Miracle-dz/UniFAR-FTUs huggingface dataset:
Optically controllable localization of exciton polariton condensates in a potential lattice

Exciton polaritons are inherently non-Hermitian systems with adjustable gain and loss coefficients. In this work we show that exciton polariton condensates can be selectively localized in an optically-induced lattice with equal potential depth by judiciously controlling a second focused pump with a very small size. Specifically, the localized polariton condensate can be tuned among different potential traps by adjusting the relative distance between the small pump spot and the potential lattice. The adjustment of the excitation position of the smaller pump and its combination with the bigger pump for the potential creation induce a position-dependent loss distribution across the system. The localization of the exciton polariton condensate and its control are independent of the orientation of the potential lattice, thus, even in slightly disordered system, one can selectively excite such localized polariton condensates. Our results illuminate a path to manipulate the non-Hermitian bosonic condensates in integrated photonic chips.
Simultaneous multicolour transit photometry of hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b

Accurate physical parameters of exoplanet systems are essential for further exploration of planetary internal structure, atmospheres, and formation history. We aim to use simultaneous multicolour transit photometry to improve the estimation of transit parameters, to search for transit timing variations (TTVs), and to establish which of our targets should be prioritised for follow-up transmission spectroscopy. We performed time series photometric observations of 12 transits for the hot Jupiters HAT-P-19b, HAT-P-51b, HAT-P-55b, and HAT-P-65b using the simultaneous four-colour camera MuSCAT2 on the Telescopio Carlos S\'anchez. We collected 56 additional transit light curves from TESS photometry. To derive transit parameters, we modelled the MuSCAT2 light curves with Gaussian processes to account for correlated noise. To derive physical parameters, we performed EXOFASTv2 global fits to the available transit and radial velocity data sets, together with the Gaia DR3 parallax, isochrones, and spectral energy distributions. To assess the potential for atmospheric characterisation, we compared the multicolour transit depths with a flat line and a clear atmosphere model. We consistently refined the transit and physical parameters. We improved the orbital period and ephemeris estimates, and found no evidence for TTVs or orbital decay. The MuSCAT2 broadband transmission spectra of HAT-P-19b and HAT-P-65b are consistent with previously published low-resolution transmission spectra. We also found that, except for HAT-P-65b, the assumption of a planetary atmosphere can improve the fit to the MuSCAT2 data. In particular, we identified HAT-P-55b as a priority target among these four planets for further atmospheric studies using transmission spectroscopy.
Medical follow-up optimization: A Monte-Carlo planning strategy

Designing patient-specific follow-up strategy is a crucial step towards personalized medicine in cancer. Tools to help doctors deciding on treatment allocation together with next visit date, based on patient preferences and medical observations, would be particularly beneficial. Such tools should be based on realistic models of disease progress under the impact of medical treatments, involve the design of (multi-)objective functions that a treatment strategy should optimize along the patient's medical journey, and include efficient resolution algorithms to optimize personalized follow-up by taking the patient's history and preferences into account. We propose to model cancer evolution with a Piecewise Deterministic Markov Process where patients alternate between remission and relapse phases with disease-specific tumor evolution. This model is controlled via the online optimization of a long-term cost function accounting for treatment side-effects, hospital visits burden and disease impact on the quality of life. Optimization is based on noisy measurements of blood markers at visit dates. We leverage the Partially-Observed Monte-Carlo Planning algorithm to solve this continuous-time, continuous-state problem, taking advantage of the nearly-deterministic nature of cancer evolution. We show that this approximate solution approach of the exact model performs better than the counterpart exact resolution of the discrete model, while allowing for more versatility in the cost function model.
Impact of perinatal maternal docosahexaenoic acid-containing phospholipid synthesis on offspring growth and neurological symptoms

Mothers provide essential nutrients, including docosahexaenoic acid (DHA), an omega-3 fatty acid, during the perinatal period. DHA deficiency in perinatal mothers is linked to developmental abnormalities, especially in the central nervous system of the offspring; however, its specific impact on distinct events in fetal and neonatal brain development and prospective brain functions remains incompletely understood. We demonstrated using mice lacking Agpat3, a gene encoding the enzyme that synthesizes DHA-containing phospholipids (DHA-PLs), that maternal DHA-PL synthesis significantly contributes to the maternal– offspring DHA supply during the fetal period but not in infancy. Selective modulation of DHA-PL levels during fetal and postnatal periods in Agpat3-knockout mice showed that fetal stage-specific insufficiency in maternal DHA-PL supply potentially influences the neuropsychiatric phenotype in adult mice without affecting postnatal tissue DHA-PL levels, weight gain, and brain expansion. Collectively, enhancing maternal DHA-PL synthesis during pregnancy may help prevent prospective neuropsychiatric abnormalities in the offspring.
Turmeric shortens lifespan in houseflies

