Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Personalize Medicine from Aggregate Data

There is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of patients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring 'training' data for large numbers of patients, labeling each patient according to the outcome of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunately, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a major impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algorithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is readily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i. ) leveraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii. ) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii. ) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy.
Upward movement of buried seeds: another ecological role of dung beetles promoting seedling establishment

Abstract: Dung beetles are known to perform important ecological functions, such as secondary seed dispersal of vertebrate-defecated seeds. We found that dung beetles also move buried seeds upwards, with positive consequences for seedling establishment. In the Lacandon rain forest of southern Mexico we conducted field experiments to address three questions: (1) What proportions of different-sized seeds buried by dung beetles are exhumed by them? (2) Does upward relocation of seeds caused by dung beetle activity promote seedling establishment? (3) Does recurrent beetle activity increase seedling establishment? Using 4-mm, 8-mm and 12-mm beads as seed mimics, embedded in howler-monkey dung, we found that 2–6% of buried beads were later exhumed by beetles, with smaller beads exhumed more often. In small plots (N = 100) where beetles were allowed to bury dung and seed rain was excluded, seedling establishment was over three times higher compared with plots without beetle activity. In plots (N = 8) where we placed dung on four occasions in 1 mo, seedling establishment was more than twice as high compared with plots with single-time dung placement. We believe that our findings open up interesting research opportunities to help further elucidate this newly discovered ecological function of dung beetles.
Comparison of the development of stem galls induced by Aulacidea hieracii (Hymenoptera: Cynipidae) on hawkweed and by Diplolepis spinosa (Hymenoptera: Cynipidae) on rose

Plant galls induced by two distantly related species of cynipid wasps, Aulacidea hieracii Bouche, which belongs to a basal lineage and induces galls on Hieracium umbellatum L. , and Diplolepis spinosa (Ashmead), which belongs to a derived group and induces galls on Rosa blanda Ait. and Rosa rugosa Thunb. , were compared. As gall characters are determined by the wasp rather than the host plant, we hypothesized that information on gall development would contribute to our understanding of the evolution of the cynipid complex. Galls induced by both species are spherical and multichambered and arise on stems. All stages of gall development were examined including host plant response to oviposition and the eggs, gall induction, reaction of the plant to larval feeding, and the formation of nutritive cells, vascular tissues, and sclerenchyma within the gall during growth and maturation. Galls of A. hieracii develop in a manner similar to galls inhabited by inquiline cynipids, a basal lineage to which they are close.
Regulation of Neurofibromin by Post-Translational Modification

Neurofibromatosis 1 (NF1) is a congenital disease affecting individuals worldwide. NF1 expresses the tumor suppressor protein neurofibromin, and mutations in the NF1 gene can lead to truncated inactive isoform of neurofibromin. Neurofibromin function is to prevent an up-regulation of the Ras signaling cascade by inhibiting the Ras protein. Phosphorylation of neurofibromin in its different domains regulate its ability to inhibit the Ras signaling. O-glycosylation of nucleo- and cytoplasmic proteins, like phosphorylation, is an equally dynamic and ubiquitous modification that can occur on the same or neighboring serine/threonine of proteins. Different post-translational modifications induce functional changes in proteins, but assessing these temporary changes is difficult to determine in vivo. Computer assisted studies can help to predict the functional changes of proteins. In this study the functional changes induced by structural modifications in mammalian neurofibromin are predicted using computer-assisted techniques. These findings illustrate the basis of different NF1-pathogenesis.
The oldest pre-Podocnemidid turtle (Chelonii, Pleurodira), from the early Cretaceous, Ceará state, Brasil, and its environment

A new form of a chelonian Pleurodira is described, Brasilemys josai, n.g. , n. sp. Its phyletic position is examined with respect to previously established phylogenies. It is situated with respect to recently described taxa whose position is not yet clearly established, such as Dortokidae, Hamadachelys and an unnamed form from Araripe. In the Podocnemidoidea, Brasilemys n.g. represents the earliest known specimen of the Podocnemidoidae, immediately after the divergence of the Bothremydidae. It is part of the formidable radiation in the Pelomedusoides which occurs during the early Cretaceous when South America separated from Africa. The turtle fauna in Chapada do Araripe includes four freshwater Pelomedusoides, samples of the ancient northern Gondwana fauna, as well as a cryptodiran turtle, possibly a littoral form belonging to a more cosmopolitan group. In agreement with the stratigrahical data as well as data from the study of fossil crocodiles, this turtle fauna dates between the late Aptian fauna of Gadoufaoua (Niger) and the late Albian-early Cenomanian fauna from the Sahara. Key words: Chelonians, New taxon, Phyletic relationships, Cretaceous, Brasil, Palaeoecolgical and Stratigraphical considerations.
PINT: Probabilistic In-band Network Telemetry

