Miracle-dz/UniFAR-FTUs huggingface dataset:
SciREX: A Challenge Dataset for Document-Level Information Extraction

Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level N-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX .
A Tetraploid Cytotype of Paspalum Durifolium: Cytology, Reproductive Behavior and Its Relationship to Diploid P. Intermedium

Paspalum durifolium is a South American bunchgrass that was previously reported to be an apomictic allohexaploid species. Ten accessions from recently acquired germplasm were investigated for ploidy level. Eight were tetraploid (2n = 4x = 40) and two were hexaploid (2n = 6x = 60). This is the first report of tetraploid plants in P. durifolium. The 4x accession Q-3947 was chosen for cytological and embryological studies. It showed regular meiosis with primarily bivalent chromosome pairing. Embryological surveys demonstrated that this tetraploid reproduces sexually. Studies on pollen germination and pollen tube growth, after self and after open pollination, suggest that the tetraploid cytotype has a genetic system to prevent self-fertilization. A controlled interspecific hybrid (2n = 3x = 30) was obtained among diploid P. intermedium (genome formula II) and tetraploid P. durifolium. The mean meiotic chromosome association for the hybrid was 8.7 I, 7.5 II, and 2.1 III with a range per cell of 5–14 I, 4–10 II, and 0–5 III. These results indicate that the parents of the hybrid have one homologous genome. However, some chromosomes of the P. intermedium genome may have some residual homology with chromosomes of the second genome of P. durifolium.
A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning

By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features. We found the inclusion of single nucleotide polymorphisms (SNPs) coded as count matrices improved model performance significantly, and no substantial difference in model performance with respect to molecular featurization between the common open source MOrdred descriptors and Dragon7 descriptors. Alongside this analysis, we outline data integration between CCL screening datasets and present evidence that new metrics and imbalanced data techniques, as well as advances in data standardization, need to be developed.
Jukebox: A Generative Model for Music

We introduce Jukebox, a model that generates music with singing in the raw audio domain. We tackle the long context of raw audio using a multi-scale VQ-VAE to compress it to discrete codes, and modeling those using autoregressive Transformers. We show that the combined model at scale can generate high-fidelity and diverse songs with coherence up to multiple minutes. We can condition on artist and genre to steer the musical and vocal style, and on unaligned lyrics to make the singing more controllable. We are releasing thousands of non cherry-picked samples at this https URL, along with model weights and code at this https URL
TESS Hunt for Young and Maturing Exoplanets (THYME). II. A 17 Myr Old Transiting Hot Jupiter in the Sco-Cen Association

We present the discovery of a transiting hot Jupiter orbiting HIP 67522 (Teff ∼ 5650 K; M* ∼ 1.2M⊙) in the 10–20 Myr old Sco-Cen OB association. We identified the transits in the TESS data using our custom notch filter planet search pipeline and characterize the system with additional photometry from Spitzer; spectroscopy from SOAR/Goodman, SALT/HRS, LCOGT/NRES, and SMARTS/CHIRON; and speckle imaging from SOAR/HRCam. We model the photometry as a periodic Gaussian process with transits to account for stellar variability and find an orbital period of days and radius of R⊕. We also identify a single transit of an additional candidate planet with radius R⊕ that has an orbital period of ≳23 days. The validated planet HIP 67522b is currently the youngest transiting hot Jupiter discovered and is an ideal candidate for transmission spectroscopy and radial velocity follow-up studies, while also demonstrating that some young giant planets either form in situ at small orbital radii or else migrate promptly from formation sites farther out in the disk.
Optical Wireless Communications for In-Body and Transdermal Biomedical Applications

This article discusses the fundamental architectures for optical-wireless-systems for biomedical-ap-plications. After summarizing the main applications and reporting their requirements, we describe the characteristics of the transdermal and in-body optical channels (OCs) as well as the challenges that they impose in the design of communication systems. Specifically, we provide three possible architectures for transdermal communications, namely electro-optical monitoring, opto-electrical, and all-optical (AO), for neural stimulation, which are currently under investigation, whereas for in-body communications, we provide a nano-scale AO concept. For each architecture, we discuss the main operation principles, the technology enablers, and research directions for their development. Finally, we highlight the necessity of designing an infor-mation-theoretic framework for the analysis and design of the physical and medium access control layers, which takes into account the channels' characteristics.
Local quantum Fisher information and local quantum uncertainty in two-qubit Heisenberg XYZ chain with Dzyaloshinskii–Moriya interactions

