Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Reference Curation for Visual Place Recognition Via Bayesian Selective Fusion

A key challenge in visual place recognition (VPR) is recognizing places despite drastic visual appearance changes due to factors such as time of day, season, weather or lighting conditions. Numerous approaches based on deep-learnt image descriptors, sequence matching, domain translation, and probabilistic localization have had success in addressing this challenge, but most rely on the availability of carefully curated representative reference images of the possible places. In this letter, we propose a novel approach, dubbed Bayesian Selective Fusion, for actively selecting and fusing informative reference images to determine the best place match for a given query image. The selective element of our approach avoids the counterproductive fusion of every reference image and enables the dynamic selection of informative reference images in environments with changing visual conditions (such as indoors with flickering lights, outdoors during sunshowers or over the day-night cycle). The probabilistic element of our approach provides a means of fusing multiple reference images that accounts for their varying uncertainty via a novel training-free likelihood function for VPR. On difficult query images from two benchmark datasets, we demonstrate that our approach matches and exceeds the performance of several alternative fusion approaches along with state-of-the-art techniques that are provided with prior (unfair) knowledge of the best reference images. Our approach is well suited for long-term robot autonomy where dynamic visual environments are commonplace since it is training-free, descriptor-agnostic, and complements existing techniques such as sequence matching.
Exercise-related genes analysis of Mongolian Horse

The Mongolian horses, as a neglected scientific resource, have excellent endurance and stress resistance to adapt to the cold and harsh plateau conditions. Intraspecific genetic diversity is mainly embodied in various genetic advantages of different branches of Mongolian horse. Abaga horse is better than Wushen horse in running speed, for example. Because people pay progressively attention to the athletic performance of horse, such as horse racing in Mongolia’s Naadam festival, we expect to guide the exercise-oriented breeding of horses through genomics research. We obtained the clean data of 630,535,376,400 bp through the entire genome second-generation sequencing for the whole blood of 4 Abaga horses and 10 Wushen horses. Based on the data analysis of single nucleotide polymorphism (SNP), we severally detected that 479 and 943 positively selected genes, particularly exercise-related, were mainly enriched on equine chromosome 4 in Abaga horses and Wushen horses, which implied that the chromosome 4 may be associated with the evolution of the Mongolian horse and athletic performance. Four hundred and forty genes of positive selection were enriched in 12 exercise-related pathways and narrowed in 21 exercise-related genes in Abaga horse, which were distinguished from Wushen horse. So, we speculated that the Abaga horse may have oriented genes for the motorial mechanism and 21 exercise-related genes also provided molecular genetic basis for exercise-directed breeding of Mongolian horse.
DeepAveragers: Offline Reinforcement Learning by Solving Derived Non-Parametric MDPs

We study an approach to offline reinforcement learning (RL) based on optimally solving finitely-represented MDPs derived from a static dataset of experience. This approach can be applied on top of any learned representation and has the potential to easily support multiple solution objectives as well as zero-shot adjustment to changing environments and goals. Our main contribution is to introduce the Deep Averagers with Costs MDP (DAC-MDP) and to investigate its solutions for offline RL. DAC-MDPs are a non-parametric model that can leverage deep representations and account for limited data by introducing costs for exploiting under-represented parts of the model. In theory, we show conditions that allow for lower-bounding the performance of DAC-MDP solutions. We also investigate the empirical behavior in a number of environments, including those with image-based observations. Overall, the experiments demonstrate that the framework can work in practice and scale to large complex offline RL problems.
Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning

Navigating fluently around pedestrians is a necessary capability for mobile robots deployed in human environments, such as buildings and homes. While research on social navigation has focused mainly on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors and doorways that limit maneuverability and influence patterns of pedestrian interaction. We present an approach based on reinforcement learning (RL) to learn policies capable of dynamic adaptation to the presence of moving pedestrians while navigating between desired locations in constrained environments. The policy network receives guidance from a motion planner that provides waypoints to follow a globally planned trajectory, whereas RL handles the local interactions. We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to more complex unseen layouts that exhibit composition of the structural elements available during training. Going beyond walls-world like domains, we show transfer of the learned policy to unseen 3D reconstructions of two real environments. These results support the applicability of the compositional principle to navigation in real-world buildings and indicate promising usage of multi-agent simulation within reconstructed environments for tasks that involve interaction. https://ai.stanford. edu/∼cdarpino/socialnavconstrained/
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism

