Miracle-dz/UniFAR-FTUs huggingface dataset:
ho Logical Imputation to Optimize Prognostic Risk Classiﬁcation in Metastatic Renal Cell Cancer

36 BACKGROUND: Application of the MSKCC and IMDC models is recommended for prognostication in metastatic renal cell cancer (mRCC). Patient classiﬁcation in MSKCC and IMDC risk groups in real-world observational studies is often hampered by missing data on required pre-treatment characteristics. 37 38 39 OBJECTIVES: To evaluate the effect of application of easy-to-use logical, or deductive, imputation on MSKCC and IMDC risk classiﬁcation in an observational study setting. 40 41 PATIENTSANDMETHODS: Weuseddataon713mRCCpatientswithﬁrst-linesunitinibtreatmentfromourobservational European multi-centre study EuroTARGET. Pre-treatment characteristics and follow-up were derived from medical ﬁles. Hospital-speciﬁc cut-off values for laboratory measurements were requested. The effect of logical imputation of missing data and consensus versus hospital-speciﬁc cut-off values on patient classiﬁcation and the subsequent models’ predictive performance for progression-free and overall survival (OS) was evaluated. 42 43 44 45 46 RESULTS: 45%ofthepatientshadmissingdatafor ≥ 1pre-treatmentcharacteristicforeithermodel.Still,72%ofallpatients could be unambiguously classiﬁed using logical imputation. Use of consensus instead of hospital-speciﬁc cut-offs led to a shift in risk group for 12% and 7% of patients for the MSKCC and IMDC model, respectively. Using logical imputation or other cut-offs did not inﬂuence the models’ predictive performance. These were in line with previous reports (c-statistic ∼ 0.64 for OS). CONCLUSIONS: Logical the proportion of patients that can be correctly classiﬁed into poor and intermediate MSKCC and IMDC risk groups in observational studies and its use in the ﬁeld should be advocated.
The Allen Cell Structure Segmenter: a new open source toolkit for segmenting 3D intracellular structures in fluorescence microscopy images

A continuing challenge in quantitative cell biology is the accurate and robust 3D segmentation of structures of interest from fluorescence microscopy images in an automated, reproducible, and widely accessible manner for subsequent interpretable data analysis. We describe the Allen Cell Structure Segmenter, a new Python-based open source toolkit developed for 3D segmentation of intracellular structures in fluorescence microscope images. This toolkit brings together classic image segmentation and iterative deep learning workflows first to generate initial high-quality 3D intracellular structure segmentations and then to easily curate these results to generate the ground truths for building robust and accurate deep learning models. The toolkit takes advantage of the high-replicate 3D live cell image data collected at the Allen Institute for Cell Science of over 30 endogenous fluorescently tagged human induced pluripotent stem cell (hiPSC) lines. Each cell line represents a different intracellular structure with one or more distinct localization patterns within undifferentiated hiPS cells and hiPSC-derived cardiomyocytes. The Allen Cell Structure Segmenter consists of two complementary elements, a classic image segmentation workflow with a restricted set of algorithms and parameters and an iterative deep learning segmentation workflow. We created a collection of 20 classic image segmentation workflows based on 20 distinct and representative intracellular structure localization patterns as a “lookup table” reference and starting point for users. The iterative deep learning workflow can take over when the classic segmentation workflow is insufficient. Two straightforward “human -in-the- loop” curation strategies convert a set of classic image segmentation workflow results into a set of 3D ground truth images for iterative model training without the need for manual painting in 3D. The deep learning model architectures used in this toolkit were designed and tested specifically for 3D fluorescence microscope images and implemented as readable scripts. This toolkit was applied to the robust segmentation of fluorescent lamin B1, which exhibits significant variability in its localization pattern during the cell cycle. The Allen Cell
High temperature spin dephasing in n-typed GaAs quantum wells

We perform a many-body study of the spin dephasing due to the D'yakonov-Perel' effect in n-typed GaAs (100) quantum wells for high temperatures ($\geq 120$ K) under moderate magnetic fields in the Voigt configuration by constructing and numerically solving the kinetic Bloch equations. We include all the spin conserving scattering such as the electron-phonon, the electron-nonmagnetic impurity as well as the electron-electron Coulomb scattering in our theory and investigate how the spin dephasing rate is affected by the initial spin polarization, temperature, impurity, magnetic field as well as the electron density. The dephasing obtained from our theory contains not only that due to the effective spin-flip scattering first proposed by D'yakonov and Perel' [Zh. Eksp. Teor. Fiz. 60, 1954(1971)[Sov. Phys.-JETP {\bf 38}, 1053(1971)]], but also the recently proposed many-body dephasing due to the inhomogeneous broadening provided by the DP term [Wu, J. Supercond. :Incorp. Novel Mechanism {\bf 14}, 245 (2001); Wu and Ning, Eur. Phys. J. B {\bf 18}, 373 (2000)]. We show that for the electron densities we study, the spin dephasing rate is dominated by the many-body effect. Equally remarkable is that we are now able to investigate the spin dephasing with extra large spin polarization (up to 100 %) which has not been discussed both theoretically and experimentally. We find a dramatic decrease of the spin dephasing rate for large spin polarizations. The spin dephasing time (SDT), which is defined as the inverse of the spin dephasing rate, we get at low initial spin polarization is in agreement with the experiment both qualitatively and quantitatively.
Human Pose Data Capture System for Training Fitness Apps

