Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

Chain-of-thought reasoning, a cognitive process fundamental to human intelligence, has garnered significant attention in the realm of artificial intelligence and natural language processing. However, there still remains a lack of a comprehensive survey for this arena. To this end, we take the first step and present a thorough survey of this research field carefully and widely. We use X-of-Thought to refer to Chain-of-Thought in a broad sense. In detail, we systematically organize the current research according to the taxonomies of methods, including XoT construction, XoT structure variants, and enhanced XoT. Additionally, we describe XoT with frontier applications, covering planning, tool use, and distillation. Furthermore, we address challenges and discuss some future directions, including faithfulness, multi-modal, and theory. We hope this survey serves as a valuable resource for researchers seeking to innovate within the domain of chain-of-thought reasoning.
The winner’s curse under dependence: repairing empirical Bayes and a comparative study

Motivation The winner’s curse is a form of selection bias that arises when estimates are obtained for a large number of features, but only a subset of most extreme estimates is reported. It occurs in large scale significance testing as well as in rank-based selection, and imperils reproducibility of findings and follow-up study design. Several methods correcting for this selection bias have been proposed, but questions remain on their relative performance and susceptibility to dependence between features since comparative studies are missing. Results We demonstrate how empirical Bayes methods tackling selection bias require a custom density estimator for competitive performance under dependence. Furthermore, we perform a comprehensive simulation study comparing different classes of correction methods for point estimates as well as confidence intervals, while studying the effect of dependence. We find a bootstrap method by Tan et al. [2015] and empirical Bayes methods to perform best overall for correcting the selection bias, although this correction does not always improve the feature ranking. We apply selection bias correction methods to a comparison of the best single-feature prediction model versus the multi-feature model in predicting Brassica napus phenotypes from gene expression data, demonstrating that the superiority of the best single-feature model may be illusory. Availability All R-code used for the analysis is included in the Supplementary information. Contact steven.maere@psb.vib-ugent.be Supplementary information Supplementary data are available at Bioinformatics online.
LAUREATE

The latest developments in wearable sensors have resulted in a wide range of devices available to consumers, allowing users to monitor and improve their physical activity, sleep patterns, cognitive load, and stress levels. However, the lack of out-of-the-lab labelled data hinders the development of advanced machine learning models for predicting affective states. Furthermore, to the best of our knowledge, there are no publicly available datasets in the area of Human Memory Augmentation. This paper presents a dataset we collected during a 13-week study in a university setting. The dataset, named LAUREATE, contains the physiological data of 42 students during 26 classes (including exams), daily self-reports asking the students about their lifestyle habits (e.g. studying hours, physical activity, and sleep quality) and their performance across multiple examinations. In addition to the raw data, we provide expert features from the physiological data, and baseline machine learning models for estimating self-reported affect, models for recognising classes vs breaks, and models for user identification. Besides the use cases presented in this paper, among which Human Memory Augmentation, the dataset represents a rich resource for the UbiComp community in various domains, including affect recognition, behaviour modelling, user privacy, and activity and context recognition.
Pattern recognition and lithological interpretation of collocated seismic and magnetotelluric models using self-organizing maps

SUMMARY Joint interpretation of models from seismic tomography and inversion of magnetotelluric (MT) data is an efficient approach to determine the lithology of the subsurface. Statistical methods are well established but were developed for only two types of models so far (seismic P velocity and electrical resistivity). We apply self-organizing maps (SOMs), which have no limitations in the number of parameters considered in the joint interpretation. Our SOM method includes (1) generation of data vectors from the seismic and MT images, (2) unsupervised learning, (3) definition of classes by algorithmic segmentation of the SOM using image processing techniques and (4) application of learned knowledge to classify all data vectors and assign a lithological interpretation for each data vector. We apply the workflow to collocated P velocity, vertical P-velocity gradient and resistivity models derived along a 40 km profile around the geothermal site Gros Schonebeck in the Northeast German Basin. The resulting lithological model consists of eight classes covering Cenozoic, Mesozoic and Palaeozoic sediments down to 5 km depth. There is a remarkable agreement between the litho-type distribution from the SOM analysis and regional marker horizons interpolated from sparse 2-D industrial reflection seismic data. The most interesting features include (1) characteristic properties of the Jurassic (low P-velocity gradients, low resistivity values) interpreted as the signature of shales, and (2) a pattern within the Upper Permian Zechstein layer with low resistivity and increased P-velocity values within the salt depressions and increased resistivity and decreased P velocities in the salt pillows. The latter is explained in our interpretation by flow of less dense salt matrix components to form the pillows while denser and more brittle evaporites such as anhydrite remain in place during the salt mobilization.
Cyclopygid Trilobites from the Ordovician Builth‐Llandrindod Inlier, Central Wales

