Miracle-dz/UniFAR-FTUs huggingface dataset:
A Palliative Care Intervention for Patients on Phase 1 Studies.

Background: Phase 1 clinical trials remain vital for oncology care. Patients on these trials require supportive care for quality-of-life (QOL) concerns. Objective: To test a Palliative Care Intervention (PCI) for patients with solid tumors enrolled in Phase I therapeutic trials with a priori hypothesis that psychological distress, QOL, satisfaction, symptoms, and resource utilization would be improved in the PCI group. Design: This unblinded randomized trial compared the PCI with usual care in patients accrued to Phase I Clinical Trials. Subjects (n = 479) were followed for 24 weeks, with 12 weeks as the primary outcome. Setting: Two Comprehensive Cancer Centers in the United States. Subjects: A consecutive sample, 21 years or older, English fluency, with solid tumors initiating a Phase 1 trial. Measurements: Psychological Distress (Distress Thermometer), QOL total and subscales (FACT-G), satisfaction (FAM-CARE), survival, and resource utilization (chart audit). Results: PCI subjects showed improved Psychological Distress (-0.47, p = 0.015) and Emotional Well-Being (0.81, p = 0.045), with differences on variables of QOL and distress between sites. High rates of symptom-management admissions (41.3%) and low rates of Advance Directive completion (39.0%), and hospice enrollment (30.7%), despite a median survival in both groups of 10.1 months from initiating a Phase 1 study. Conclusions: A nurse-delivered PCI can improve some QOL outcomes and distress for patients participating in Phase 1 trials. Greater integration of PC is needed to provide quality care to these patients and to support transitions from treatment to supportive care, especially at the end of life. ClinicalTrials.gov Identifier: NCT01612598.
Pathogenesis of Eastern Equine Encephalitis Virus in Mice and Development of a Second Generation Vaccine

Abstract : Eastern equine encephalitis virus (EEEV), an Alphavirus in the family Togaviridae, is an important human and veterinary pathogen, and is considered the most deadly of the mosquito-borne alphaviruses due to the high case fatality rate associated with clinical infections, reaching as high as 75% in humans and 90% in horses. In patients that survive, the neurologic sequelae are often devastating. Although natural infections are acquired by mosquito bite, EEEV is also highly infectious by aerosol. This fact, along with the relative ease of production and stability of this virus, has led it to being identified as a potential agent of bioterrorism. Characterizing the early events in the pathogenesis of EEEV (FL93-939) by various routes of infection is an important first step in developing a vaccine to prevent disease. We hypothesize that when mice are challenged with EEEV either intranasally or via aerosol that the virus will enter the brain rapidly using the olfactory system, which will have important implications for therapeutic and vaccine development. The goal of vaccine development is to produce a product that closely mimics natural infection; thereby stimulating an appropriate and effective immune response. However, new EEEV vaccine candidates should protect against both subcutaneous and aerosol exposure to virulent virus, which can be challenging. Formalin, INA, and gamma irradiation have been used to inactivate viruses and have recently been used to inactivate V3526. Inactivating an attenuated-live virus provides an additional layer of safety in the formulation of the vaccine candidate. We hypothesize that formalin, INA, and gamma-irradiation will inactivate a genetically modified strain of EEEV (CVEV1219) but will maintain its antigenic epitopes, thereby creating a valid vaccine candidate that will result in protective immune responses in mice when challenged by the parental virus (EEEVFL93-939).
Unsupervised Sound Separation Using Mixture Invariant Training

