Miracle-dz/UniFAR-FTUs huggingface dataset:
Phylogenetic Analysis of Early Hominids [and Comments and Reply]

The proposal of the new australopithecine species Australopithecus afarensis has led to a multiplicity of hypotheses concerning the evolutionary relationships between the known Pliocene and Pleistocene hominid species. We use phylogenetic analysis to gain a new perspective on the subject. Using 69 traits, we construct a series of 12 complexes, each with a defining polarized morphocline. Four mutually exclusive cladograms are derived from these complexes, the most parsimonious of which implies that Homo habilis and A. robustus/boisei are more closely related to each other than either is to A. africanus and that these three species form a distinct evolutionary group relative to the more primitive A. afarensis. We advocate a phylogeny wherein A. afarensis is ancestral to A. africanus, which is in turn ancestral to A. robustus/boisei and H. habilis. We believe that the evolutionary transition from Australopithecus to Homo involved reduction in the size of the chewing teeth and associated traits leading to a unique derived condition in H. habilis that superficially resembles the primitive condition of A. afarensis. Five other current phylogenies are treated as critiques of this one.
Representational aspects of depth and conditioning in normalizing flows

Normalizing flows are among the most popular paradigms in generative modeling, especially for images, primarily because we can efficiently evaluate the likelihood of a data point. Normalizing flows also come with difficulties: models which produce good samples typically need to be extremely deep -- which comes with accompanying vanishing/exploding gradient problems. Relatedly, they are often poorly conditioned since typical training data like images intuitively are lower-dimensional, and the learned maps often have Jacobians that are close to being singular. In our paper, we tackle representational aspects around depth and conditioning of normalizing flows -- both for general invertible architectures, and for a particular common architecture -- affine couplings. For general invertible architectures, we prove that invertibility comes at a cost in terms of depth: we show examples where a much deeper normalizing flow model may need to be used to match the performance of a non-invertible generator. For affine couplings, we first show that the choice of partitions isn't a likely bottleneck for depth: we show that any invertible linear map (and hence a permutation) can be simulated by a constant number of affine coupling layers, using a fixed partition. This shows that the extra flexibility conferred by 1x1 convolution layers, as in GLOW, can in principle be simulated by increasing the size by a constant factor. Next, in terms of conditioning, we show that affine couplings are universal approximators -- provided the Jacobian of the model is allowed to be close to singular. We furthermore empirically explore the benefit of different kinds of padding -- a common strategy for improving conditioning -- on both synthetic and real-life datasets.
NATSA: A Near-Data Processing Accelerator for Time Series Analysis

Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, and more. Matrix profile, the state-of-the-art algorithm to perform time series analysis, computes the most similar subsequence for a given query subsequence within a sliced time series. Matrix profile has low arithmetic intensity, but it typically operates on large amounts of time series data. In current computing systems, this data needs to be moved between the off-chip memory units and the on-chip computation units for performing matrix profile. This causes a major performance bottleneck as data movement is extremely costly in terms of both execution time and energy. In this work, we present NATSA, the first Near-Data Processing accelerator for time series analysis. The key idea is to exploit modern 3D-stacked High Bandwidth Memory (HBM) to enable efficient and fast specialized matrix profile computation near memory, where time series data resides. NATSA provides three key benefits: 1) quickly computing the matrix profile for a wide range of applications by building specialized energy-efficient floating-point arithmetic processing units close to HBM, 2) improving the energy efficiency and execution time by reducing the need for data movement over slow and energy-hungry buses between the computation units and the memory units, and 3) analyzing time series data at scale by exploiting low-latency, high-bandwidth, and energy-efficient memory access provided by HBM. Our experimental evaluation shows that NATSA improves performance by up to 14.2× (9.9× on average) and reduces energy by up to 27.2 × (19.4 × on average), over the state-of-the-art multi-core implementation. NATSA also improves performance by 6.3 × and reduces energy by 10.2 × over a general-purpose NDP platform with 64 in-order cores.
Linear degradation of calpains - the key to an accurate post-mortem indicator?