Commodity network devices support adding in-band telemetry measurements into data packets, enabling a wide range of applications, including network troubleshooting, congestion control, and path tracing. However, including such information on packets adds significant overhead that impacts both flow completion times and application-level performance. We introduce PINT, an in-band network telemetry framework that bounds the amount of information added to each packet. PINT encodes the requested data on multiple packets, allowing per-packet overhead limits that can be as low as one bit. We analyze PINT and prove performance bounds, including cases when multiple queries are running simultaneously. PINT is implemented in P4 and can be deployed on network devices.Using real topologies and traffic characteristics, we show that PINT concurrently enables applications such as congestion control, path tracing, and computing tail latencies, using only sixteen bits per packet, with performance comparable to the state of the art.
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques

Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about peoples sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.
Blob interactions in 2D scrape-off layer simulations

Interaction of coherent structures known as blobs in the scrape-off layer of magnetic confinement fusion devices is investigated. Isolated and interacting seeded blobs as well as full plasma turbulence are studied with a two dimensional fluid code. The features of the blobs (size, amplitude, position) are determined with a blob tracking algorithm, which identifies them as coherent structures above a chosen density threshold and compared to a conventional center of mass approach. The agreement of these two methods is shown to be affected by the parameters of the blob tracking algorithm. The benchmarked approach is then extended to a population of interacting plasma blobs with statistically distributed amplitudes, sizes and initial positions for different levels of intermittency. As expected, for decreasing intermittency, we observe an increasing number of blobs deviating from size-velocity scaling laws of perfectly isolated blobs. This is found to be caused by the interaction of blobs with the electrostatic potential of one another, leading to higher average blob velocities. The degree of variation from the picture of perfectly isolated blobs is quantified as a function of the average waiting time of the seeded blobs.
Identification and characterization of five polymorphic microsatellite loci in the freshwater copepod Hemidiaptomus gurneyi (Copepoda: Calanoida: Diaptomidae)

Abstract Hemidiaptomus diaptomid copepods are known to be excellent biological indicators for the highly biodiverse crustacean communities inhabiting Mediterranean temporary ponds (MTPs), an endangered inland water habitat whose conservation is considered a priority according to the “Habitat Directive” of the European Union. This study reports on the characterization of five polymorphic microsatellite loci in Hemidiaptomus gurneyi, to be used as markers for fine-scale studies on the population genetic structure and metapopulation dynamics of a typical and obligate MTP dweller. The five selected loci proved to be polymorphic in the species, with three to five polymorphic loci per studied population. Overall, mean heterozygosity scored for all loci and populations was lower than that reported for the few other diaptomid species for which microsatellite loci have been to date described; this is possibly due to the intrinsically fragmented and isolated peculiar habitat inhabited by the species. Furthermore, the presence of indels within the flanking regions of selected loci was scored. This study, albeit confirming the technical difficulties in finding proper microsatellite markers in copepods, provides for the first time a set of useful polymorphic microsatellite loci for a Hemidiaptomus species, thus allowing the realization of fine-scale phylogeographic and population genetics studies of this flagship crustacean taxon for MTPs.
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads

The last decade has witnessed growth in the computational requirements for training deep neural networks. Current approaches (e.g., data/model parallelism, pipeline parallelism) parallelize training tasks onto multiple devices. However, these approaches always rely on specific deep learning frameworks and requires elaborate manual design, which make it difficult to maintain and share between different type of models. In this paper, we propose Auto-MAP, a framework for exploring distributed execution plans for DNN workloads, which can automatically discovering fast parallelization strategies through reinforcement learning on IR level of deep learning models. Efficient exploration remains a major challenge for reinforcement learning. We leverage DQN with task-specific pruning strategies to help efficiently explore the search space including optimized strategies. Our evaluation shows that Auto-MAP can find the optimal solution in two hours, while achieving better throughput on several NLP and convolution models.
Understanding Object Affordances Through Verb Usage Patterns