Climate change poses a significant threat to food security and global public health with the increasing likelihood of insect pest outbreaks. Alternative ways to control insect populations, preferably using environmental-friendly compounds, are needed. Turmeric has been suggested as a natural insecticide with toxicity properties in some insect groups. However, empirical evidence of the effects turmeric – and their interaction with other ecological factors such as diet – on insect survival has been limited. Here, we tested the effects of turmeric and its interactions with diets differing in protein source in the common housefly, Musca domestica. We found that turmeric shortened lifespan independent of diet and sex. Females in turmeric diets were heavier at death, which was likely driven by a combination of relatively lower rates of body mass loss during their lifetime and a higher percentage of water content at death. Each sex responded differently to the protein source in the diet, and the magnitude of the difference in lifespan between sexes were greatest in diets in which protein source was hydrolysed yeast; individuals from both sexes lived longest in sucrose-milk diets and shortest in diets with hydrolysed yeast. There was no evidence of an interaction between turmeric and diet, suggesting that the toxicity effects are independent of protein source in the diet. Given the seemingly opposing effects of turmeric in insects and mammals being uncovered in the literature, our findings provide further evidence in support of turmeric as a potential natural insecticide.
Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex

Recently, there has been a surge in the popularity of pre trained large language models (LLMs) (such as GPT-4), sweeping across the entire Natural Language Processing (NLP) and Computer Vision (CV) communities. These LLMs have demonstrated advanced multi-modal understanding capabilities and showcased strong performance across various benchmarks. The LLM has started to embody traits of artificial general intelligence, which holds vital guidance for enhancing brain-like characteristics within visual encoding models. Hence, This paper proposes a new multi-modal training paradigm, aligning with LLM, for encoding fMRI activity in visual cortex. Based on this paradigm, we trained an encoding model in fMRI data named the LLM-Visual Encoding Model (LLM-VEM). Specifically, we utilize LLM (miniGPT4) to generate descriptive text for all stimulus images, forming a high-quality textual description set. Moreover, we use the pre-trained text encoder (CLIP) to process these detailed descriptions, obtaining the text embedding features. Next, we use the contrast loss function to minimize the distance between the image embedding features and the text embedding features to complete the alignment operation of the stimulus image and text information. With the assistance of the pre-trained LLM, this alignment process facilitates better learning of the visual encoding model, resulting in higher precision. The final experimental results indicate that our training paradigm has significantly aided in enhancing the performance of the visual encoding model.
Volatility Models in Practice: Rough, Path-Dependent or Markovian?

An extensive empirical study of the class of Volterra Bergomi models using SPX options data between 2011 and 2022 reveals the following fact-check on two fundamental claims echoed in the rough volatility literature: Do rough volatility models with Hurst index $H \in (0,1/2)$ really capture well SPX implied volatility surface with very few parameters? No, rough volatility models are inconsistent with the global shape of SPX smiles. They suffer from severe structural limitations imposed by the roughness component, with the Hurst parameter $H \in (0,1/2)$ controlling the smile in a poor way. In particular, the SPX at-the-money skew is incompatible with the power-law shape generated by rough volatility models. The skew of rough volatility models increases too fast on the short end, and decays too slow on the longer end where"negative"$H$ is sometimes needed. Do rough volatility models really outperform consistently their classical Markovian counterparts? No, for short maturities they underperform their one-factor Markovian counterpart with the same number of parameters. For longer maturities, they do not systematically outperform the one-factor model and significantly underperform when compared to an under-parametrized two-factor Markovian model with only one additional calibratable parameter. On the positive side: our study identifies a (non-rough) path-dependent Bergomi model and an under-parametrized two-factor Markovian Bergomi model that consistently outperform their rough counterpart in capturing SPX smiles between one week and three years with only 3 to 4 calibratable parameters. \end{abstract}
The control of overt and covert attention across two nodes of the attention-control network