—The raising interest about human behaviour recognition on fields like robotics, fitness and security led to a growing attention of the academic community to computer vision problems and image analysis. In the past few years deep learning techniques, like neural networks, have been used extensively to improve the accuracy detection of the human keypoints, which are essential to describe human behaviour, poses and gestures on images. These methods need datasets and their accuracy is deeply tied with the quantity and quality of the datasets used for training. A dataset consists of not only the images but also the labels with the ground-truth localization of each keypoint of the human body, thus their creation is a very time consuming task and prone to errors. For the specific case of fitness images they tend to be more challenging since they can be fast paced, have occlusions and unusual body positions. In this work we propose a semi-supervised calibration method, with a motion capture system and smartphone cameras, to create datasets with high accuracy and low effort for training, validating or evaluating human pose estimation algorithms.
The Structure of Tempuutn Langit Tana: An Ethnographic Study of an Indonesian Myth

One of the myths in Indonesia is Tempuutn Langit Tana , namely the myth of the creation of the world (heaven and earth) in the Dayak Benuaq and Tunjung communities. This study aims to describe the structure of Tempuutn Mate in the Dayak Benuaq and Tunjung tribes. This research is a qualitative descriptive study with an ethnographic method following the Levi Strauss approach. The data source is the mythological book entitled Tempuutn: The Myth of Dayak Benuaq and Tunjung by Michael Hopes, Madrah, and Karaakng. Data were collected by reading and recording and validated using library source triangulations and interviews with Dayak Benuaq community leaders. Results revealed that Tempuutn Langit Tana created a cultural phenomenon, still preserved today, based on (1) a geographic level scheme in the form of a journey to meet a human god couple and their descendants who will inhabit the earth and heaven, (2) the techno-economic level in the form of creation process of the earth and heaven, (3) the sociological level related to
an ensemble deep learning framework

Thanks for the suggestion. We have followed the journal's formatting guidelines when making the tables, the plots, and other parts of the composition, making them easy to understand. The numbers of tables and figures have also been indicated in the text when they are referred to. We have added the description of Figure 2, please see Line 133-144. The description of Figure 3 can be found in Lines 178-186.
Detection of a planetary companion around the giant star γ1 Leonis

Aims. Our primary goal is to search for planets around intermediate mass stars. We are also interested in studying the nature of radial velocity (RV) variations of K giant stars. Methods. We selected about 55 early K giant (K0-K4) stars brighter than fifth magnitude that were observed using BOES, a high resolution spectrograph attached to the 1.8 m telescope at BOAO (Bohyunsan Optical Astronomy Observatory). BOES is equipped with I 2 absorption cell for high precision RV measurements. Results. We detected a periodic radial velocity variations in the K0 III star γ 1 Leonis with a period of P = 429 days. An orbital fit of the observed RVs yields a period of P = 429 days, a semi-amplitude of K = 208 m s -1 , and an eccentricity of e = 0.14. To investigate the nature of the RV variations, we analyzed the photometric, Ca II λ 8662 equivalent width, and line-bisector variations ofy' Leonis . We conclude that the detected RV variations can be best explained by a planetary companion with an estimated mass of m sin i = 8.78 M Jupiter and a semi-major axis of α = 1.19 AU, assuming a stellar mass of 1.23 M ⊙ .
INTELLIGENT IMAGE ANALYZER FOR TELECOM NETWORK

Moving ahead in digital times, it is very crucial that we address various tedious and time-consuming manual efforts in Telecom industry where multiple use cases require rigorous visual inspection and verifications to validate, upgrade or replace the network elements on field at regular intervals. Moreover, this process is less accurate since it is primarily based on human experience and doesn’t follow any specific rule base. This paper elaborates and explores the various practical use cases based on Image Analytics specifically in telecom domain ranging from usual brownfield use cases such as network inspection in case of upgrade of network (4G to 5G etc.), identifying and replacing various equipments to even complex scenarios such as address fallout management, drone-based image analytics for 5G small cell deployment, tower inspection etc. On the technical aspect, the solution leverages Deep learning-based object detection algorithms and advanced analytics to solve these multifaceted problems.
a journal of mathematics Klein links and related torus links

