unprecedented light echo in the different regions of the nebula.
Swarm Performance Indicators: Metrics for Robustness, Fault Tolerance, Scalability and Adaptability

Swarms have distributed control and so are assumed to inherently have superior robustness, scalability and adaptability compared to centralised multi-agent systems. However, these features have generally only been defined qualitatively and there is a lack of quantitative metrics and experimental measures for the claimed parameters. Swarm Performance Indicators are defined here as Key Performance Indicators for swarm features but can be applied to multi-agent systems with centralised control as well. These swarm features are Robustness, Fault Tolerance, Adaptability and Scalability. Swarm Performance Indicators can be used to highlight the benefits of swarms beyond solely considering task-based performance metrics (e.g. time taken)
FedSN: A Novel Federated Learning Framework over LEO Satellite Networks

Recently, a large number of Low Earth Orbit (LEO) satellites have been launched and deployed successfully in space by commercial companies, such as SpaceX. Due to multimodal sensors equipped by the LEO satellites, they serve not only for communication but also for various machine learning applications, such as space modulation recognition, remote sensing image classification, etc. However, the ground station (GS) may be incapable of downloading such a large volume of raw sensing data for centralized model training due to the limited contact time with LEO satellites (e.g. 5 minutes). Therefore, federated learning (FL) has emerged as the promising solution to address this problem via on-device training. Unfortunately, to enable FL on LEO satellites, we still face three critical challenges that are i) heterogeneous computing and memory capabilities, ii) limited uplink rate, and iii) model staleness. To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites. Specifically, we first present a novel sub-structure scheme to enable heterogeneous local model training considering different computing, memory, and communication constraints on LEO satellites. Additionally, we propose a pseudo-synchronous model aggregation strategy to dynamically schedule model aggregation for compensating model staleness. To further demonstrate the effectiveness of the FedSN, we evaluate it using space modulation recognition and remote sensing image classification tasks by leveraging the data from real-world satellite networks. Extensive experimental results demonstrate that FedSN framework achieves higher accuracy, lower computing, and communication overhead than the state-of-the-art benchmarks and the effectiveness of each components in FedSN.
Multi-EuP: The Multilingual European Parliament Dataset for Analysis of Bias in Information Retrieval

We present Multi-EuP, a new multilingual benchmark dataset, comprising 22K multi-lingual documents collected from the European Parliament, spanning 24 languages. This dataset is designed to investigate fairness in a multilingual information retrieval (IR) context to analyze both language and demographic bias in a ranking context. It boasts an authentic multilingual corpus, featuring topics translated into all 24 languages, as well as cross-lingual relevance judgments. Furthermore, it offers rich demographic information associated with its documents, facilitating the study of demographic bias. We report the effectiveness of Multi-EuP for benchmarking both monolingual and multilingual IR. We also conduct a preliminary experiment on language bias caused by the choice of tokenization strategy.
MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery

Assessing the environmental impact of the mineral extraction industry plays a critical role in understanding and mitigating the ecological consequences of extractive activities. This paper presents MineSegSAT, a model that presents a novel approach to predicting environmentally impacted areas of mineral extraction sites using the SegFormer deep learning segmentation architecture trained on Sentinel-2 data. The data was collected from non-overlapping regions over Western Canada in 2021 containing areas of land that have been environmentally impacted by mining activities that were identified from high-resolution satellite imagery in 2021. The SegFormer architecture, a state-of-the-art semantic segmentation framework, is employed to leverage its advanced spatial understanding capabilities for accurate land cover classification. We investigate the efficacy of loss functions including Dice, Tversky, and Lovasz loss respectively. The trained model was utilized for inference over the test region in the ensuing year to identify potential areas of expansion or contraction over these same periods. The Sentinel-2 data is made available on Amazon Web Services through a collaboration with Earth Daily Analytics which provides corrected and tiled analytics-ready data on the AWS platform. The model and ongoing API to access the data on AWS allow the creation of an automated tool to monitor the extent of disturbed areas surrounding known mining sites to ensure compliance with their environmental impact goals.
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms

Physical adversarial attacks on road signs are continuously exploiting vulnerabilities in modern day autonomous vehicles (AVs) and impeding their ability to correctly classify what type of road sign they encounter. Current models cannot generalize input data well, resulting in overfitting or underfitting. In overfitting, the model memorizes the input data but cannot generalize to new scenarios. In underfitting, the model does not learn enough of the input data to accurately classify these road signs. This paper explores the resilience of autonomous driving systems against three main physical adversarial attacks (tape, graffiti, illumination), specifically targeting object classifiers. Several machine learning models were developed and evaluated on two distinct datasets: road signs (stop signs, speed limit signs, traffic lights, and pedestrian crosswalk signs) and geometric shapes (octagons, circles, squares, and triangles). The study compared algorithm performance under different conditions, including clean and adversarial training and testing on these datasets. To build robustness against attacks, defense techniques like adversarial training and transfer learning were implemented. Results demonstrated transfer learning models played a crucial role in performance by allowing knowledge gained from shape training to improve generalizability of road sign classification, despite the datasets being completely different. The paper suggests future research directions, including human-in-the-loop validation, security analysis, real-world testing, and explainable AI for transparency. This study aims to contribute to improving security and robustness of object classifiers in autonomous vehicles and mitigating adversarial example impacts on driving systems.
Multivariate brain-cognition associations in euthymic bipolar disorder