In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sources. Reliance on this synthetic training data is problematic because good performance depends upon the degree of match between the training data and real-world audio, especially in terms of the acoustic conditions and distribution of sources. The acoustic properties can be challenging to accurately simulate, and the distribution of sound types may be hard to replicate. In this paper, we propose a completely unsupervised method, mixture invariant training (MixIT), that requires only single-channel acoustic mixtures. In MixIT, training examples are constructed by mixing together existing mixtures, and the model separates them into a variable number of latent sources, such that the separated sources can be remixed to approximate the original mixtures. We show that MixIT can achieve competitive performance compared to supervised methods on speech separation. Using MixIT in a semi-supervised learning setting enables unsupervised domain adaptation and learning from large amounts of real world data without ground-truth source waveforms. In particular, we significantly improve reverberant speech separation performance by incorporating reverberant mixtures, train a speech enhancement system from noisy mixtures, and improve universal sound separation by incorporating a large amount of in-the-wild data.
Life history parameters of the microtype parasitoid fly Pales pavida (Diptera: Tachinidae), and effects of host age and number of eggs ingested by host on parasitism success

ABSTRACT With the aim of developing better procedures for rearing the microtype tachinid fly Pales pavida (Meigen), we performed ecological studies in the laboratory using the natural host Mythimna separata (Walker), investigating larval development, mating behaviour, individual oviposition patterns and relationships between parasitisation and number of eggs ingested (NEI) per host. The host instar at the time of parasitoid egg ingestion significantly affected the development time of the immature parasitoid: development took longer when the hosts ingested eggs when at the fifth instar than at the sixth (last) instar. There was no difficulty obtaining mated females in the laboratory when day 0–1 female flies were kept with day 2–4 males. Mean lifetime fecundity was 5805 ± 568 eggs per female. Daily rates of oviposition by individual females varied widely; the greatest number of eggs laid in a day was exactly 1700. When the NEI by day 1 fifth instars or day 0 or 3 last instars was 1, 3, 6 or 10, the parasitisation percentage tended to increase with increasing NEI, although it did not differ significantly between NEI 6 and 10. Therefore, the percentage adult emergence per egg ingested decreased from NEI 6 to 10, particularly in the case of last instars. Using day 0 last instars, with six eggs ingested per host, should increase parasitisation rates and shorten the development time of the parasitoid for rearing.
Exploring Understandable Algorithms to Suggest Fitness Tracker Goals that Foster Commitment

While fitness trackers are gaining popularity, they struggle to offer long-term health benefits, largely due to their inability to offer engaging goals. Understanding how trackers can suggest and update fitness goals can lead to building improved systems that support wellbeing. We investigate how to suggest fitness tracker goals to users and ways to help them commit to those goals. We compared algorithms for step goal setting in a pre-study. Next, we conducted two surveys (a vignette study and a survey using the users’ Fitbit data) that compared the users’ attitudes to suggested goals, with and without disclosing the algorithm to them. We found that explaining how a step goal was computed increased goal commitment and, in one study, contributed to building trust in the goal. Our work shows that explaining how a tracker works can help build engaging fitness tracking experiences. We contribute insights on designing transparent personal informatics systems.
Species of the water-fern megaspore genus Molaspora from a Cenomanian deposit in western France: occurrence, sporoderm ultrastructure and evolutionary relationships

Abstract The partially reticulate sculpture of Molaspora aspera sp. nov., a marsileaceous megaspore from a Cenomanian deposit in western France, distinguishes it from other species of Molaspora. An acrolamella entirely surrounds and obscures a small tetrad scar, a feature that has been demonstrated hitherto within members of the genus only in M. fibrosa. It was also encountered for the first time in M. lobata, with which the new species is associated in the same French mesofossil assemblage. The ultrastructure of the sporoderm of M. aspera is similar to that of M. lobata, but differs particularly in that the inner epispore is markedly thicker and may also contain numerous large, homogeneous spherules or, alternatively, holes of comparable dimensions and only a few small spherules. It is possible that these are a response to some hostile bacterial or other activity when the developing sporoderm was partially permeable. The cavity replacing part of the epispore in one of the specimens, and in the specimen of M. lobata examined, may be a preservational feature or have served to increase buoyancy of the spore in water. Molaspora lobata is very similar to megaspores of fossil and extant Regnellidium, but M. aspera bears some resemblance to other members of extant Marsileaceae and certain species of Cretaceous Arcellites, although there are significant differences between them. This suggests that Molaspora is a heterogeneous taxon embracing megaspores produced by water ferns of more than one natural genus, of which only Regnellidium has survived to the present day.
Lazy Batching: An SLA-aware Batching System for Cloud Machine Learning Inference