This review investigates the possibility of enzymes, specifically  and m-calpains providing an uninfluenced biochemical marker. Research associated with calpains has largely been concerned with the tenderization of animal protein and more recently medical conditions such as Stroke and Alzheimer’s disease. Results of these studies have shown these enzymes degrade in a dependable and consistent manner over a period of 360 hours. It is this key fact that potentially may provide a PMI biochemical marker that will reduce the ambiguity of current estimations.
Laser-Microstructured ZnO/p-Si Photodetector with Enhanced and Broadband Responsivity across the Ultraviolet–Visible–Near-Infrared Range

We develop ZnO/p-Si photodetectors by atomic layer deposition (ALD) of ZnO thin films on laser-microstructured silicon and we investigate their electrical and optical behavior, demonstrating high sensitivity and broadband operation. Microstructured p-type silicon was obtained by ns-laser irradiation in SF6 gas, which results in the formation of quasi-ordered and uniform microspikes on the silicon surface. The irradiated silicon contains sulfur impurities, which extend its absorbance to the near infrared. A thin film of ZnO was conformally deposited on the microstructured silicon substrates by ALD. Photoluminescence measurements indicate high crystalline quality of the ZnO film after annealing. Current-voltage (I-V) measurements of the ZnO/p-Si heterodiodes in dark show a non-linear behavior with unusual high current values in reverse bias. Under illumination photocurrent is observed for reverse bias, even for wavelengths below the silicon bandgap in the case of the laser-microstructured photodetectors. Higher current values are measured for the microstructured photodetectors, compared to planar ones. Photoconductivity measurements show enhanced responsivity across the UV-Vis-NIR spectral range for the laser-microstructured devices, due to their increased surface area and light absorption.
MIME: MIMicking Emotions for Empathetic Response Generation

Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of this polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at this https URL.
TCLNet: Learning to Locate Typhoon Center using Deep Neural Network

Conventional typhoon center location algorithms mostly rely on digital image processing and mathematical morphology operation, which achieve limited performance. In this paper, we propose an efficient end-to-end deep neural network named TCLNet to locate the typhoon center position automatically. In addition, we also present a brand new large-scale typhoon center location dataset (TCLD) so that the TCLNet can be trained in a supervised manner. Furthermore, we propose to use a novel TCL+ piecewise loss function to further improve the performance of TCLNet. Extensive experimental results and comparison demonstrate the performance of our model, and TCLNet achieves a 14.4% increase in accuracy on the basis of a 92.7% reduction in parameters compared with SOTA deep learning based typhoon center location methods.
Fairness and Diversity for Rankings in Two-Sided Markets

Ranking items by their probability of relevance has long been the goal of conventional ranking systems. While this maximizes traditional criteria of ranking performance, there is a growing understanding that it is an oversimplification in online platforms that serve not only a diverse user population, but also the producers of the items. In particular, ranking algorithms are expected to be fair in how they serve all groups of users -- not just the majority group -- and they also need to be fair in how they divide exposure among the items. These fairness considerations can partially be met by adding diversity to the rankings, as done in several recent works, but we show in this paper that user fairness, item fairness and diversity are fundamentally different concepts. In particular, we find that algorithms that consider only one of the three desiderata can fail to satisfy and even harm the other two. To overcome this shortcoming, we present the first ranking algorithm that explicitly enforces all three desiderata. The algorithm optimizes user and item fairness as a convex optimization problem which can be solved optimally. From its solution, a ranking policy can be derived via a new Birkhoff-von Neumann decomposition algorithm that optimizes diversity. Beyond the theoretical analysis, we provide a comprehensive empirical evaluation on a new benchmark dataset to show the effectiveness of the proposed ranking algorithm on controlling the three desiderata and the interplay between them.
Reconstructing Masses of Merging Neutron Stars from Stellar r-process Abundance Signatures

Neutron star mergers (NSMs) are promising astrophysical sites for the rapid neutron-capture (“r”) process, but can their integrated yields explain the majority of heavy-element material in the Galaxy? One method to address this question implements a forward approach that propagates NSM rates and yields along with stellar formation rates and compares those results with observed chemical abundances of r-process-rich, metal-poor stars. In this work, we take the inverse approach by utilizing r-process-element abundance ratios of metal-poor stars as input to reconstruct the properties—especially the masses—of their neutron star (NS) binary progenitors. This novel analysis provides an independent avenue for studying the population of the original NS binary systems that merged and produced the r-process material now incorporated in Galactic metal-poor halo stars. We use ratios of elements typically associated with the limited-r-process and the actinide region to those in the lanthanide region (i.e., Zr/Dy and Th/Dy) to probe the NS masses of the progenitor merger. We find that NSMs can account for all r-process material in metal-poor stars that display r-process signatures, while simultaneously reproducing the present-day distribution of double-NS systems. Notably, with our model assumptions and the studied stellar sample, we postulate that the most r-process enhanced stars (the r–II stars) on their own would require progenitor NSMs of asymmetric systems that are distinctly different from present ones in the Galaxy. We also explore variations to the model and find that the predicted degree of asymmetry is most sensitive to the electron fraction of the remnant disk wind.
Transformer-Based Neural Text Generation with Syntactic Guidance