In this paper, we present our constructions and results leading up to our discovery of a class of Klein links that are not equivalent to any torus links. In particular, we calculate the number and types of components in a K p , q Klein link and show that K p , p ≡ K p , p − 1 , K p , 2 ≡ T p − 1 , 2 , and K 2 p , 2 p ≡ T 2 p , p . Finally, we show that in contrast to the fact that every Klein knot is a torus knot, no Klein link K p , p , where p ≥ 5 is odd, is equivalent to a torus link. The ﬁrst difﬁculty is that Klein bottles do not exist in three-dimensional MSC2010: 57M25.
Music Copyright Management using Smart Contracts and Tokenization on the Ethereum Blockchain

An increase in transparency and speed could improve the systems in place today for music copyright management and royalty payments in order to return the control of music copyright to its creators. The ownership of the copyright for a song is distributed between a group of people. There is no one specific global authority on the distribution of music copyright ownership. It instead exists in separate databases maintained and protected by individual companies or countries. This, along with a convoluted payment chain, leads to significant delays in the payment of royalties to artists, composers and other song copyright owners. With its inherent transparency, security and decentralisation, Blockchain is proposed to target these issues in music copyright management. This paper details a proof-of-concept copyright management, royalty payment and copyright marketplace decentralised application (DApp) built on the Polygon Blockchain. Tokenisation is used to represent the shares of ownership in a song s̓ copyright. Tokens are developed using the ERC1155 multi-token standard, which enables all tokens relating to individual copyright to be governed by the state of a single smart contract. This solution presents a novel utilisation of lazy minting to facilitate share initialisation and a forward-compatible secondary fee implementation following the ERC-2981 royalty standard. The application presents a royalty payment distribution through a pull payment method and utilises events for informative integration with blockchain explorers like Polyscan and wallet so ware like Metamask. Emphasis is placed on the implementation security, user focus and transparency of the system.
Evaluating Step-by-Step Reasoning through Symbolic Verification

Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations or chain-of-thoughts (CoT)) for in-context learning. On the other hand, these reasoning tasks are usually presumed to be more approachable for symbolic programming. To understand the mechanism of reasoning of LMs, we curate synthetic datasets containing equivalent (natural, symbolic) data pairs, where symbolic examples contain first-order logic rules and predicates from non-parametric knowledge bases (KBs), supporting automated verification of intermediate reasoning results. Then we revisit neuro-symbolic approaches and propose to learn from demonstrations containing logic rules and corresponding examples to iteratively reason over KBs, recovering Prolog's backward chaining algorithm and supporting automated verification of LMs' outputs. Comprehensive experiments are included to systematically compare LMLP with CoT in deductive reasoning settings, showing that LMLP enjoys more than $25\%$ higher accuracy than CoT on length generalization benchmarks even with smaller model sizes.
Supporting Posits in Deep Learning Frameworks: A PyTorch Implementation

—Reducing the energy consumption of computationally intensive deep learning implementations has received a growing interest in the last years. This is particularly relevant in applications where there are strict energy restrictions, such as space and aerial devices. To this end, the posit number format has shown promising results as a more energy efficient replacement to the standard IEEE-754 floating-point for deep learning computations. Recent research suggests that 16-bit posits achieve similar results as 32-bit floating-point and even smaller posits can be used to train and evaluate deep learning models. However, to study the use of posits for deep learning, researchers have to develop customized functions, since the most popular deep learning frameworks do not yet support posits. This work aims at bridging this gap, by integrating posits natively in PyTorch, the most popular framework for deep learning research. The proposed implementation makes posits a built-in data type in the framework, which means that they can be used in the same way as any other data type that the framework already supports. To validate the implementation, the convolutional neural network LeNet-5 was trained and tested using posits on the MNIST and FashionMNIST datasets. The obtained results with 16-bit posits were similar to those with 32-bit floating-point, suggesting that the implementation of the considered posit operators is correct. To disseminate this contribution, the produced code and documentation was made available on a public GitHub repository.
MENTAL THEORETICAL, OBSERVATIONAL OR DISPOSITIONAL APPROACH?