Quantum correlations play an important role in quantum information theory. Quantum Fisher information (QFI) via local observables and via local measurements (i.e. local QFI (LQFI)) is a fundamental concept in quantum estimation and quantum metrology. LQFI captures the quantumness of correlations in a multicomponent quantum system. This new discord-like measure is very similar to the quantum correlations measure called local quantum uncertainty (LQU). In this work we will study the LQFI and the LQU in the two-qubit Heisenberg XYZ spin chain model with Dzyaloshinskii–Moriya (DM) interaction in the z-direction. Here we show that the DM interaction and spin interactions along the x- and z-axes can increase and maintain LQFI and LQU. It is also shown that because of thermal fluctuations LQFI and LQU are decreased by increasing temperature. They are equal to one at very low temperature and start to decay only after a thereshold temperature.
Homotopes of finite-dimensional algebras

Abstract In this article, we study homotopes of finite-dimensional algebras (not necessarily, associative). In the case of associative algebras, we study homotopes by the methods of Category theory and give description of the so-called well-tempered elements of finite-dimensional associative algebra in algebraic terms.
Performance of Generalized Hypercubes in Dynamic Peer-to-Peer Networks

Highly dynamic peer-to-peer networks are becoming very significant due to their wide range of applications. Although many structures were proposed to deploy peer-to-peer networks, hypercube structures could grasp the researchers' attention for study because of their desirable properties. A lot of studies have considered binary hypercubes for research. In this paper, we study more generalized topology of hypercubes, namely generalized hypercubes and measure their performance in dynamic networks where nodes can enter and leave the network at any time. Python code was developed to simulate the networks and visualize the results. In addition to being highly flexible and scalable, generalized hypercubes proved to have lower average path length and lower verge density than binary hypercubes.
Top‐down and bottom‐up control of periphyton by benthivorous fish and light supply in two streams

Summary We conducted a paired large-scale predation experiment over 32 months in two streams being seasonally shaded by deciduous riparian trees, using the benthivorous fish species gudgeon (Gobio gobio) and stone loach (Barbatula barbatula) as top predators. The biomass of benthic grazers and periphyton in the presence/absence of fish was measured and the periphyton production was compared with the consumption rates using a model-based approach. A three-level trophic cascade from benthivorous fish via benthic grazers to periphyton was evident from the field experiment. Integrated over the whole study period, fish reduced the biomass of benthic grazers and indirectly increased the periphyton biomass. Scenario analyses, using a simple dynamic model, indicated top-down control of periphyton to be strongest during autumn, when periphyton growth was light-limited, and weaker in the spring, when periphyton growth was not light-limited. The seasonal light supply variation was caused by shading due to deciduous riparian trees during the vegetation period. This asymmetry in temporal processes weakened the top-down control in a natural benthic community. Even though grazer biomass is naturally reduced in summer, due to the emergence of the most abundant species (mayflies), a grazer biomasses high enough to reduce the spring periphyton peak could not be sustained by the low summer periphyton growth. We suppose that the temporal decoupling of grazer biomass from periphyton biomass might be caused by the very short generation time of the primary producers (days) compared with the long generation time of the primary consumers (mostly 1 year).
The Effectiveness of Uniform Sampling for Center-Based Clustering with Outliers