We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation. Existing approaches to this problem use recurrent structures, which not only suffer from the long-term dependency problem but also falls short in modeling the tree structure of the syntactic guidance. We propose to leverage the parallelism of Transformer to better incorporate parse trees. Our method first expands a partial template constituency parse tree to a full-fledged parse tree tailored for the input source text, and then uses the expanded tree to guide text generation. The effectiveness of our model in this process hinges upon two new attention mechanisms: 1) a path attention mechanism that forces one node to attend to only other nodes located in its path in the syntax tree to better incorporate syntax guidance; 2) a multi-encoder attention mechanism that allows the decoder to dynamically attend to information from multiple encoders. Our experiments in the controlled paraphrasing task show that our method outperforms SOTA models both semantically and syntactically, improving the best baseline's BLEU score from 11.83 to 26.27.
History of Coffee Consumption and Risk of Alzheimer's Disease: a Meta-epidemiological Study of Population-based Cohort Studies

Background and Purpose Four published quantitative systematic reviews showed conflicting results involving coffee consumption and the risk of Alzheimer's disease (AD). The aim of this meta-epidemiological meta-analysis (MEMA) was to evaluate the factors underlying the conflicting results and estimate the effect size and direction of the AD risk associated with coffee consumption in population-based cohort studies. Methods The primary subjects of MEMA were derived from 3 cohort studies selected by the related systematic reviews. Additional studies involving the primary subjects were searched using citation discovery tools. Prospective cohort studies evaluating the association between coffee consumption and AD risk were selected. A fixed effects model was applied to estimate the summary relative risk (sRR) and its 95% confidence intervals (CIs). Subgroup analysis was conducted according to the level of coffee consumption. Egger's test was used to evaluate publication bias. Results Four cohort studies were finally selected. A total of 36,300 participants from Finland, Sweden, Germany, and the United States of America were selected. The sRR (and its 95% CI) (I-squared value) by highest-versus-lowest method was 0.98 (0.92–1.05) (0.0%). In addition, none of the results of subgroup analyses by the level of coffee consumption showed any statistical significance. Conclusions This MEMA found that there was no association between coffee consumption and AD risk. Based on recent evidence suggesting that gene-environment interactions contribute to AD pathogenesis, it is necessary to conduct population-based cohort studies involving non-Caucasians.
Mechanisms of TERT Reactivation and Its Interaction with BRAFV600E

The telomerase reverse transcriptase (TERT) gene, which is repressed in most differentiated human cells, can be reactivated by somatic TERT alterations and epigenetic modulations. Moreover, the recruitment, accessibility, and binding of transcription factors also affect the regulation of TERT expression. Reactivated TERT contributes to the development and progression of cancer through telomere lengthening-dependent and independent ways. In particular, because of recent advances in high-throughput sequencing technologies, studies on genomic alterations in various cancers that cause increased TERT transcriptional activity have been actively conducted. TERT reactivation has been reported to be associated with poor prognosis in several cancers, and TERT promoter mutations are among the most potent prognostic markers in thyroid cancer. In particular, when a TERT promoter mutation coexists with the BRAFV600E mutation, these mutations exert synergistic effects on a poor prognosis. Efforts have been made to uncover the mechanisms of these synergistic interactions. In this review, we discuss the role of TERT reactivation in tumorigenesis, the mechanisms of TERT reactivation across all human cancers and in thyroid cancer, and the mechanisms of interactions between BRAFV600E and TERT promoter mutations.
Drought stress strengthens the link between chlorophyll fluorescence parameters and photosynthetic traits