Cyclopygids are a minor element in the abundant trilobite fauna of the Ordovician Llanfawr Mudstones Formation (teretiusculus–gracilis biozones) of the Builth‐Llandrindod inlier. The rarity of these trilobites may be due to the accumulation of these sediments close to the shoreward limit of their depth range, and if this is the case, their presence, together with sedimentological evidence, can be used to infer the bathymetric limits within which the Llanfawr Mudstones Formation might have been deposited. Species of Degamella, Microparia (Microparia), Microparia (Heterocyclopyge), Sagavia and Emmrichops are described; some are common to the Dobrotivá Formation in Bohemia, whilst others have affinities with species from approximately coeval strata in Kazakhstan and north‐west China.
Generalization of deep learning models for predicting spatial gene expression profiles using histology images: A breast cancer case study

Spatial transcriptomics is a breakthrough technology that enables spatially-resolved measurement of molecular profiles in tissues, opening the opportunity for integrated analyses of morphology and transcriptional profiles through paired imaging and gene expression data. However, the high cost of generating data has limited its widespread adoption. Predicting gene expression profiles from histology images only can be an effective and cost-efficient in-silico spatial transcriptomics solution but is computationally challenging and current methods are limited in model performance. To advance research in this emerging and important field, this study makes the following contributions. We first provide a systematic review of deep learning methods for predicting gene expression profiles from histology images, highlighting similarities and differences in algorithm, model architecture, and data processing pipelines. Second, we performed extensive experiments to evaluate the generalization performance of the reviewed methods on several spatial transcriptomics datasets for breast cancer, where the datasets are generated using different technologies. Lastly, we propose several ideas for model improvement and empirically investigate their effectiveness. Our results shed insight on key features in a neural network model that either improve or not the performance of in-silico spatial transcriptomics, and we highlight challenges in developing algorithms with strong generalization performance. Key Messages We comprehensively compared the performance of existing methods for predicting spatial gene expression profiles from histology images We assessed the roles of different algorithms, model architectures, and data processing pipelines to model performance We performed extensive experiments to evaluate the generalization of the models on in-distribution and out-of-distribution spatial transcriptomics datasets We proposed several strategies for improving existing models and empirically investigated their effectiveness
Nitrogen concentration in New Zealand vegetation foliage derived from laboratory and field spectrometry

Multiple linear regression models of leaf nitrogen concentrations were developed from measured leaf values of selected native New Zealand plant species and spectral absorption obtained from laboratory NIR spectrometry (NIRS 6500). Two sets of equations were produced from samples that were from either dried and ground or fresh and intact foliage. Canopy nitrogen was predicted from these laboratory-derived equations and applied to GER 2600 spectroradiometer data collected above small plant canopies (r2 = 0.65 to 0.77). Reflectance data from the NIRS 6500 and GER 2600 were not directly comparable, therefore calibration of predicted canopy nitrogen values was necessary to place the predicted nitrogen in the observed range of values. Highest accuracy occurred where wavelengths selected in the laboratory analysis were not 'attributable' to nitrogen bonds rather to complex carbohydrate compounds (e.g. 2160 nm). In addition, as laboratory equations were derived from small sample sizes, canopy nitrogen was predicted with greatest accuracy when only a single 'attributable' wavelength was utilised.
Seasonal to Decadal Variability of Longshore Sand Transport at the Northwest Coast of Portugal

AbstractLongshore sediment transport (LST) is a major driver of coastal evolution. However, despite the recognition that it presents an unsteady behavior at the seasonal scale, the variability at longer term scales (interannual and decadal) is still far from being properly acknowledged. The present work contributes to the understanding of the seasonal to decadal variability of the LST, benefiting from recent developments in wave hindcast modeling. This work was developed for the northwest coast of Portugal, which is fully exposed to the highly energetic wave regime generated in the Northeast Atlantic Ocean that induces unusually large LST rates. Hindcast offshore waves, between 1953 and 2010, were used as offshore forcing to deduce LST estimates. The mean annual LST, between 1953 and 2010, shows an irregular and noncyclic pattern. Computed mean annual LST, for this coastal stretch, is around 1 million cubic meters directed to the south, with yearly averages ranging from 108,000 to 2.24 million m3 year−1 a.
Knowing Your Heart Condition Anytime