Attention is a central cognitive capability whose focus is thought to be directed by a spatial map coding behavioral priority. Here we tested the three defining properties of priority map theory with electrophysiological recordings from two attentional control areas. Both areas, lateral intraparietal area LIP and dorsal posterior inferotemporal cortex PITd, selected behaviorally relevant locations even in the absence of visual stimuli (cognitive sustainment principle). Second, priority signals are thought to arise from the summation of multiple spatial signals (superposition principle). LIP approximated linear summation for visual stimuli, spatial attention, and eye movements, while PITd did not. Third, the same priority signal should guide different behaviors (agnosticity principle). LIP, instead, used separable processing channels for representing attentional focus and eye position, while PITd coded attentional focus only. Thus primate attentional control circuits implement multiple priority maps, whose functional diversity and dimensionality increase the computational capacity of attentional selection. SIGNIFICANCE STATEMENT A central theory of attention poses that the brain computes a priority map to highlight spatial locations of relevance for behavior. Here we tested the hypothesis and its key predictions about how priority signals are assembled and used, through electrophysiological single-unit recordings from two nodes of the attention control network, localized by functional magnetic resonance imaging. We found both areas to highlight locations even in the absence of a stimulus, and that each assembled spatial signals differently and provided spatial information in different forms. These findings force a revision of how and where spatial attention is controlled in the brain.
A foundation for exact binarized morphological neural networks

Training and running deep neural networks (NNs) often demands a lot of computation and energy-intensive specialized hardware (e.g. GPU, TPU.). One way to reduce the computation and power cost is to use binary weight NNs, but these are hard to train because the sign function has a non-smooth gradient. We present a model based on Mathematical Morphology (MM), which can binarize ConvNets without losing performance under certain conditions, but these conditions may not be easy to satisfy in real-world scenarios. To solve this, we propose two new approximation methods and develop a robust theoretical framework for ConvNets binarization using MM. We propose as well regularization losses to improve the optimization. We empirically show that our model can learn a complex morphological network, and explore its performance on a classification task.
How to optimize neuroscience data utilization and experiment design for advancing primate visual and linguistic brain models?

In recent years, neuroscience has made significant progress in building large-scale artificial neural network (ANN) models of brain activity and behavior. However, there is no consensus on the most efficient ways to collect data and design experiments to develop the next generation of models. This article explores the controversial opinions that have emerged on this topic in the domain of vision and language. Specifically, we address two critical points. First, we weigh the pros and cons of using qualitative insights from empirical results versus raw experimental data to train models. Second, we consider model-free (intuition-based) versus model-based approaches for data collection, specifically experimental design and stimulus selection, for optimal model development. Finally, we consider the challenges of developing a synergistic approach to experimental design and model building, including encouraging data and model sharing and the implications of iterative additions to existing models. The goal of the paper is to discuss decision points and propose directions for both experimenters and model developers in the quest to understand the brain.
Attention and Autoencoder Hybrid Model for Unsupervised Online Anomaly Detection

This paper introduces a hybrid attention and autoencoder (AE) model for unsupervised online anomaly detection in time series. The autoencoder captures local structural patterns in short embeddings, while the attention model learns long-term features, facilitating parallel computing with positional encoding. Unique in its approach, our proposed hybrid model combines attention and autoencoder for the first time in time series anomaly detection. It employs an attention-based mechanism, akin to the deep transformer model, with key architectural modifications for predicting the next time step window in the autoencoder's latent space. The model utilizes a threshold from the validation dataset for anomaly detection and introduces an alternative method based on analyzing the first statistical moment of error, improving accuracy without dependence on a validation dataset. Evaluation on diverse real-world benchmark datasets and comparing with other well-established models, confirms the effectiveness of our proposed model in anomaly detection.
Auditory and visual gratings elicit distinct gamma responses

Sensory stimulation is often accompanied by fluctuations at high frequencies (>30Hz) in brain signals. These could be “narrowband” oscillations in the gamma band (30-70 Hz) or non-oscillatory “broadband” high-gamma (70-150 Hz) activity. Narrowband gamma oscillations, which are induced by presenting some visual stimuli such as gratings and have been shown to weaken with healthy aging and the onset of Alzheimer’s Disease, hold promise as potential biomarkers. However, since delivering visual stimuli is cumbersome as it requires head stabilization for eye tracking, an equivalent auditory paradigm could be useful. Although simple auditory stimuli have been shown to produce high-gamma activity, whether specific auditory stimuli can also produce narrowband gamma oscillations is unknown. We tested whether auditory ripple stimuli, which are considered an analogue to visual gratings, could elicit narrowband oscillations in auditory areas. We recorded 64-channel EEG from male and female (18 each) subjects while they either passively fixated on the monitor while viewing static visual gratings, or listened to stationary and moving ripples, played using loudspeakers, with their eyes open or closed. We found that while visual gratings induced narrowband gamma oscillations with suppression in the alpha band (8-12Hz), auditory ripples did not produce narrowband gamma but instead elicited very strong broadband high-gamma response and suppression in the beta band (14-26Hz). Even though we used equivalent stimuli in both modalities, our findings indicate that the underlying neuronal circuitry may not share ubiquitous strategies for stimulus processing. Significance statement In the visual cortex, gratings can induce robust narrowband gamma oscillations (30-70Hz). These visual stimulus-induced oscillations can further be used as a biomarker for diagnosing neuronal disorders. However, tasks used to elicit these oscillations are challenging for elderly subjects, and therefore, we tested if we could use auditory stimuli instead. We hypothesized that auditory ripple stimuli, which are analogous to visual gratings, may elicit these narrowband oscillations. We found that ripples induce a broadband high-gamma response (70-150Hz) in human EEG, unlike visual gratings that produce robust gamma. Thus, the underlying neural circuitry in the two areas may not be canonical.
Entropy and forecasting complexity of hidden Markov models, matrix product states, and observable operator models

