Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinearity-Enhanced Continuous Microwave Detection Based on Stochastic Resonance

In practical sensing tasks, noise is usually regarded as an obstruction to better performance and will degrade the sensitivity. Fortunately, \textit{stochastic resonance} (SR), a counterintuitive concept, can utilize noise to greatly enhance the detected signal amplitude. Although fundamentally important as a mechanism of weak signal amplification, and has been continually explored in geological, biological, and physical science, both theoretically and experimentally, SR has yet to be demonstrated in realistic sensing tasks. Here we develop a novel SR-based nonlinear sensor using a thermal ensemble of interacting Rydberg atoms. With the assistance of stochastic noise (either inherently in the system or added externally) and strong nonlinearity in the Rydberg ensembles, the signal encoded in a weak MW field is greatly enhanced (over 25 dB). Moreover, we show that the SR-based atomic sensor can achieve a better sensitivity in our system, which is over 6.6 dB compared to a heterodye atomic sensor. Our results show potential advantage of SR-based MW sensors in commercial or defense-related applications.
Episodic-free Task Selection for Few-shot Learning

Episodic training is a mainstream training strategy for few-shot learning. In few-shot scenarios, however, this strategy is often inferior to some non-episodic training strategy, e. g. , Neighbourhood Component Analysis (NCA), which challenges the principle that training conditions must match testing conditions. Thus, a question is naturally asked: How to search for episodic-free tasks for better few-shot learning? In this work, we propose a novel meta-training framework beyond episodic training. In this framework, episodic tasks are not used directly for training, but for evaluating the effectiveness of some selected episodic-free tasks from a task set that are performed for training the meta-learners. The selection criterion is designed with the affinity, which measures the degree to which loss decreases when executing the target tasks after training with the selected tasks. In experiments, the training task set contains some promising types, e. g. , contrastive learning and classification, and the target few-shot tasks are achieved with the nearest centroid classifiers on the miniImageNet, tiered-ImageNet and CIFAR-FS datasets. The experimental results demonstrate the effectiveness of our approach.
Information-Theoretic Thresholds for Planted Dense Cycles

We study a random graph model for small-world networks which are ubiquitous in social and biological sciences. In this model, a dense cycle of expected bandwidth $n \tau$, representing the hidden one-dimensional geometry of vertices, is planted in an ambient random graph on $n$ vertices. For both detection and recovery of the planted dense cycle, we characterize the information-theoretic thresholds in terms of $n$, $\tau$, and an edge-wise signal-to-noise ratio $\lambda$. In particular, the information-theoretic thresholds differ from the computational thresholds established in a recent work for low-degree polynomial algorithms, thereby justifying the existence of statistical-to-computational gaps for this problem.
A Diffusion-Based Approach for Simulating Forward-in-Time State-Dependent Speciation and Extinction Dynamics

We establish a general framework using a diffusion approximation to simulate forward-in-time state counts or frequencies for cladogenetic state-dependent speciation-extinction (ClaSSE) models. We apply the framework to various two- and three-region geographic-state speciation-extinction (GeoSSE) models. We show that the species range state dynamics simulated under tree-based and diffusion-based processes are comparable. We derive a method to infer rate parameters that are compatible with given observed stationary state frequencies and obtain an analytical result to compute stationary state frequencies for a given set of rate parameters. We also describe a procedure to find the time to reach the stationary frequencies of a ClaSSE model using our diffusion-based approach, which we demonstrate using a worked example for a two-region GeoSSE model. Finally, we discuss how the diffusion framework can be applied to formalize relationships between evolutionary patterns and processes under state-dependent diversification scenarios.
Actor Identification in Discourse: A Challenge for LLMs?

The identification of political actors who put forward claims in public debate is a crucial step in the construction of discourse networks, which are helpful to analyze societal debates. Actor identification is, however, rather challenging: Often, the locally mentioned speaker of a claim is only a pronoun (“He proposed that [claim]”), so recovering the canonical actor name requires discourse understanding. We compare a traditional pipeline of dedicated NLP components (similar to those applied to the related task of coreference) with a LLM, which appears a good match for this generation task. Evaluating on a corpus of German actors in newspaper reports, we find surprisingly that the LLM performs worse. Further analysis reveals that the LLM is very good at identifying the right reference, but struggles to generate the correct canonical form. This points to an underlying issue in LLMs with controlling generated output. Indeed, a hybrid model combining the LLM with a classifier to normalize its output substantially outperforms both initial models.
GPT4Battery: An LLM-driven Framework for Adaptive State of Health Estimation of Raw Li-ion Batteries