Data Parallelism (DP) and Model Parallelism (MP) are two common paradigms to enable large-scale distributed training of neural networks. Recent trends, such as the improved model performance of deeper and wider neural networks when trained with billions of data points, have prompted the use of hybrid parallelism---a paradigm that employs both DP and MP to scale further parallelization for machine learning. Hybrid training allows compute power to increase, but it runs up against the key bottleneck of communication overhead that hinders scalability. In this paper, we propose a compression framework called Dynamic Communication Thresholding (DCT) for communication-efficient hybrid training. DCT filters the entities to be communicated across the network through a simple hard-thresholding function, allowing only the most relevant information to pass through. For communication efficient DP, DCT compresses the parameter gradients sent to the parameter server during model synchronization, while compensating for the introduced errors with known techniques. For communication efficient MP, DCT incorporates a novel technique to compress the activations and gradients sent across the network during the forward and backward propagation, respectively. This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data. Under modest assumptions, we show that the convergence of training is maintained with DCT. We evaluate DCT on natural language processing and recommender system models. DCT reduces overall communication by 20x, improving end-to-end training time on industry scale models by 37%. Moreover, we observe an improvement in the trained model performance, as the induced sparsity is possibly acting as an implicit sparsity based regularization.
DIETAS DE ALTA PROPORÇÃO DE CONCENTRADO PARA BOVINOS DE CORTE CONFINADOS

O objetivo deste trabalho foi de avaliar os efeitos de dietas de alta proporcao de concentrado, sobre os desempenhos e caracteristicas de carcaca, comportamento ingestivo e indicadores fecais de 20 bovinos Nelore, com idade de 28 meses. As dietas DT+BIN (10% de bagaco de cana in natura , 54,52% de sorgo moido, 10,94% de caroco de algodao, 18% de casca de soja, 2,54% de farelo de soja e 4% de nucleo farelado), MGI (10% de casca de soja, 75% de milho grao e 15% de nucleo peletizado) e DT (44,44% de sorgo moido, 16,70% de caroco de algodao, 28,89% de casca de soja e 10% de nucleo farelado) foram distribuidas aos animais em delineamento inteiramente casualizado em baias individuais. O consumo das dietas e dos nutrientes, foi determinado mediante diferencas entre o oferecido e as sobras. As dietas, depois de pesadas, foram distribuidas aos animais na forma de mistura total em duas refeicoes diarias, sendo 60% do total as 8h e 40% do total as 17h, permitindo-se sobras de aproximadamente 5% do ofertado. As pesagens dos animais foram realizadas no inicio do periodo experimental e a cada 21 dias, sempre em jejum hidrico e alimentar de 14 horas. Foram mensurados o pH e os comprimentos das meias-carcacas no momento da saida das câmaras, apos o processo de resfriamento. Da meia-carcaca esquerda foram medidas AOL, EGSO a altura da 12a costela. No registro do tempo gasto em alimentacao, ruminacao, ocio e ingestao de agua foram adotadas observacoes visuais dos animais a cada cinco minutos, por quatro periodos integrais de 24 horas. Para determinacao do pH das fezes e do amido fecal, amostras de fezes foram coletadas do reto de cada bovino. Houve diferenca significativa (P 0,05). Os tempos de ocios e ingestoes de agua diferiram entre si, com valores de 436,80 e 4,46 min/dia para o tratamento DT+BIN, 546,0 e 2,81 min/dia para o tratamento MGI e 568,80 e 4,02 min/dia para o tratamento DT. Os valores medios em percentagem do amido fecal, materia seca fecal, medida de pH para o local de fermentacao do amido e consumo de amido/kg nao foram influenciados (P>0,05) pelos tratamentos. O escore de consistencia fecal e FDN das fezes foram influenciados (P<0,05) pelos tratamentos com valores de escore e FDN fecal de 3,12 e 46,29% para o tratamento DT+BIN, 2,92 e 35,65% para o tratamento MGI e 3,20 e 48,97% para o tratamento DT. A adicao de 10% de BIN em dieta totalmente concentrada, promove melhoria no consumo de MS, g/kg de PV, % de MS em relacao ao PV, maior peso e acabamento de carcaca. A dieta de alto grao (MGI) foi superior a dieta de alta proporcao de concentrado (DT) para a caracteristica de ganho de peso final aos 84 dias e peso de carcaca quente. O tempo gasto com ruminacao, mastigacao e a eficiencia de alimentacao (g FDN/h) e (g de FDNfe/h) foi mais alto para a dieta com adicao de BIN. A consistencia de fezes avaliada nos bovinos do tratamento DT+BIN com 90% de concentrado e 10% de BIN classificada como firme evidencia que estes animais tiveram uma melhor fermentacao ruminal
Human Colonization of Asia in the Late Pleistocene

