Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale-Free Spanning Trees and Their Application in Genomic Epidemiology

We study the algorithmic problem of finding the most “scale-free-like” spanning tree of a connected graph. This problem is motivated by the fundamental problem of genomic epidemiology: given viral genomes sampled from infected individuals, reconstruct the transmission network (“who infected whom”). We use two possible objective functions for this problem and introduce the corresponding algorithmic problems termed m-SF (-scale free) and s-SF Spanning Tree problems. We prove that those problems are APX- and NP-hard, respectively, even in the classes of cubic and bipartite graphs. We propose two integer linear programming (ILP) formulations for the s-SF Spanning Tree problem, and experimentally assess its performance using simulated and experimental data. In particular, we demonstrate that the ILP-based approach allows for accurate reconstruction of transmission histories of several hepatitis C outbreaks.
Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning

Model-free deep reinforcement learning (RL) agents can learn an effective policy directly from repeated interactions with a black-box environment. However in practice, the algorithms often require large amounts of training experience to learn and generalize well. In addition, classic model-free learning ignores the domain information contained in the state transition tuples. Model-based RL, on the other hand, attempts to learn a model of the environment from experience and is substantially more sample efficient, but suffers from significantly large asymptotic bias owing to the imperfect dynamics model. In this paper, we propose a gradient matching algorithm to improve sample efficiency by utilizing target slope information from the dynamics predictor to aid the model-free learner. We demonstrate this by presenting a technique for matching the gradient information from the model-based learner with the model-free component in an abstract low-dimensional space and validate the proposed technique through experimental results that demonstrate the efficacy of this approach.
A Practical Index Structure Supporting Fréchet Proximity Queries Among Trajectories

We present a scalable approach for range and k nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning. Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results. We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered without any distance computations.
The Impact of the Physical Layer on the Performance of Concurrent Transmissions

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15. 4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect the performance of CT has not yet been investigated in detail. This paper fills this gap and provides the first extensive study on the impact of the physical layer on CT-based solutions using IEEE 802.15. 4 and BLE 5. We first highlight through simulation how the impact of errors induced by de-synchronization and beating on the performance of CT highly depends on the choice of the underlying physical layer. We then confirm these observations experimentally on real hardware through an analysis of the bit error distribution across received packets, unveiling possible techniques to effectively handle these errors. We further study the performance of CT-based flooding protocols in the presence of radio interference on a large-scale, and derive important insights on how the used physical layer affects their dependability.
Pro-definability of spaces of definable types

We show pro-definability of spaces of definable types in various classical complete first order theories, including complete o-minimal theories, Presburger arithmetic, p p -adically closed fields, real closed and algebraically closed valued fields and closed ordered differential fields. Furthermore, we prove pro-definability of other distinguished subspaces, some of which have an interesting geometric interpretation. Our general strategy consists of showing that definable types are uniformly definable, a property which implies pro-definability using an argument due to E. Hrushovski and F. Loeser. Uniform definability of definable types is finally achieved by studying classes of stably embedded pairs.
Studying Ranking-Incentivized Web Dynamics

The ranking incentives of many authors of Web pages play an important role in the Web dynamics. That is, authors who opt to have their pages highly ranked for queries of interest often respond to rankings for these queries by manipulating their pages; the goal is to improve the pages' future rankings. Various theoretical aspects of this dynamics have recently been studied using game theory. However, empirical analysis of the dynamics is highly constrained due to lack of publicly available datasets. We present an initial such dataset that is based on TREC's ClueWeb09 dataset. Specifically, we used the WayBack Machine of the Internet Archive to build a document collection that contains past snapshots of ClueWeb documents which are highly ranked by some initial search performed for ClueWeb queries. Temporal analysis of document changes in this dataset reveals that findings recently presented for small-scale controlled ranking competitions between documents' authors also hold for Web data. Specifically, documents' authors tend to mimic the content of documents that were highly ranked in the past, and this practice can result in improved ranking.
Commercial manufacturing scale formulation and analytical characterization of therapeutic recombinant antibodies