State of health (SOH) is a crucial indicator for assessing the degradation level of batteries that cannot be measured directly but requires estimation. Accurate SOH estimation enhances detection, control, and feedback for Li-ion batteries, allowing for safe and efficient energy management and guiding the development of new-generation batteries. Despite the significant progress in data-driven SOH estimation, the time and resource-consuming degradation experiments for generating lifelong training data pose a challenge in establishing one large model capable of handling diverse types of Li-ion batteries, e.g., cross-chemistry, cross-manufacturer, and cross-capacity. Hence, this paper utilizes the strong generalization capability of large language model (LLM) to proposes a novel framework for adaptable SOH estimation across diverse batteries. To match the real scenario where unlabeled data sequentially arrives in use with distribution shifts, the proposed model is modified by a test-time training technique to ensure estimation accuracy even at the battery's end of life. The validation results demonstrate that the proposed framework achieves state-of-the-art accuracy on four widely recognized datasets collected from 62 batteries. Furthermore, we analyze the theoretical challenges of cross-battery estimation and provide a quantitative explanation of the effectiveness of our method.
PAP-REC: Personalized Automatic Prompt for Recommendation Language Model

Recently emerged prompt-based Recommendation Language Models (RLM) can solve multiple recommendation tasks uniformly. The RLMs make full use of the inherited knowledge learned from the abundant pre-training data to solve the downstream recommendation tasks by prompts, without introducing additional parameters or network training. However, handcrafted prompts require significant expertise and human effort since slightly rewriting prompts may cause massive performance changes. In this paper, we propose PAP-REC, a framework to generate the Personalized Automatic Prompt for RECommendation language models to mitigate the inefficiency and ineffectiveness problems derived from manually designed prompts. Specifically, personalized automatic prompts allow different users to have different prompt tokens for the same task, automatically generated using a gradient-based method. One challenge for personalized automatic prompt generation for recommendation language models is the extremely large search space, leading to a long convergence time. To effectively and efficiently address the problem, we develop surrogate metrics and leverage an alternative updating schedule for prompting recommendation language models. Experimental results show that our PAP-REC framework manages to generate personalized prompts, and the automatically generated prompts outperform manually constructed prompts and also outperform various baseline recommendation models. The source code of the work is available at https://github.com/rutgerswiselab/PAP-REC.
Investigation on the impact of solar flares on the Martian atmospheric emissions in the dayside near-terminator region: Case Studies

Solar transient events like flares can cause sudden changes in planetary plasma and neutral environment. Here, we present an investigation of the variability of the Martian atmospheric emissions viz. OI 130.4 nm, 135.6 nm, CO2+ ultraviolet doublet (UVD), and CO Cameron band (CB) in the less explored dayside near-terminator region during solar flare events. The two X8.2 and M6 class flares during September 2017 on Mars have been selected from existing catalogs. Using data from the imaging ultraviolet spectrograph (IUVS) aboard the MAVEN spacecraft, we examined limb radiance profiles. We observed a significant increase in radiance for major emissions around the peak with a more pronounced impact below the peak during flares compared to quiet time. During solar flares, for 130.4 nm and 135.6 nm emission, the maximum deviation in radiance beneath peak approaches to ~63% and ~123%, respectively. Whereas, for CO2+ UVD and CO CB, it is ~64% and ~50%, respectively. Additionally, we have presented an average scenario of dayside near-terminator (SZA~70-90{\deg}) and observed a notable trend of higher percentage deviation for atomic emissions compared to molecular emissions during flares. Further, our analysis depicts a higher percentage deviation during X8.2 compared to M6 class flare. This study underscores that during flares, higher photoelectron impact and irradiance flux drive the production processes, leading to enhanced emissions. The case studies emphasize, for the first time, the significant influence of flares on the Martian dayside near-terminator region, advancing our deeper understanding the impact of varying solar flare intensities to the planetary atmosphere.
Investigating the magnetism of Ni from a momentum space perspective

For more than three decades, clear discrepancies have existed between spin densities in momentum space revealed by Magnetic Compton scattering experiments and theoretical calculations based on density functional theory (DFT). Here by making a wide comparison between different theoretical methods, including DFT, DFT combined with dynamical mean field theory, and Hedin's $GW$ approximation, we discover how the magnetic Compton profiles of Ni can be predicted remarkably well. We find that the essential ingredients missing in DFT are (i) local spin fluctuations and (ii) a non-local treatment of electron correlations.
Detecting Brain Tumors through Multimodal Neural Networks