In a series of three papers, Jurgens and Crutchfield recently proposed a supposedly novel method to compute entropies of hidden Markov models (HMMs), discussed in detail its relationship to iterated function systems, and applied it to compute their ``ambiguity rates", a concept supposedly introduced by Claude Shannon. We point out that the basic formalism is not new (it is the well known ``forward algorithm"for HMMs), and that all three papers have also serious other faults.
Interpretation of EarthScope magnetotelluric data for Northwestern United States

In this dissertation, I present the results of large-scale three-dimensional magnetotelluric (MT) inversion, based on the nonlinear conjugate gradient algorithm and the contraction integral equation forward modeling method, applied to data collected in Northwestern United States for part of the EarthScope project. It is shown that the most noteworthy anomalies within the inverse geo-electrical model are resistive structures associated with oceanic lithosphere and cratons, and conductive features associated with mantle upwelling. Density estimations from seismic data analysis show upwelling phenomena in the upper mantle where Yellowstone is the present-day surface expression of the deep heat source. Comparison of MT results to approximately 400 km depth have reasonable correlation with P-wave and S-wave velocity models obtained from seismic tomography. The MT inversion demonstrates that strong resistive zones line up along the northwest coast correlating to recent seismic interpretations of old oceanic slabs at 100 km depth believed to be remnants of the Farrallon oceanic plate. This dissertation shows that access to multiple physical properties within the subsurface increases our ability to understand complexities in geological interpretations resulting from the interplay of transforming quanta at differing pressure and temperature regimes with depth. Therefore, EarthScope is proving true to the founding philosophy that the bold, new experiment will fundamentally change our view of the planet. In loving memory of Kyle & Eric Tietze. "Now he has departed from this strange world a little ahead of me. That means nothing. People like us, who believe in physics, know that the distinction between past, present, and future is only a stubbornly persistent illusion. " ~Albert Einstein
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring

We present a joint learning scheme of video super-resolution and deblurring, called VSRDB, to restore clean high-resolution (HR) videos from blurry low-resolution (LR) ones. This joint restoration problem has drawn much less attention compared to single restoration problems. In this paper, we propose a novel flow-guided dynamic filtering (FGDF) and iterative feature refinement with multi-attention (FRMA), which constitutes our VSRDB framework, denoted as FMA-Net. Specifically, our proposed FGDF enables precise estimation of both spatio-temporally-variant degradation and restoration kernels that are aware of motion trajectories through sophisticated motion representation learning. Compared to conventional dynamic filtering, the FGDF enables the FMA-Net to effectively handle large motions into the VSRDB. Additionally, the stacked FRMA blocks trained with our novel temporal anchor (TA) loss, which temporally anchors and sharpens features, refine features in a course-to-fine manner through iterative updates. Extensive experiments demonstrate the superiority of the proposed FMA-Net over state-of-the-art methods in terms of both quantitative and qualitative quality. Codes and pre-trained models are available at: https://kaist-viclab.github. io/fmanet-site
Laminar and turbulence forced heat transfer convection correlations inside tubes. A review

This work proposes an extensive review of laminar and turbulent forced convective heat transfer correlations inside tubes by analyzing both experimental and computational research. Convective heat transfer is influenced by fluid turbulence and boundary layers, with geometry significantly impacting flow conditions. Nusselt number correlations quantifying the heat transfer coefficient are vital for various applications. Previous reviews are summarized regarding nanofluid heat transfer modeling approaches in circular tubes. Additionally, comparisons of tube shapes like circular, elliptical and flat geometries are needed. Recent database searches indicate more experimental than numerical studies have been conducted, with greater focus on turbulent flows. This review systematically evaluates laminar and turbulent forced heat transfer correlations reported in the open literature, discussing both older established work as well as newer findings to provide a holistic perspective. The goal is to better understand convective transportation dependencies for optimized thermal design and performance improvement.