In cloud ML inference systems, batching is an essential technique to increase throughput which helps optimize total-cost-of-ownership. Prior graph batching combines the individual DNN graphs into a single one, allowing multiple inputs to be concurrently executed in parallel. We observe that the coarse-grained graph batching becomes suboptimal in effectively handling the dynamic inference request traffic, leaving significant performance left on the table. This paper proposes LazyBatching, an SLA-aware batching system that considers both scheduling and batching in the granularity of individual graph nodes, rather than the entire graph for flexible batching. We show that LazyBatching can intelligently determine the set of nodes that can be efficiently batched together, achieving an average $15\times, 1.5\times$, and $5.5\times$ improvement than graph batching in terms of average response time, throughput, and SLA satisfaction, respectively.
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training

Personalized recommendations are one of the most widely deployed machine learning (ML) workload serviced from cloud datacenters. As such, architectural solutions for high-performance recommendation inference have recently been the target of several prior literatures. Unfortunately, little have been explored and understood regarding the training side of this emerging ML workload. In this paper, we first perform a detailed workload characterization study on training recommendations, root-causing sparse embedding layer training as one of the most significant performance bottlenecks. We then propose our algorithm-architecture co-design called Tensor Casting, which enables the development of a generic accelerator architecture for tensor gather-scatter that encompasses all the key primitives of training embedding layers. When prototyped on a real CPUGPU system, Tensor Casting provides $1.9 - 21 \times$ improvements in training throughput compared to state-of-the-art approaches.
Hybridization signals in Fucus diversification

Hybridization is one of the major evolutionary forces driving speciation. Understanding the role of hybridization in diversification processes and the generation of evolutionary novelty is a fundamental goal in evolutionary biology. Hybridization is a ubiquitous process resulting in hybrid lineages with admixed genomes. Patterns of genome admixture may reflect different pathways of the evolutionary history of species that can be related to biogeographic processes. The marine genus Fucus (Fucales, Phaeophyceae) includes key foundational species that structure intertidal ecosystems on North Atlantic shores. A recently described species, Fucus guiryi revealed discordant phylogenetic signals in the northern part of its range and possible introgressive hybridization involving the sister group F. vesiculosus/F. spiralis where these occur in sympatry. However, to understand the extent of nuclear introgression and its contribution to divergence and adaptive evolution in this system remains a great challenge. The main objective of this project was to analyse the extent of nuclear introgression among Fucus species and to understand if hybridization signals support a single or multiple hybrid origin of F. guiryi, by undertaking comparative analyses across the extant species ranges. To answer these questions we used extensive transcriptome sequence data to assess incongruence between gene and species trees on a genome-wide scale, while accounting for ancestral shared polymorphism (incomplete lineage sorting, ILS). We combined phylogenomic tools based on coalescence and Bayesian analysis of multigene genealogies to untangle the relationships between these three lineages. Moreover, we tested for the occurrence of introgression between populations throughout the allopatric and sympatric distributional ranges of both parental and putative hybrid lineages. This work reports the first largescale phylogenomic analysis of divergence in the genus Fucus. We found agreement between the different approaches applied to species tree reconstruction. Most importantly, we concluded that the conflicting patterns observed between gene phylogenies could be attributed to introgressive hybridization between these species.
Structural Brain Asymmetries in Youths with Combined and Inattentive Presentations of Attention Deficit Hyperactivity Disorder