Tumors can manifest in various forms and in different areas of the human body. Brain tumors are specifically hard to diagnose and treat because of the complexity of the organ in which they develop. Detecting them in time can lower the chances of death and facilitate the therapy process for patients. The use of Artificial Intelligence (AI) and, more specifically, deep learning, has the potential to significantly reduce costs in terms of time and resources for the discovery and identification of tumors from images obtained through imaging techniques. This research work aims to assess the performance of a multimodal model for the classification of Magnetic Resonance Imaging (MRI) scans processed as grayscale images. The results are promising, and in line with similar works, as the model reaches an accuracy of around 98\%. We also highlight the need for explainability and transparency to ensure human control and safety.
Unveiling nonequilibrium from multifilar events

Closely related to the laws of thermodynamics, the detection and quantification of disequilibria are crucial in unraveling the complexities of nature, particularly those beneath observable layers. Theoretical developments in nonequilibrium thermodynamics employ coarse-graining methods to consider a diversity of partial information scenarios that mimic experimental limitations, allowing the inference of properties such as the entropy production rate. A ubiquitous but rather unexplored scenario involves observing events that can possibly arise from many transitions in the underlying Markov process--which we dub $\textit{multifilar events}$--as in the cases of exchanges measured at particle reservoirs, hidden Markov models, mixed chemical and mechanical transformations in biological function, composite systems, and more. We relax one of the main assumptions in a previously developed framework, based on first-passage problems, to assess the non-Markovian statistics of mutifilar events. By using the asymmetry of event distributions and their waiting-times, we put forward model-free tools to detect nonequilibrium behavior and estimate entropy production, while discussing their suitability for different classes of systems and regimes where they provide no new information, evidence of nonequilibrium, a lower bound for entropy production, or even its exact value. The results are illustrated in reference models through analytics and numerics.
Anomalous Shubnikov-de Haas effect and observation of the Bloch-Gr\"uneisen temperature in the Dirac semimetal ZrTe5

Appearance of quantum oscillations (QO) in both thermodynamic and transport properties of metals at low temperatures is the most striking experimental consequence of the existence of a Fermi surface (FS). The frequency of these oscillations and the temperature dependence of their amplitude provides essential information about the FS topology and fermionic quasiparticle properties. Here, we report the observation of an anomalous suppression of the QO amplitude seen in resistivity (Shubnikov de-Haas effect) at sub-kelvin temperatures in ZrTe5 samples with a single small FS sheet comprising less than 5% of the first Brillouin zone. By comparing these results with measurements of the magneto-acoustic QO and the recovery of the usual Lifshitz-Kosevich behavior of the Shubnikov de-Haas (SdH) effect in ZrTe$_5$ samples with a multi-sheet FS, we show that the suppression of the SdH effect originates from a decoupling of the electron liquid from the lattice. On crossing the so-called Bloch-Gr\"uneisen temperature, T$_BG$, electron-phonon scattering becomes strongly suppressed and in the absence of Umklapp scattering the electronic liquid regains Galilean invariance. In addition, we show, using a combination of zero-field electrical conductivity and ultrasonic-absorption measurements, that entering this regime leads to an abrupt increase of electronic viscosity.
Grounded language acquisition through the eyes and ears of a single child

Starting around 6 to 9 months of age, children begin acquiring their first words, linking spoken words to their visual counterparts. How much of this knowledge is learnable from sensory input with relatively generic learning mechanisms, and how much requires stronger inductive biases? Using longitudinal head-mounted camera recordings from one child aged 6 to 25 months, we trained a relatively generic neural network on 61 hours of correlated visual-linguistic data streams, learning feature-based representations and cross-modal associations. Our model acquires many word-referent mappings present in the child’s everyday experience, enables zero-shot generalization to new visual referents, and aligns its visual and linguistic conceptual systems. These results show how critical aspects of grounded word meaning are learnable through joint representation and associative learning from one child’s input. Editor’s summary How do young children learn to associate new words with specific objects or visually represented concepts? This hotly debated question in early language acquisition has been traditionally examined in laboratories, limiting generalizability to real-world settings. Vong et al. investigated the question in an unprecedented, longitudinal manner using head-mounted video recordings from a single child’s first-person experiences in naturalistic settings. By applying machine learning, they introduced the Child’s View for Contrastive Learning (CVCL) model, pairing video frames that co-occurred with uttered words, and embedded the images and words in shared representational spaces. CVCL represents sets of visually similar things from one concept (e.g., puzzles) through distinct subclusters (animal versus alphabet puzzles). It combines associative and representation learning that fills gaps in language acquisition research and theories. —Ekeoma Uzogara Machine learning advances research into early language acquisition in children.
Adaptive, sample-specific parameter selection for more accurate transcript assembly