Clustering has many important applications in computer science, but real-world datasets often contain outliers. Moreover, the presence of outliers can make the clustering problems to be much more challenging. To reduce the complexities, various sampling methods have been proposed in past years. Namely, we take a small sample (uniformly or non-uniformly) from input and run an existing approximation algorithm on the sample. Comparing with existing non-uniform sampling methods, the uniform sampling approach has several significant benefits. For example, it only needs to read the data in one-pass and is very easy to implement in practice. Thus, the effectiveness of uniform sampling for clustering with outliers is a natural and fundamental problem deserving to study in both theory and practice. In this paper, we propose a new and unified framework for analyzing the effectiveness of uniform sampling for three representative center-based clustering with outliers problems, $k$-center/median/means clustering with outliers. We introduce a "significance" criterion and prove that the performance of uniform sampling depends on the significance degree of the given instance. In particular, we show that the sample size can be independent of the ratio $n/z$ and the dimensionality. More importantly, to the best of our knowledge, our method is the first uniform sampling approach that allows to discard exactly $z$ outliers for these three center-based clustering with outliers problems. The results proposed in this paper also can be viewed as an extension of the previous sub-linear time algorithms for the ordinary clustering problems (without outliers). The experiments suggest that the uniform sampling method can achieve comparable clustering results with other existing methods, but greatly reduce the running times.
How to Reduce Dimension With PCA and Random Projections?

In our “big data” age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are “data-oblivious” methods such as random projections and sketching, and “data-aware” methods such as principal component analysis (PCA). Both have their strengths, such as speed for random projections, and data-adaptivity for PCA. In this work, we study how to combine them to get the best of both. We study “sketch and solve” methods that take a random projection (or sketch) first, and compute PCA after. We compute the performance of several popular sketching methods (random iid projections, random sampling, subsampled Hadamard transform, CountSketch, etc) in a general “signal-plus-noise” (or spiked) data model. Compared to well-known works, our results are: 1) give asymptotically exact results; 2) apply when the signal components are only slightly above the noise, but the projection dimension is non-negligible. We also study stronger signals allowing more general covariance structures. We find that: 1) signal strength decreases under projection in a delicate way depending on the structure of the data and the sketching method; 2) orthogonal projections are slightly more accurate; 3) randomization does not hurt too much, due to concentration of measure; 4) the CountSketch can be somewhat improved by a normalization method. Our results have implications for statistical learning and data analysis. We also illustrate that the results are highly accurate in simulations and in analyzing empirical data.
A Cellular Automaton Model of Early Tumor Growth and Invasion: The Effects of Native Tissue Vascularity and Increased Anaerobic Tumor Metabolism

A hybrid cellular automaton model is described and used to simulate early tumor growth and examine the roles of host tissue vascular density and tumor metabolism in the ability of a small number of monoclonal transformed cells to develop into an invasive tumor. The model incorporates normal cells, tumor cells, necrotic or empty space, and a random network of native microvessels as components of a cellular automaton state vector. Diffusion of glucose and H(+)ions (the latter largely resulting from the tumor's excessive reliance on anaerobic metabolism) to and from the microvessels, and their utilization or production by cells, is modeled through the solution of differential equations. In this way, the cells and microvessels affect the extracellular concentrations of glucose and H(+)which, in turn, affect the evolution of the automaton. Simulations of the model demonstrate that: (i) high tumor H(+)ion production is favorable for tumor growth and invasion; however for every H(+)ion production rate, there exists a range of optimal microvessel densities (leading to a local pH favorable to tumor but not to normal cells) for which growth and invasion is most effective, (ii) at vascular densities below this range, both tumor and normal cells die due to excessively low pH, (iii) for vascular densities above the optimal range the microvessel network is highly efficient at removing acid and therefore the tumor cells lose their advantage over normal cells gained by high local H(+)concentration. While significant spatial gradients of glucose formed, no regions of detrimentally poor glucose perfusion (for either cell type) were observed, regardless of microvessel density. Depending on metabolic phenotype, a variety of tumor morphologies similar to those clinically observed were realized in the simulations. Lastly, a sharp transition (analogous to that of the adenoma-carcinoma sequence) between states of initial tumor confinement and efficient invasiveness was observed when H(+)production reached a critical value.
High variability of plasmid uptake rates in Escherichia coli isolated from sewage and river sediments