In order to interact with objects in our environment, we rely on an understanding of the actions that can be performed on them, and the extent to which they rely or have an effect on the properties of the object. This knowledge is called the object "affordance". We propose an approach for creating an embedding of objects in an affordance space, in which each dimension corresponds to an aspect of meaning shared by many actions, using text corpora. This embedding makes it possible to predict which verbs will be applicable to a given object, as captured in human judgments of affordance. We show that the dimensions learned are interpretable, and that they correspond to patterns of interaction with objects. Finally, we show that they can be used to predict other dimensions of object representation that have been shown to underpin human judgments of object similarity.
Incidences with curves in three dimensions

We study incidence problems involving points and curves in $R^3$. The current (and in fact only viable) approach to such problems, pioneered by Guth and Katz, requires a variety of tools from algebraic geometry, most notably (i) the polynomial partitioning technique, and (ii) the study of algebraic surfaces that are ruled by lines or, in more recent studies, by algebraic curves of some constant degree. By exploiting and refining these tools, we obtain new and improved bounds for point-curve incidence problems in $R^3$. Incidences of this kind have been considered in several previous studies, starting with Guth and Katz's work on points and lines. Our results, which are based on the work of Guth and Zahl concerning surfaces that are doubly ruled by curves, provide a grand generalization of most of the previous results. We reconstruct the bound for points and lines, and improve, in certain significant ways, recent bounds involving points and circles (in Sharir, Sheffer and Zahl), and points and arbitrary constant-degree algebraic curves (in Sharir, Sheffer and Solomon). While in these latter instances the bounds are not known (and are strongly suspected not) to be tight, our bounds are, in a certain sense, the best that can be obtained with this approach, given the current state of knowledge. As an application of our point-curve incidence bound, we show that the number of triangles spanned by a set of $n$ points in $R^3$ and similar to a given triangle is $O(n^{15/7})$, which improves the bound of Agarwal et al. Our results are also related to a study by Guth et al.~(work in progress), and have been recently applied in Sharir, Solomon and Zlydenko to related incidence problems in three dimensions.
On Entropic Optimization and Path Integral Control

This article is motivated by the question whether it is possible to solve optimal control (OC) or dynamic optimization problems in a similar fashion to how static optimization problems can be addressed with Evolutionary Strategies (ES). The latter maintain a sequence of Gaussian search distributions that converge to the optimum. For the moment, this question has been answered partially by a set of algorithms that are known as Path Integral Control (PIC). Those maintain a sequence of locally linear Gaussian feedback controllers. So far PIC methods have been derived solely from the theory of Linearly Solvable OC, which includes only a narrow subset of optimal control problems and has only limited application potential as a consequence. We aim to address this question within a more general mathematical setting. Therefore, we first identify the framework of entropic inference as a suitable setting to synthesise stochastic search algorithms. Therewith we establish the formal framework of entropic optimization and provide a compelling justification for the inclusion of entropy measures in stochastic optimization. From this theory follows a formal optimal search distribution sequence which converges monotonically to the Dirac delta distribution centred at the optimum. Then we demonstrate how this result can be used to derive Gaussian search distributions similar to existing ES. We then proceed to transfer these ideas from a static to a dynamic setting, therewith establishing the framework of Entropic OC which shares characteristics with entropy based Reinforcement Learning. From this theory we can construct a number of formal optimal path distribution sequences. Thence we derive the outlines of a generalised algorithmic framework complementing the existing PIC class. Our main ambition is to reveal how all of these fields are related in a most exciting fashion.
Effect of Phytophthora infestans on potato yield in dependence on variety characteristics and fungicide control.