We appreciate the efforts of The Neuro Bureau and Virginia Tech’s ARC team for providing the ADHD-200 preprocessed data repository. We give profound thanks to the King Abdullah University of Science and Technology for providing our team with the baseline funds to conduct this work. Last, we thank the KAUST Biostatistics Group and Dr. Aritra Dutta for their feedback and suggestions. The authors acknowledge the support provided in part by the NARSAD Young Investigator Grant and the K01 NIH (1MH110645).
Stellar occultations enable milliarcsecond astrometry for Trans-Neptunian objects and Centaurs

Context. Trans-Neptunian objects (TNOs) and Centaurs are remnants of our planetary system formation, and their physical properties have invaluable information for evolutionary theories. Stellar occultation is a ground-based method for studying these distant small bodies and has presented exciting results. These observations can provide precise profiles of the involved body, allowing an accurate determination of its size and shape. Aims. The goal is to show that even single-chord detections of TNOs allow us to measure their milliarcsecond astrometric positions in the reference frame of the Gaia second data release (DR2). Accurate ephemerides can then be generated, allowing predictions of stellar occultations with much higher reliability. Methods. We analyzed data from various stellar occultation detections to obtain astrometric positions of the involved bodies. The events published before the Gaia era were updated so that the Gaia DR2 stellar catalog is the reference, thus providing accurate positions. Events with detection from one or two different sites (single or double chord) were analyzed to determine the event duration. Previously determined sizes were used to calculate the position of the object center and its corresponding error with respectto the detected chord and the International Celestial Reference System propagated Gaia DR2 star position. Results. We derive 37 precise astrometric positions for 19 TNOs and four Centaurs. Twenty-one of these events are presented here for the first time. Although about 68% of our results are based on single-chord detection, most have intrinsic precision at the submilliarcsecond level. Lower limits on the diameter of bodies such as Sedna, 2002 KX14, and Echeclus, and also shape constraints on 2002 VE95, 2003 FF128, and 2005 TV189 are presented as valuable byproducts. Conclusions. Using the Gaia DR2 catalog, we show that even a single detection of a stellar occultation allows improving the object ephemeris significantly, which in turn enables predicting a future stellar occultation with high accuracy. Observational campaigns can be efficiently organized with this help, and may provide a full physical characterization of the involved object, or even the study of topographic features such as satellites or rings.
Theory-Oriented Deep Leakage from Gradients via Linear Equation Solver

In this paper, we take a theory-oriented approach to systematically study the privacy properties of gradients from a broad class of neural networks with rectified linear units (ReLU), probably the most popular activation function used in current deep learning practices. By utilizing some intrinsic properties of neural networks with ReLU, we prove the existence of exclusively activated neurons is critical to the separability of the activation patterns of different samples. Intuitively, an activation pattern is like the fingerprint of the corresponding sample during the training process. With the separated activation patterns, we for the first time show the equivalence of data reconstruction attacks with a sparse linear equation system. In practice, we propose a novel data reconstruction attack on fully-connected neural networks and extend the attack to more commercial convolutional neural network architectures. Our systematic evaluations cover more than $10$ representative neural network architectures (e.g., GoogLeNet, VGGNet and $6$ more), on various real-world scenarios related with healthcare, medical imaging, location, face recognition and shopping behaviors. In the majority of test cases, our proposed attack is able to infer ground-truth labels in the training batch with near $100\%$ accuracy, reconstruct the input data to fully-connected neural networks with lower than $10^{-6}$ MSE error, and provide better reconstruction results on both shallow and deep convolutional neural networks than previous attacks.
Provable Memorization via Deep Neural Networks using Sub-linear Parameters

It is known that $\Theta(N)$ parameters are sufficient for neural networks to memorize arbitrary $N$ input-label pairs. By exploiting depth, we show that $\Theta(N^{2/3})$ parameters suffice to memorize $N$ pairs, under a mild condition on the separation of input points. In particular, deeper networks (even with width $3$) are shown to memorize more pairs than shallow networks, which also agrees with the recent line of works on the benefits of depth for function approximation. We also provide empirical results that support our theoretical findings.
Reheating in the kination epoch via multichannel decay of gravitationally created massive scalars