Electrocardiogram (ECG) monitoring has been widely explored in detecting and diagnosing cardiovascular diseases due to its accuracy, simplicity, and sensitivity. However, medical- or commercial-grade ECG monitoring devices can be costly for people who want to monitor their ECG on a daily basis. These devices typically require several electrodes to be attached to the human body which is inconvenient for continuous monitoring. To enable low-cost measurement of ECG signals with off-the-shelf devices on a daily basis, in this paper, we propose a novel ECG sensing system that uses acceleration data collected from a smartphone. Our system offers several advantages over previous systems, including low cost, ease of use, location and user independence, and high accuracy. We design a two-tiered denoising process, comprising SWT and Soft-Thresholding, to effectively eliminate interference caused by respiration, body, and hand movements. Finally, we develop a multi-level deep learning recovery model to achieve efficient, real-time and user-independent ECG measurement on commercial mobile phones. We conduct extensive experiments with 30 participants (with nearly 36,000 heartbeat samples) under a user-independent scenario. The average errors of the PR interval, QRS interval, QT interval, and RR interval are 12.02 ms, 16.9 ms, 16.64 ms, and 1.84 ms, respectively. As a case study, we also demonstrate the strong capability of our system in signal recovery for patients with common heart diseases, including tachycardia, bradycardia, arrhythmia, unstable angina, and myocardial infarction.
In vivo multi-site electrophysiology enabled by flexible optrodes towards bi-directional spinal cord interrogation

Optical neural interfaces combining optogenetics and electrophysiology have been demonstrated as powerful tools for distinguishing the causal roles of neural circuits in the nervous system. Functional optrodes for multipoint stimulation and recording have already been demonstrated in the brain. However, soft and flexible multimodal optrodes for the purpose of probing the spinal cord have remained undeveloped. Here, we present the design and fabrication of a novel optrode for multi-site optical stimulation and electrical recording in the spinal cord by combining optical fiber drawing of polymer material, laser micromachining, and integration of tungsten microelectrodes in a monolithic fiber-based structure. The results from space-resolved scattering measurements, electrochemical impedance spectroscopy, and an acute in vivo electrophysiology experiment in an anesthetized rodent suggest this probe as a potential novel interface, which can serve as a part of therapeutic strategies against neurological conditions and injury in the spinal cord.
Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations’ precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis; specifically, we identified three types of emQTL networks associated with alterations linked to the regulation of proliferation, metabolism, and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in transcription factor binding regions (TFBRs) located in enhancers. The underlying CpGs were topologically linked to upregulated genes through chromatin loops. Loss of enhancer methylation and target genes were exemplified in pancreatic cancer. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL. Taken together, our integrative pan-cancer analysis reveals a common architecture of aberrant DNA demethylation that illustrates a convergence of pathological regulatory mechanisms across cancer types.
Middle atmosphere temperature trend and solar cycle revealed by long-term Rayleigh lidar observations

The long-term temperature profile data sets obtained by Rayleigh lidars at three different northern latitudes within the Network for the Detection of Atmospheric Composition Change were used to derive the middle atmosphere temperature trend and response to the 11 year solar cycle. The lidars were located at the Mauna Loa Observatory, Hawaii (MLO, 19.5°N); the Table Mountain Facility, California (TMF, 34.4°N); and the Observatoire de Haute Provence, France (OHP, 43.9°N). A stratospheric cooling trend of 2-3 K/decade was found for both TMF and OHP, and a trend of ≤0.5 ± 0.5 K/decade was found at MLO. In the mesosphere, the trend at TMF (3-4 K/decade) was much larger than that at both OHP and MLO (<1 K/decade). The lidar trends agree well with earlier satellite and rocketsonde trends in the stratosphere, but a substantial discrepancy was found in the mesosphere. The cooling trend in the upper stratosphere at OHP during 1981-1994 (∼2-3 K/decade) was much larger than that during 1995-2009 (≤0.8 K/decade), coincident with the slightly increasing upper stratospheric ozone density after 1995. Significant temperature response to the 11 year solar cycle was found. The correlation was positive in both the stratosphere and mesosphere at MLO and TMF. At OHP a wintertime negative response in the upper stratosphere and a positive response in the middle mesosphere were observed during 1981-1994, but the opposite behavior was found during 1995-2009. This behavior may not be a direct solar cycle response at all but is likely related to an apparent response to decadal variability (e.g., volcanoes, modulated random occurrence of sudden stratospheric warmings) that is more complex.
Mesozoic transpression, transtension, subduction and metallogenesis in northern and central California