The epidemiology of potato late blight ( Phytophthora infestans) was observed over the three-years with 110 potato varieties from various maturity groups treated or not treated with fungicides. The determined severity values were transformed into AUDPC coefficients characterizing rate of disease spreading across the crop in connection with varietal characteristics, fungicide application and year effects. The analysis of the pathogen population for the dis tribution of races virulent to usually used resistance genes indicated non-efficiency of these genes; resistance could only be increased by breeding for polygenically determined horizontal resistance. There were important yearly variations observed for the epidemiology of P. infestans. A correlation was detected between increasing AUDPC value and yield reduction in both fungicide treated and non-treated variants. AUDPC value of the treated variant is to a certain extent positively correlated with the value of the non-treated variant. However, particular interaction between potato genotype and applied fungicide program was shown, which could positively or negatively affect dis ease course in dependence on the variety.
Obesity-associated phenotypes and circulating levels of ghrelin, cholecystokinin, low-density lipoprotein and zinc: genetic and observational studies

iii ACKNOWLEDGEMENTS I want to acknowledge and express my deep gratitude to the members of my dissertation committee, without whose unstinting support, encouragement, and guidance, and critical insights that helped focus my efforts, as well as for giving me access to a wide range of resources and assistance. Dr. Kimberly Kline and Dr. Michelle Lane for their invaluable ideas and suggestions, and for encouraging me to make the most of the resources and opportunities. I am also indebted to them for their patience, comments, suggestions, and candid feedback. Upadhyay, and Wendy Vorndam of SFBR for their assistance with the technical and statistical analysis aspects of my research. I want to thank the many friends that I made during my years at the University of Texas at Austin and SFBR, especially Dr. Elizabeth Tejero, for the incredible support and motivation that I received from them. the love, strength and inspiration. Without them, I could not have pursued my dreams. Obesity is a complex problem that is believed to result from both genetic and environmental factors. This condition greatly increases the risk of developing serious health consequences, such as metabolic syndrome and cardiovascular disease. The primary goal of this research was to study the genetic influence on plasma ghrelin, cholecystokinin (CCK), and low-density lipoprotein (LDL), and their relationship with obesity. A secondary goal was to investigate the effect of weight loss on plasma zinc and other risk factors for metabolic syndrome. Aims 1 and 2 were to estimate the additive heritabilities and to localize the responsible chromosomal quantitative trait loci associated with circulating levels of the appetite regulating hormones, ghrelin and CCK, in baboons. Plasma ghrelin and CCK were higher in baboons than humans, with males exhibiting greater levels. Ghrelin was inversely linked to body weight (r = 0.23, p < 0.001), insulin v (r =-0.19, p < 0.05), and leptin (r =-0.14, p < 0.05). Significant heritabilities were observed for ghrelin and CCK. A strong signal was detected for plasma CCK on chromosome 17p12 (LOD = 3.1, p < 0.01). Aim 3 was to detect heritability and pleiotropy between the obesity-related anthropometric phenotypes and low-density lipoproteins (LDL) in humans. Effect of genes on LDL size was evident, with substantial heritability and strong genetic correlations between LDL and obesity-related traits; small LDL and weight (rho G = 0.65, p < 0.001), waist (rho G = 0.80, p < 0.001), and BMI (rho …
A General Framework and Novel Transceiver Architecture Based on Hybrid Beamforming for NOMA in Massive MIMO Channels

In this paper, a generic user-grouping based hybrid beamforming framework for mm-wave massive MIMO systems with code domain non-orthogonal multiple access (NOMA), which is considered as an intra-group process, is proposed. It is shown that message passing algorithm (MPA) for decoding sparse code multiple access (SCMA) can be directly applied in this framework without additional complexity. While classical multi-user shared access (MUSA) receiver is adapted for downlink, a novel receiver architecture which is an improvement over classical one is proposed for uplink MUSA. This receiver makes MUSA preferable over SCMA for uplink transmission with lower complexity. In addition, simulation results showed that code domain NOMA methods outperform conventional methods where users are spatially close to each other and number of radio frequency (RF) chains at the base station (BS) is very limited.
A Tukey type trend test for repeated carcinogenicity bioassays, motivated by multiple glyphosate studies

In the last two decades, significant methodological progress to the simultaneous inference of simple and complex randomized designs, particularly proportions as endpoints, occurred. This includes: i) the new Tukey trend test approach, ii) multiple contrast tests for binomial proportions, iii) multiple contrast tests for poly-k estimates, and Add-1 approximation for one-sided inference. This report focus on a new Tukey type trend test to evaluate repeated long-term carcinogenicity bioassays which was motivated by multiple glyphosate studies. Notice, it is not the aim here to contribute to the evaluation of Glyphosate and its controversies. By means of the CRAN-packages tukeytrend, MCPAN, multcomp the real data analysis is straightforward possible.