Stable therapeutic antibody dosage forms present production technology challenges, particularly when high‐concentration formulations are needed to meet the elevated dose requirements that are generally required for successful antibody therapy. Solid dosage forms, such as lyophilized powders, are generally more stable than liquid formulations. High‐concentration drug products can be achieved by reconstitution of the lyophilisate in a smaller volume than its initial (pre‐lyophilization) volume, but requires a significant vial overfill. High‐concentration liquid formulations are becoming feasible as new techniques and technologies become available. Analytical methods to detect subtle molecular variations have been developed to demonstrate manufacturing consistency. Some molecular heterogeneity is contributed by conserved sites, such as Asn297 glycosylation and the loss of heavy chain C‐terminal Lys residues. Characteristics that affect potency, stability, or immunogenicity must be elucidated for each therapeutic antibody. Drug Dev. Res. 61:137–154, 2004. © 2004 Wiley‐Liss, Inc.
On the Limits of Parallelizing Convolutional Neural Networks on GPUs

GPUs are currently the platform of choice for training neural networks. However, training a deep neural network (DNN) is a time-consuming process even on GPUs because of the massive number of parameters that have to be learned. As a result, accelerating DNN training has been an area of significant research in the last couple of years. While earlier networks such as AlexNet had a linear dependency between layers and operations, state-of-the-art networks such as ResNet, PathNet, and GoogleNet have a non-linear structure that exhibits a higher level of inter-operation parallelism. However, popular deep learning (DL) frameworks such as TensorFlow and PyTorch launch the majority of neural network operations, especially convolutions, serially on GPUs and do not exploit this inter-op parallelism. In this brief announcement, we make a case for the need and potential benefit of exploiting this rich parallelism in state-of-the-art non-linear networks for reducing the training time. We identify the challenges and limitations in enabling concurrent layer execution on GPU backends (such as cuDNN) of DL frameworks and propose potential solutions.
Inferring signaling pathways with probabilistic programming

Abstract Motivation Cells regulate themselves via dizzyingly complex biochemical processes called signaling pathways. These are usually depicted as a network, where nodes represent proteins and edges indicate their influence on each other. In order to understand diseases and therapies at the cellular level, it is crucial to have an accurate understanding of the signaling pathways at work. Since signaling pathways can be modified by disease, the ability to infer signaling pathways from condition- or patient-specific data is highly valuable. A variety of techniques exist for inferring signaling pathways. We build on past works that formulate signaling pathway inference as a Dynamic Bayesian Network structure estimation problem on phosphoproteomic time course data. We take a Bayesian approach, using Markov Chain Monte Carlo to estimate a posterior distribution over possible Dynamic Bayesian Network structures. Our primary contributions are (i) a novel proposal distribution that efficiently samples sparse graphs and (ii) the relaxation of common restrictive modeling assumptions. Results We implement our method, named Sparse Signaling Pathway Sampling, in Julia using the Gen probabilistic programming language. Probabilistic programming is a powerful methodology for building statistical models. The resulting code is modular, extensible and legible. The Gen language, in particular, allows us to customize our inference procedure for biological graphs and ensure efficient sampling. We evaluate our algorithm on simulated data and the HPN-DREAM pathway reconstruction challenge, comparing our performance against a variety of baseline methods. Our results demonstrate the vast potential for probabilistic programming, and Gen specifically, for biological network inference. Availability and implementation Find the full codebase at https://github.com/gitter-lab/ssps. Supplementary information Supplementary data are available at Bioinformatics online.
Looking back to lower-level information in few-shot learning

Humans are capable of learning new concepts from small numbers of examples. In contrast, supervised deep learning models usually lack the ability to extract reliable predictive rules from limited data scenarios when attempting to classify new examples. This challenging scenario is commonly known as few-shot learning. Few-shot learning has garnered increased attention in recent years due to its significance for many real-world problems. Recently, new methods relying on meta-learning paradigms combined with graph-based structures, which model the relationship between examples, have shown promising results on a variety of few-shot classification tasks. However, existing work on few-shot learning is only focused on the feature embeddings produced by the last layer of the neural network. The novel contribution of this paper is the utilization of lower-level information to improve the meta-learner performance in few-shot learning. In particular, we propose the Looking-Back method, which could use lower-level information to construct additional graphs for label propagation in limited data settings. Our experiments on two popular few-shot learning datasets, miniImageNet and tieredImageNet, show that our method can utilize the lower-level information in the network to improve state-of-the-art classification performance.
El género Perinereis (Annelida, Polychaeta, Nereidae) en Chile