Chlorophyll fluorescence (ChlF) has been used to understand photosynthesis and its response to climate change, particularly with satellite-based data. However, it remains unclear how the ChlF ratio and photosynthesis are linked at the leaf level under drought stress. Here, we examined the link between ChlF ratio and photosynthesis at the leaf level by measuring photosynthetic traits, such as net CO2 assimilation rate (An), the maximum carboxylation rate of Rubisco (Vcmax), the maximum rate of electron transport (Jmax), stomatal conductance (gs) and total chlorophyll content (Chlt). The ChlF ratio of the leaf level such as maximum quantum efficiency of PSII (Fv/Fm) is based on fluorescence kinetics. ChlF intensity ratio (LD685/LD740) based on spectrum analysis was obtained. We found that a combination of the stomatal limitation, non-stomatal limitation, and Chlt regulated leaf photosynthesis under drought stress, while Jmax and Chlt governed the ChlF ratio. A significant link between the ChlF ratio and An was found under drought stress while no significant correlation in the control, which indicated that drought stress strengthens the link between the ChlF ratio and photosynthetic traits. These results suggest that the ChlF ratio can be a powerful tool to track photosynthetic traits of terrestrial ecosystems under drought stress.
Structure of the lens and its associations with the visual quality

In humans, the lens is the organ with the ability to change morphology and refractive power, designated as accommodation, to focus light from various distances and obtain clear retinal image. The accommodative ability of the lens depends on its structure and biological parameters. The lens grows throughout the life, forming specific lens sutures and a unique gradient refractive index, and possesses regenerative ability under certain circumstances. Minimally invasive lens surgery that preserves endogenous lens epithelial stem/progenitor cells (LECs) can achieve functional lens regeneration in humans. The lens is the main source of intraocular aberration, especially intraocular higher-order aberrations (IHOAs) which is found to be binocularly symmetrical in phakic eyes. There is a compensation mechanism between corneal aberrations and lens aberrations. Therefore, the structure and the biological parameters of the lens, the binocular relationship of the lens and the correlation between the lens and cornea affect visual quality. This paper summarises the above findings and their current and potential applications in refractive surgeries, providing a comprehensive understanding of the lens as a strong determinant of visual quality in the optical system.
Protective Effect of Ethanolic Extract of Fruit of Luffa aegyptiaca Mill. on Carbon Tetrachloride Induced Hepatotoxicity in Rats.

Oxidative damage is implicated in the pathogenesis of various liver injuries. The study was aimed to investigate the antioxidant activity of fruit of Luffa aegyptiaca Mill. on carbon tetrachloride (CCl4) induced oxidative stress in female wistar rats. CCl4 injection induced oxidative stress by a significant rise in serum marker enzymes and thiobarbituric acid reactive substances (TBARS) along with the reduction of antioxidant enzymes. In serum, the activities of enzymes like alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP) were evaluated. Pre-treatment of rats with different doses of plant extract (250 and 500 mg/kg) significantly lowered ALT, AST and TBARS levels against CCl4 treated rats. Hepatic enzymes like catalase and reduced glutathione were significantly increased by treatment with plant extract, against CCl4 treated rats. Histopathological examinations showed extensive liver injuries, characterized by extensive hepatocellular degeneration/necrosis, inflammatory cell infiltration, congestion, and sinusoidal dilatation. Oral administration of the fruit extract at a dose of 500mg/kg body weight significantly reduced the toxic effects of CCl4. The activity of fruit extract at the dose of 500 mg/kg was comparable to the standard drug, silymarin. Based on these results, it was observed that Luffa aegyptiaca Mill. fruit extract protects liver from oxidative stress induced by CCl4 and thus helps in evaluation of traditional claim on this plant.
Proanthocyanidins: Absorption,Metabolism and Bioavailability

Proanthocyanidins (PAs), as a predominant group of both flavonoids and polyphenols, are less studied than other simpler compounds due to their large molecular size, complicated structure and polymeric nature, though they play major roles in both human diet and animal forages and have noticeable health effects. In this review, the absorption and bioavailability of PAs are compared among catechin (CC) monomers, PA dimers/trimers and larger PA oligomers. The metabolic fate of PAs is summarized, and one possible mechanism of PA degradation in viva is discussed, based on some evidence of a preliminary depolymerization. Regarding to various physiological potential of PAs, plasma circulation and cellular uptake of these compounds and their metabolites are also briefly referred. These help to figure out to what extent the monomers, dimers/trimers, larger PA polymers, or their metabolites directly contribute to the multiple biological effects in vivo, so as to demonstrate the health effects of PAs, and elucidate the relevant mechanisms.