In the article I discuss the conceptual problem of other minds and different approaches to mental concepts. Firstly, I introduce the conceptual problem and argue that solutions proposed by theory-theory and direct perception approach are inadequate. I claim that mental concepts are neither theoretical terms nor observational terms. Then, I consider third option which states that mental concepts are dispositional terms, i.e. they concern particular patterns (stereotypes) of behavior. Finally, I argue that dispositional approach is to some extent coherent with phenomenological account and that phenomenological concept of embodiment can improve this position.
An Unexpectedly Small Emission Region Size Inferred from Strong High-frequency Diffractive Scintillation in GRB 161219B

We present Karl G. Jansky Very Large Array radio observations of the long gamma-ray burst GRB 161219B (z = 0.147) spanning 1–37 GHz. The data exhibit unusual behavior, including sharp spectral peaks and minutes-timescale large-amplitude variability centered at 20 GHz and spanning the full frequency range. We attribute this behavior to scattering of the radio emission by the turbulent ionized Galactic interstellar medium (ISM), including both diffractive and refractive scintillation. However, the scintillation is much stronger than predicted by a model of the Galactic electron density distribution (NE2001); from the measured variability timescale and decorrelation bandwidth we infer a scattering measure of SM ≈ (8–70) × 10−4 kpc m−20/3 (up to 25 times larger than predicted in NE2001) and a scattering screen distance of dscr ≈ 0.2–3 kpc. We infer an emission region size of μas ( cm) at ≈4 days, and find that prior to 8 days the source size is an order of magnitude smaller than model predictions for a uniformly illuminated disk or limb-brightened ring, indicating a slightly off-axis viewing angle or significant substructure in the emission region. Simultaneous multi-hour broadband radio observations of future GRB afterglows will allow us to characterize the scintillation more completely, and hence to probe the observer viewing angle, the evolution of the jet Lorentz factor, the structure of the afterglow emission regions, and ISM turbulence at high Galactic latitudes.
Scaling Laws in Magnetohydrodynamic Turbulence

Magnetic fields are observed in all gravitationally bound large-scale structures in the Universe. They have been detected in galaxies, in galaxy clusters, and there are strong hints that they exist in superclusters, and in galaxies at high redshifts. These last astronomical observations support the conjecture that magnetic fields have been generated in the early Universe by microphysics processes (for a full discussion see Ref. [1, 2, 3, 4, 5, 6, 7] and references therein). The study of the evolution of primordial magnetic fields has been developed in the framework of the so-called freely decaying magnetohydrodynamic (MHD) turbulence. Numerical and analytical studies show that the relevant integral quantities in MHD turbulence, as for example the magnetic energy and correlation length, evolve in time following simple power laws [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. In this paper we shall give a possible explanation of those laws in the light of some recent high-resolution numerical .
Large discrepancies in the representation of compound long-duration dry and hot spells over Europe in CMIP5

Long-duration dry spells in combination with temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a 15 consecutive number of days with daily precipitation less than 1mm. CMIP5 models tend to underestimate the persistence of dry spells in Northern Europe while a large variability exists between model estimates in Central and Southern Europe where models have contrasting biases. Our results indicate that this variability in model estimates is due to inherent model differences and not internal variability. In Northern Europe, differences in the representation of persistent dry spells are related to the representation of persistent anticyclonic conditions. We also 20 find a large spread in the representation of temperature extremes during dry spells. In Central and Southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Overall, there are large discrepancies in the representation of long-duration dry and hot events in the CMIP5 ensemble where the simulated climates vary from models with shorter-cooler dry spells to models with longer-hotter dry spells. This information 25 is important to consider when interpreting the plausibility of future projections from climate models.
Mechanical Characterization of Wire Arc Additive Manufactured HSLA Steel

In the aerospace industry, there is a continuous demand for faster, cheaper, and more sustainable options. Additive manufacturing, more speciﬁcally Wire and Arc Additive Manufacturing, emerges as a viable competitor to more traditional methods, showing the great potential of being used in part production. This technique allows the creation of large geometry parts for a variety of materials. Some disadvantages are related to the poor mechanical performance and surface quality of the deposited material. However, a deeper understanding of this process is still needed as it remains fairly unexplored. In this work, HSLA steel WAAM wall structures were manufactured using two diﬀerent heat inputs with the aim of studying its impact on the specimens’ mechanical performance. Results showed that it had some inﬂuence on the specimen’s mechanical performance, with the Lower Heat input set presenting a more reﬁned microstructure and higher values of maximum strain and hardness. Little diﬀerences were spotted regarding the various specimen heights along the wall. Regarding fatigue life tests, and despite the small number of specimens tested, a large dispersion of results was observed. Phenomena such as secondary hardening may have contributed to this and future studies should focus on their investigation. Strain ﬁeld measurement during the tensile tests with digital image correlation (DIC) allowed the veriﬁcation of stress concentration areas in speciﬁc defects that led to the rupture of the specimens. These failure modes were veriﬁed in the fractography analysis and the mechanical behavior was compared with numerical models without the presence of defects.