Summary The present work is a systematic study of the species oi Perinereis in Chile. A new species, P. longidonta n.sp. is described. The characterizations were based on material collected by the authors (P. falklandica, P. longidonta n.sp., P. nuntia var. vallata) and specimens deposited in the U.S. National Museum, Smithsonian Institution, collected in Easter Island and Juan Fernandez archipelago (P. helleri, P. camiguinoides). The characterization of P. pseudocamiguina is taken from the literature, since specimens were not available. P. gualpensis Jeldes 1963 is probably related to P. nuntia var. vallata but it was not possible to compare them due to lack of material. A key for the 6 valid species of Perinereis in Chile is included: P. camiguinoides, P. falklandica, P. helleri, P. longidonta n.sp. , P. nuntia var. vallata, and P. pseudocamiguina.
Detecting Scatteredly-Distributed, Small, andCritically Important Objects in 3D OncologyImaging via Decision Stratification

Finding and identifying scatteredly-distributed, small, and critically important objects in 3D oncology images is very challenging. We focus on the detection and segmentation of oncology-significant (or suspicious cancer metastasized) lymph nodes (OSLNs), which has not been studied before as a computational task. Determining and delineating the spread of OSLNs is essential in defining the corresponding resection/irradiating regions for the downstream workflows of surgical resection and radiotherapy of various cancers. For patients who are treated with radiotherapy, this task is performed by experienced radiation oncologists that involves high-level reasoning on whether LNs are metastasized, which is subject to high inter-observer variations. In this work, we propose a divide-and-conquer decision stratification approach that divides OSLNs into tumor-proximal and tumor-distal categories. This is motivated by the observation that each category has its own different underlying distributions in appearance, size and other characteristics. Two separate detection-by-segmentation networks are trained per category and fused. To further reduce false positives (FP), we present a novel global-local network (GLNet) that combines high-level lesion characteristics with features learned from localized 3D image patches. Our method is evaluated on a dataset of 141 esophageal cancer patients with PET and CT modalities (the largest to-date). Our results significantly improve the recall from $45\%$ to $67\%$ at $3$ FPs per patient as compared to previous state-of-the-art methods. The highest achieved OSLN recall of $0.828$ is clinically relevant and valuable.
Trapping sheep blowflies with lures based on bacterial cultures

Summary. Baits of Proteus mirabilis cultured in a commercial medium or in a gut mucus mixture were tested in New Zealand sheep pasture for their ability to trap sheep strike flies. Liver–sodium sulfide baits were used for comparison. At the start of the flystrike season, medium cultures were as effective as liver–sodium sulfide baits in trapping flies. As the season progressed, the liver–sodium sulfide mixture trapped a significantly higher number of Lucilia cuprina and Calliphora stygia than medium cultures, though they trapped significantly fewer Chrysomya rufifacies. In one case the medium culture trapped a significantly higher proportion of gravid L. cuprina than the liver–sodium sulfide mixture. Adding dimethyl sulfoxide to the medium culture significantly reduced its efficacy. The gut mucus culture was significantly less effective than the liver–sodium sulfide bait in trapping gravid L. cuprina, and significantly more effective in trapping Chrysomya rufifacies. This is the first published record of sheep strike flies being attracted by bacterial odours in the field.
Subspace Newton Method for Sparse SVM

Kernel-based methods for support vector machines (SVM) have seen a great advantage in various applications. However, they may incur prohibitive computational costs when the involved sample size is on a large scale. Therefore, reducing the number of support vectors (or say sample reduction) appears to be necessary, which gives rise to the topic of the sparse SVM. Motivated by this, we aim at solving a sparsity constrained kernel SVM optimization, which is capable of controlling the number of the support vectors. Based on the established optimality conditions associated with the stationary equations, a subspace Newton method is cast to tackle the sparsity constrained problem and enjoys one-step convergence property if the starting point is close to a local region of a stationary point, leading to a super-fast computational speed. Numerical comparisons with some other excellent solvers demonstrate that the proposed method performs exceptionally well, especially for datasets with large numbers of samples, in terms of a much fewer number of support vectors and shorter computational time.
Dual Message Passing Neural Network for Molecular Property Prediction

is crucial for real applications like molecular design and drug discovery. Our extensive experimental evaluations demonstrate that DualMPNN achieves remarkably superior improvement over the state-of-the-art approaches on a variety of challenging benchmarks. Meanwhile, the visualization of the self-attention demonstrates how different atoms affect the prediction performance, which brings in the interpretability of deep learning models out of the black-box.
P2B: Point-to-Box Network for 3D Object Tracking in Point Clouds