Background: People with bipolar disorder (BD) tend to show widespread cognitive impairment compared to healthy controls. Impairments in processing speed (PS), attention, and executive function (EF) may represent 'core' impairments that have a role in wider cognitive dysfunction. Cognitive impairments appear to relate to structural brain abnormalities in BD, but whether core deficits are related to particular brain regions is unclear and much of the research on brain-cognition associations is limited by univariate analysis and small samples. Methods: Euthymic BD patients (n=56) and matched healthy controls (n=26) underwent T1-weighted MRI scans and completed neuropsychological tests of PS, attention, and EF. We utilised public datasets to develop a normative model of cortical thickness (n=5,977) to generate robust estimations of cortical abnormalities in patients. Canonical correlation analysis was used to assess multivariate brain-cognition associations in BD, controlling for age, sex, and premorbid IQ. Results: BD showed impairments on tests of PS, attention, and EF, and abnormal cortical thickness in several brain regions compared to healthy controls. Impairments in tests of PS and EF were most strongly associated with cortical thickness in left inferior temporal, right entorhinal, and right temporal pole areas. Conclusion: Impairments in PS, attention, and EF can be observed in euthymic BD and may be related to abnormal cortical thickness in temporal regions. Future research should continue to leverage multivariate methods to examine complex brain-cognition associations in BD. Future research may benefit from exploring covariance between traditional brain structural morphological metrics such as cortical thickness, cortical volume, and surface area.
Schottky Photodetectors with Transparent Conductive Oxides for Photonic Integrated Circuits

Silicon photonics has many attractive features but faces a major issue: inefficient and slow photodetection in the telecom range. New metal-semiconductor Schottky photodetectors based on intraband absorption address this problem, but their efficiency remains low. We suggest that by creating a junction between silicon and a transparent oxide with appropriate doping, which results in a real permittivity close to zero (known as the epsilon near zero or ENZ regime), detection efficiency could increase by more than tenfold. Using Aluminum Zinc Oxide (AZO) as an example, we design an optimized AZO/Si slot photonic waveguide detector that could potentially reach an efficiency of several tens of percent, in contrast to a few percent for a metal/Si Schottky detector. This increase is primarily due to the lower density of states in AZO compared to metal, along with superior coupling efficiency and strong absorption within a 10 nm slot.
Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos

In gradient descent dynamics of neural networks, the top eigenvalue of the Hessian of the loss (sharpness) displays a variety of robust phenomena throughout training. This includes early time regimes where the sharpness may decrease during early periods of training (sharpness reduction), and later time behavior such as progressive sharpening and edge of stability. We demonstrate that a simple $2$-layer linear network (UV model) trained on a single training example exhibits all of the essential sharpness phenomenology observed in real-world scenarios. By analyzing the structure of dynamical fixed points in function space and the vector field of function updates, we uncover the underlying mechanisms behind these sharpness trends. Our analysis reveals (i) the mechanism behind early sharpness reduction and progressive sharpening, (ii) the required conditions for edge of stability, and (iii) a period-doubling route to chaos on the edge of stability manifold as learning rate is increased. Finally, we demonstrate that various predictions from this simplified model generalize to real-world scenarios and discuss its limitations.
A Highly-Compact Direct-Injection Universal Power Flow and Quality Control Circuit

This paper presents a novel direct-injection modular universal power flow and quality control topology exclusively using lower power components. In addition to conventional high-voltage applications, it is particularly attractive for the distribution and secondary grids, e.g., in soft open points, down to low voltage as it can exploit the latest developments in low-voltage high-current semiconductors. In contrast to other concepts that do not interface the grid through transformers, it does not need to convert the entire line power but only the injected or extracted power difference. The proposed power flow and quality (f/q) controller comprises a shunt active front end, together with high-frequency links serving as a power supply for a series floating module per phase. Each of the floating modules is in series with one phase of the line, floating with the electric potential of that particular phase, avoiding any ground connection. Omitting bulky and dynamically limited line transformers of conventional universal power flow controllers, the presented direct-injection f/q controller enables exceptionally small size and volume, high power density, high frequency content, and fast response. In contrast to direct-injection concepts with full back-to-back converters, it only needs to handle a fraction of the power. The circuit combines grid-voltage low-current electronics in the shunt unit and low-voltage high-current modules in the floating series injection units. Simulations and experiments demonstrate and validate the concept.
Ensemble Principal Component Analysis

Efficient representations of data are essential for processing, exploration, and human understanding, and Principal Component Analysis (PCA) is one of the most common dimensionality reduction techniques used for the analysis of large, multivariate datasets today. Two well-known limitations of the method include sensitivity to outliers and noise and no clear methodology for the uncertainty quantification of the principal components or their associated explained variances. Whereas previous work has focused on each of these problems individually, we propose a scalable method called Ensemble PCA (EPCA) that addresses them simultaneously for data which has an inherently low-rank structure. EPCA combines boostrapped PCA with $k$ -means cluster analysis to handle challenges associated with sign-ambiguity and the re-ordering of components in the PCA subsamples. EPCA provides a noise-resistant extension of PCA that lends itself naturally to uncertainty quantification. We test EPCA on data corrupted with white noise, sparse noise, and outliers against both classical PCA and Robust PCA (RPCA) and show that EPCA performs competitively across different noise scenarios, with a clear advantage on datasets containing outliers and orders of magnitude reduction in computational cost compared to RPCA.
Majorana qubit codes that also correct odd-weight errors

The tetron architecture is a promising candidate for topological quantum computation. Each tetron Majorana island has four Majorana zero modes, and possible measurements are constrained to span zero or two Majoranas per tetron. Such measurements are known to be sufficient for correcting so-called"bosonic errors,"which affect an even number of Majoranas per tetron. We demonstrate that such measurements are also sufficient for correcting"fermionic errors,"which affect an odd number of Majoranas per tetron. In contrast, previous proposals for"fermionic error correction"on tetrons introduce more experimental challenges. We show that"fermionic codes"can be derived from traditional"bosonic codes"by inclusion of tetrons in the stabilizer group.