Motivation Transcript assemblers are tools to reconstruct expressed transcripts from RNA-seq data. These tools have a large number of tunable parameters, and accurate transcript assembly requires setting them suitably. Because of the heterogeneity of different RNA-seq samples, a single default setting or a small fixed set of parameter candidates can only support the good performance of transcript assembly on average, but are often suboptimal for many individual samples. Manually tuning parameters for each sample is extremely time consuming and requires specialized experience. Therefore, developing an automated system that can advise good parameter settings for individual samples becomes an important problem. Results Using Bayesian optimization and contrastive learning, we develop a new automated parameter advising system for transcript assembly that can generate sets of sample-specific parameter candidates. Our framework achieves efficient sample-specific parameter advising by learning parameter knowledge from a large representative set of existing RNA-seq samples and transferring the knowledge to unseen samples. We use Scallop and StringTie, two well-known transcript assemblers, to test our framework on two collections of RNA-seq samples. Results show that our new parameter advising system significantly outperforms the previous advising method in each dataset and each transcript assembler. The source code to reproduce the results from this study can be found at https://github.com/Kingsford-Group/autoparadvisor.
Revealing brain network dynamics during the emotional state of suspense using topological data analysis

Suspense is an affective state ubiquitous in human life, from art to quotidian events. However, little is known about the behavior of large-scale brain networks during suspenseful experiences. To address this question, we examined the continuous brain responses of participants watching a suspenseful movie, along with reported levels of suspense from an independent set of viewers. We employ sliding window analysis and Pearson correlation to measure functional connectivity states over time. Then, we use Mapper, a topological data analysis tool, to obtain a graphical representation that captures the dynamical transitions of the brain across states; this representation enables the anchoring of the topological characteristics of the combinatorial object with the measured suspense. Our analysis revealed changes in functional connectivity within and between the salience, fronto-parietal, and default networks associated with suspense. In particular, the functional connectivity between the salience and fronto-parietal networks increased with the level of suspense. In contrast, the connections of both networks with the default network decreased. Together, our findings reveal specific dynamical changes in functional connectivity at the network level associated with variation in suspense, and suggest topological data analysis as a potentially powerful tool for studying dynamic brain networks.
Adapting and optimizing GCaMP8f for use in Caenorhabditis elegans

Improved genetically-encoded calcium indicators (GECIs) are essential for capturing intracellular dynamics of both muscle and neurons. A novel set of GECIs with ultra-fast kinetics and high sensitivity was recently reported by Zhang et al. (Nature, 2023). While these indicators, called jGCaMP8, were demonstrated to work in Drosophila and mice, data for Caenorhabditis elegans was not reported. Here, we present an optimized plasmid for C. elegans and use this to generate several strains expressing GCaMP8f. Utilizing the myo-2 promoter, we compare pharyngeal muscle activity measured with GCaMP7f and GCaMP8f and find that GCaMP8f is brighter, shows faster kinetics and is less disruptive to the intrinsic contraction dynamics of the pharynx. Additionally, we validate its application for detecting neuronal activity in touch receptor neurons which reveals robust calcium transients at 25 ms time resolution. As such, we establish GCaMP8f as a potent tool for C. elegans research which is capable of extracting fast calcium dynamics at very low magnifications across multiple cell types.
Couples' socioeconomic resources and completed fertility in Poland

Recently, a growing number of studies have examined the impact of educational level and educational enrolment on the childbearing behaviour of both sexes. At the same time, other socioeconomic characteristics, such as educational field or occupation, are usually neglected or, if included, focus only on women. This study aims at analysing how socioeconomic resources of both partners in a couple affect their completed fertility in a relatively gender-conservative country – Poland. As a representative of the postsocialist European countries, Poland is an interesting case study, in which women are often double burdened and the conditions to develop a family are more difficult. Since behavioural drivers could differ between parents and childless couples, the Bayesian Zero-Inflated Poisson model consisting of two states (childlessness and parenthood) is applied. The first-wave Generations and Gender Survey (GGS) data for Poland from 2011 are used. The results confirm that including both partners’ socioeconomic resources in the model increases the ability to characterise couples’ childbearing behaviour. In particular, the occupation of both partners occurs to have a substantial influence on their completed family size.