Towards 3D object tracking in point clouds, a novel point-to-box network termed P2B is proposed in an end-to-end learning manner. Our main idea is to first localize potential target centers in 3D search area embedded with target information. Then point-driven 3D target proposal and verification are executed jointly. In this way, the time-consuming 3D exhaustive search can be avoided. Specifically, we first sample seeds from the point clouds in template and search area respectively. Then, we execute permutation-invariant feature augmentation to embed target clues from template into search area seeds and represent them with target-specific features. Consequently, the augmented search area seeds regress the potential target centers via Hough voting. The centers are further strengthened with seed-wise targetness scores. Finally, each center clusters its neighbors to leverage the ensemble power for joint 3D target proposal and verification. We apply PointNet++ as our backbone and experiments on KITTI tracking dataset demonstrate P2B’s superiority (~10%’s improvement over state-of-the-art). Note that P2B can run with 40FPS on a single NVIDIA 1080Ti GPU. Our code and model are available at https://github.com/HaozheQi/P2B.
Genetic divergence, female choice and male mating success in trinidadian guppies

Guppy, Poecilia reticulata, populations from two major Trinidadian drainages, the Caroni and Oropuche, are characterised by high levels of genetic divergence. Our aim in this paper was to determine whether this divergence is linked to behaviourally-based reproductive isolation. We compared two populations of guppies, one from the Tacarigua River in the Caroni drainage, the other from the Oropuche River in the Oropuche drainage. Guppies in both sites are subject to predation from the pike cichlid, Crenicichla alta, and other predators. In visual choice tests, virgin females from both the Oropuche and Tacarigua populations showed no preference for either type of male. This result was confirmed when females had free access to males. However, a population asymmetry in male mating behaviour resulted in Tacarigua males gaining virtually all copulations. We argue that predation risk has constrained female choice and discuss the evolutionary significance of population differences in male behaviour.
Nutrition, IBD and Gut Microbiota: A Review

Inflammatory bowel disease (IBD) is a chronic relapsing–remitting systemic disease of the gastrointestinal tract, characterized by an inflammatory process that requires lifelong treatment. The underlying causes of IBD are still unclear, as this heterogeneous disorder results from a complex interplay between genetic variability, the host immune system and environmental factors. The current knowledge recognizes diet as a risk factor for the development of IBD and attributes a substantial pathogenic role to the intestinal dysbiosis inducing an aberrant mucosal immune response in genetically predisposed individuals. This review focused on the clinical evidence available that considers the impact of some nutrients on IBD onset and the role of different diets in the management of IBD and their effects on the gut microbiota composition. The effects of the Specific Carbohydrate Diet, low fermentable oligosaccharides, disaccharides, monosaccharides and polyols (FODMAP) diet, gluten free diet, anti-inflammatory diet and Mediterranean diet are investigated with regard to their impact on microbiota and on the evolution of the disease. At present, no clear indications toward a specific diet are available but the assessment of dysbiosis prior to the recommendation of a specific diet should become a standard clinical approach in order to achieve a personalized therapy.
Evaluation of the Anti-apoptotic and Anti-cytotoxic Effect of Epicatechin Gallate and Edaravone on SH-SY5Y Neuroblastoma Cells

Introduction: Parkinson disease (PD) is the second most common neurodegenerative disease affecting older individuals with signs of motor disability and cognitive impairment. Epicatechin (EC) and edaravone have neuroprotective effects most probably due to their antioxidant activity; however, a limited number of studies have considered their role in PD. This research aimed at investigating the neuroprotective effect of EC and edaravone in a neurotoxin-induced model of PD. Methods: An in vitro model of PD was made by subjecting SH-SY5Y neuroblastoma cells to neurotoxin: 6-hydroxydopamine (6-OHDA) 100 μM/well. The cytoprotective effect of EC and edaravone in five concentrations on cell viability was tested using the MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) assay. The apoptotic assay was done by annexin V and propidium iodide method using flow cytometry. Results: According to the MTT assay analysis, EC and edaravone had protective effects against 6-OH DA-induced cytotoxicity in SH-SY5Y neuroblastoma cells that were much more significant for edaravone and also a relative synergistic effect between EC and edaravone was observed. The apoptotic analysis showed that edaravone alone could decrease early and late apoptosis, whereas EC diminished early apoptosis, but enhanced late apoptosis and necrosis. Besides, co-treatment of edaravone and EC had a synergistic effect on decreasing apoptosis and increasing cell viability. Conclusion: The protective effect of edaravone on apoptosis and cytotoxicity was demonstrated clearly and EC had a synergistic effect with edaravone.