Middle Paleozoic to Middle Jurassic terrane assemblies in the Klamaths and Sierran Foothills consist of mafic–ultramafic complexes + fine‐grained terrigenous strata derived from previously accreted continental‐margin belts. Sutured oceanic terranes reflect c. 230 Myr of margin‐parallel slip involving chiefly transtension and transpression. Quartzofeldspathic clastic rocks and blueschists ± eclogites are very rare. Little devolatilization occurred at magmagenic depths; hence, coeval hydrothermal ore deposits and granitoids are uncommon. In contrast, nearly head‐on Cretaceous subduction of the Farallon plate generated the massive Klamath–Sierra Nevada volcanic–plutonic arc, reflecting dewatering of the eastward descending oceanic lithosphere in the magmagenic zone. Immature Great Valley forearc and Franciscan trench deposits shed from the arc record c. 70 Myr. of rapid crustal growth. Au‐bearing solutions rising from magmagenic depths, exsolved from plutons, and expelled from heated wall rocks were mobilized attending arc construction. Precipitation of gold‐bearing quartz veins occurred where H2O + CO2‐bearing fluids encountered major geochemical discontinuities in the wall rocks.
Spremembe prostornine Triglavskega ledenika

We compared a geodetic map of the Triglav glacier from 1952 to a topographical map from 2001 made using photogrammetric calculations from helicopter photographs to calculate changes in thickness and volume. Over fifty years, the glacier thinned in some places by more than thirty-five meters, and its volume decreased to one fiftieth of its size in 1952.
An Integrated Comparative Genomics, Subtractive Proteomics and Immunoinformatics Framework for the Rational Design of a Pan-Salmonella Multi-Epitope Vaccine

Salmonella infections are a global public health issue due to the high cost of illness surveillance, prevention, and treatment. In this study, we explored the core proteome in Salmonella to design a multi-epitope vaccine through Subtractive Proteomics and immunoinformatics approaches. A total of 2395 core proteins presents in 30 different strains of Salmonella (reference strain-NZ CP014051) were curated. Utilizing the subtractive proteomics approach on the Salmonella core proteome, Curlin major subunit A (CsgA) was selected as the vaccine candidate. csgA is a conserved gene that is related with biofilm formation. Immunodominant B and T cell epitopes from CsgA were predicted using numerous immunoinformatics tools. T lymphocyte epitopes had adequate population coverage and their corresponding MHC alleles showed significant binding scores after peptide-protein based molecular docking. Afterward, a multiepitope vaccine was constructed with peptide linkers and Human Beta Defensin-2 (as an adjuvant). The vaccine was found to be highly antigenic, non-toxic, non-allergic, and had physicochemical properties. Additionally, Molecular Dynamics Simulation and Immune Simulation demonstrated that the vaccine can bind with Toll Like Receptor 4 and elicit robust immune response. Using in vitro, in vivo, and clinical trials, our results would yield a Pan-Salmonella vaccine that will provide protection against various Salmonella species.
HYDROPOWER COSTS OF ENVIRONMENTAL FLOWS AND CLIMATE WARMING IN CALIFORNIA'S UPPER YUBA RIVER WATERSHED

Understanding the trade‐offs between water for the environment and water for hydropower in regulated rivers can inform decision making about hydropower system planning, policy and operations, especially with anticipated climate warming–induced changes in runoff. This study used a multireservoir optimisation model to assess the hydropower effects of increasing minimum instream flows (MIFs) and imposing weekly scale down ramp rates (DRRs) in three locations in California's Upper Yuba River (UYR). The UYR is currently used for hydropower generation yet has high potential for freshwater habitat restoration. Trade‐offs between DRRs, MIFs and hydropower generation and revenue are explored with uniform air temperature increases of 0 °C, 2 °C, 4 °C and 6 °C to approximate anticipated regional climate warming. With 6 °C warming, the most ecologically beneficial MIF and DRR reduced hydropower generation by 7.9% and revenue by 5.5% compared with base case management and a historical climate. This has important implications for licensure of the UYR project and other hydropower projects, where multiple benefits under current and potential future conditions must be balanced. This study provides a methodological approach that can be used by water managers, regulators and stakeholders to better understand inherent trade‐offs in allocating water for multiple beneficial uses in a nonstationary hydroclimate. Copyright © 2012 John Wiley & Sons, Ltd.
Mixed-Membership of Experts Stochastic Blockmodel

Title Mixed-Membership of Experts Stochastic Blockmodel Authors(s) White, Arthur; Murphy, Thomas Brendan Publication date 2016-03 Publication information Network Science, 4 (1): 48-80 Publisher Cambridge University Press Item record/more information http://hdl.handle. net/10197/8509 Publisher's statement This article has been accepted for publication and will appear in a revised form, subsequent to peer review and/or editorial input by Cambridge University Press, in Network Science. Publisher's version (DOI) 10.1017/nws.2015. 29