The horizontal transfer of plasmids is a key mechanism behind the spread of antibiotic resistance in bacteria. So far, transfer rate constants were measured for a variety of plasmids, donors and recipients. The employed strains typically had a long history in laboratories. Existing data are, therefore, not necessarily representative for real-world environments. Moreover, information on the inter-strain variability of plasmid transfer rates is scarce. Using a high-throughput approach, we studied the uptake of RP4 by various Escherichia coli recipients using Serratia marcescens as the donor. The recipient strains were isolated from human-borne sewage and river sediments. The rate constants of plasmid transfer generally followed a log-normal distribution with considerable variance. The rate constants for good and poor recipients (95 and 5% quantile) differed by more than three orders of magnitude. Specifically, the inter-strain variability of the rate constant was large in comparison to alterations induced by low-level antibiotic exposure. We did not find evidence for diverging efficiencies of plasmid uptake between E. coli recipients of different origin. On average, strains isolated from river bottom sediments were equally efficient in the acquisition of RP4 as isolates extracted from sewage. We conclude that E. coli strains persisting in the aquatic environment and those of direct human origin share a similar intrinsic potential for the conjugative uptake of certain plasmids. In view of the large inter-strain variability, we propose to work towards probabilistic modeling of the environmental spread of antibiotic resistance.
Beyond Brane-Higgs Regularization

We present the field theory treatment of 5D fermions coupled to a brane-Higgs field in order to get the fermionic Kaluza-Klein mass spectrum. The extra dimension is flat and we consider the interval compactification. The approach in the literature involves a regularization procedure of shifting or smoothing the brane-Higgs field. We show several drawbacks of these methods and propose to treat the vacuum expectation value of the Higgs field in the boundary conditions for the bulk fields. For that purpose, we need to introduce fermion brane terms, reminiscent of the holographic approach. We highlight the link between the conservation of currents and the natural versus essential boundary conditions on the fields.
Context-Aware Wireless Connectivity and Processing Unit Optimization for IoT Networks

A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of Things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the best connectivity and processing unit (e.g., device, fog, and cloud) along with the percentage of data to be offloaded by jointly optimizing energy consumption, response time, security, and monetary cost. The proposed scheme employs a reinforcement learning algorithm and manages to achieve significant gains compared to deterministic solutions. In particular, the requirements of IoT devices in terms of response time and security are taken as inputs along with the remaining battery level of the devices, and the developed algorithm returns an optimized policy. The results obtained show that only our method is able to meet the holistic multiobjective optimization criteria, albeit, the benchmark approaches may achieve better results on a particular metric at the cost of failing to reach the other targets. Thus, the proposed approach is a device-centric and context-aware solution that accounts for the monetary and battery constraints.
Integrated Human Immunodeficiency Virus Type 1 Sequence in J-Lat 10.6

The full length of HIV/R7/E−/GFP integrated in the J-Lat 10.6 cell line was sequenced in this study. The single copy of the integrated virus, including the breakpoints from the human chromosome to the provirus, was amplified by two separate PCRs. A 10,200-bp genome sequence was acquired, analyzed, and deposited in GenBank. ABSTRACT The full length of HIV/R7/E−/GFP integrated in the J-Lat 10.6 cell line was sequenced in this study. The single copy of the integrated virus, including the breakpoints from the human chromosome to the provirus, was amplified by two separate PCRs. A 10,200-bp genome sequence was acquired, analyzed, and deposited in GenBank.
Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research

Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks — such as sentiment polarity classification — and datasets, there is an underlying perception that this field has reached its maturity. In this article, we discuss this perception by pointing out the shortcomings and under-explored, yet key aspects of this field necessary to attain true sentiment understanding. We analyze the significant leaps responsible for its current relevance. Further, we attempt to chart a possible course for this field that covers many overlooked and unanswered questions.
Random and repetitive primer amplified polymorphic DNA analysis of Bacillus sphaericus

A comparative study of 15 strains representing the five homology groups of Bacillus sphaericus was performed by two PCR methods: RAPD and rep‐PCR fingerprinting. The PCR analysis performed with primers corresponding to the naturally occurring repetitive sequences REP, ERIC and BOX, as well as with three random primers, showed highly variable patterns and allowed differentiation of the strains studied. This demonstrated the high discriminative power of the methods. The cluster analysis revealed a low level of similarity between the different homology groups, and within groups I and III, which is evidence of the high genetic heterogeneity of the species B. sphaericus. Close genetic relatedness was observed for the representatives of group IIA, pathogenic to mosquitoes, which supports the idea for differentiation of this group as a separate species.