We provide a detailed study of reheating in the kination regime in a scenario where the particle content is produced by gravitational production of massive scalars decaying into massless scalars and fermions which eventually reheat the Universe. A detailed calculation is given by using Boltzmann equations and decay rates obtained using formalism of quantum field theory in curved spacetime. By numerical calculations the reheating temperature is found to be in the ${10}^{9}--{10}^{13}\text{ }\text{ }\mathrm{GeV}$ regime. Moreover, the fermionic channel of decay is found to be the dominant channel of decay when the mass $m$ of the decaying particle is small raising the reheating temperature as opposed to a single scalar decay channel.
Text Editing by Command

A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints the user wishes to impose on the generated text are dynamic, especially when authoring longer documents. We address this limitation with an interactive text generation setting in which the user interacts with the system by issuing commands to edit existing text. To this end, we propose a novel text editing task, and introduce WikiDocEdits, a dataset of single-sentence edits crawled from Wikipedia. We show that our Interactive Editor, a transformer-based model trained on this dataset, outperforms baselines and obtains positive results in both automatic and human evaluations. We present empirical and qualitative analyses of this model’s performance.
Dry Matter Yield and Agronomic Performance of Herbaceous Legumes Intercropped With Napier Grass (Pennisetum Purpureum) in the Semi-Arid Areas of Eastern Amhara Region

The study was conducted at the two trial sites (Cheffa and Sirinka) of Sirinka Agricultural Research Center which represents the low and mid altitude agro ecologies of the eastern Amhara region to identify legume species that would have best compatibility with Napier grass and to examine the DM herbage yield of mixed cultures of legumes and Napier grass. Seven adaptable and recommended perennial herbaceous legume species (Desmodium unicinatum, Macrotyloma axillare, Clitoria ternatea, Macroptilium atropurpreum, Stylosanthes guianensis, Stylosanthes hamata and Medicago sativa) were intercropped with recommended Napier grass (Pennisetum purpureum) Acc. No. 14984 for the areas were included in the study. With in the three years experimental periods a total of six and four harvests, with an average harvesting interval of 135 and 206.75 days were obtained at Cheffa and Sirinka trial site, respectively. The agronomic performances of legumes among intercropped treatments and during each harvesting stages, respectively were significantly (P<0.001) different at each trial site (Cheffa and Sirinka) and at Cheffa. Only the DM yield of intercropped legumes was significantly (P<0.01) different at each location. However, the DM leaf and total (leaf + stem) yield of Napier and both (Napier + Legume) yield difference among the intercropped treatments were significant (P<0.05) at Sirinka. Hence, among the intercropped treatments highest Napier DM leaf and total (leaf + stem) yield, legume and both (Napier and legume) of (7.85, 1.39 and 9.23 t/ha) at Cheffa and (8.61, 2.15 and 10.76 t/ha) at Sirinka, respectively were obtained from treatment in which Stylosanthes guianensis was intercropped with Napier grass. The average Napier DM leaf and total yields in sole and intercropped treatments were similar at each location. As the result of climatic (RF and Maximum temperature) variations and better adaptation, the DM herbage yield responses of Napier within both sole and intercropped treatments and legumes in intercropped treatments were increases significantly (P<0.001) in advancement of harvesting stages. As the study result indicates, intercropping Napier grass with herbaceous legumes has significant advantage than growing Napier grass solely in increasing the DM yield harvested. Therefore, among the tested legumes and Napier grass combinations, intercropping Stylosanthes guianensis with Napier grass was found to be the best for its compatibility and higher Napier total (leaf + stem), legume and both (Legume + Napier) DM yields at each (Cheffa and Sirinka) trial site. Therefore, we recommend this combination for future use. However, further studies are needed to evaluate the effect of legume intercropping on herbage quality and soil fertility improvement, and the economic advantage of legumes and Napier intercropping.