The origin and evolution of modern humans continues to be of great interest to the scientific and public communities alike; the field has long been dominated by findings from Europe and Africa. With new discoveries of hominin fossils and archaeological sites, in addition to the application of recent genomic and paleoclimate modeling studies, findings from Asia are serving to revolutionize the field of modern human origins. The identification of Neanderthals and Denisovans in Siberia, for example, along with growing fossil and archaeological evidence for the presence of early modern humans in East and Southeast Asia, much earlier than originally thought, places the spotlight on the evolutionary history of our species in Asia over the last 125,000 years. Exciting and unanticipated new discoveries call for a need to critically reexamine the Asian record. Taking a multidisciplinary perspective, a group of active researchers participated in a week-long Wenner-Gren symposium titled “Human Colonization of Asia in the Late Pleistocene” (#153) in March 2016. Here we review the major themes and findings emerging from the symposium and discuss avenues to move the field forward.
Frequent translation of small open reading frames in evolutionary conserved lncRNA regions

The mammalian transcriptome includes thousands of transcripts that do not correspond to annotated protein-coding genes. Although many of these transcripts show homology between human and mouse, only a small proportion of them have been functionally characterized. Here we use ribosome profiling data to identify translated open reading frames, as well as non-ribosomal protein-RNA interactions, in evolutionary conserved and non-conserved transcripts. We find that conserved regions are subject to significant evolutionary constraints and are enriched in translated open reading frames, as well as non-ribosomal protein-RNA interaction signatures, when compared to non-conserved regions. Translated ORFs can be divided in two classes, those encoding functional micropeptides and those that show no evidence of protein functionality. This study underscores the importance of combining evolutionary and biochemical measurements to advance in a more complete understanding of the transcriptome.
Evaluation of carotenoid biosynthesis, accumulation and antioxidant activities in sweetcorn (Zea mays L.) during kernel development

Summary To have a better understanding of regulatory mechanism of carotenoid accumulation in sweetcorn (Zea mays L.), we analysed carotenoid profiles, expression patterns of carotenogenic genes and antioxidant activities in two genotypes of sweetcorn during kernel development. The results suggested that the expression of carotenogenic genes played an essential role in carotenoid accumulation and higher expression levels of carotenogenic genes obviously led to higher levels of total carotenoids. It was found that the expression of upstream genes (ZmPDS, ZmZDS and ZmCRTISO) had major influence on the content of total carotenoid during kernel development. The accumulation of lycopene was closely related to the coordination of upstream and downstream genes. The expression of ZmHYD and ZmZEP was influential on zeaxanthin accumulation, which was also influenced by the rate of ZmCHYB/ZmCHYE. Expression levels of ZmCHYE were still stable in order for α-xanthophylls largely accumulated during kernel development. Lutein, zeaxanthin, α-cryptoxanthin and β-cryptoxanthin constituted the predominant carotenoids in sweetcorn. Two essential stages for carotenoid accumulation were found in sweetcorn during kernel development.
Control of Lineage Decisions in the Embryonic Kidney by Hedgehog Signalling

Foxd1-expressing cells give rise to stromal components in the mammalian embryonic kidney, and are the major source of myofibroblasts during renal fibrosis. The signalling pathways involved in segregation of the stromal and nephrogenic lineages from their common precursor cells is not fully understood. Previous studies in the Rosenblum lab have shown the role of HH signaling in expansion of cortical stromal gene expression and ureteropelvic junction development. I present a stage-specific role for HH signalling in early renal precursors expressing Sall1 and Osr1 in promoting stromal cell specification without affecting the nephrogenic lineage. Temporal deletion of Ptc1 in Sall1+ and Osr1+ cells results in an early increase in stromal cells, disorganization of the cortical stroma, medullary localization of cortical stromal cells, decreased nephron number, and no change in cell proliferation. Mutant kidneys exhibit UPJ obstruction at birth caused by ectopic cortical stromal cells in an activated state of HH signalling.
Fair Valuation of Guaranteed Contracts: The Interaction between Assets and Liabilities

In this paper we study the investment behavior of an insurance company consisting of policy and equity holders and issuing fair valued guaranteed contracts. Building up on the work of Briys and De Varenne (1997) and De Munnik and Schotman (1994) an intertemporal stochastic asset model and an interest rate model are specified and a procedure to estimate the required parameters is developed. Next, simulation is used to generate risk/return profiles for different investment portfolios and various guaranteed return contracts. It is shown that hedging the guaranteed return contracts is of a primary importance to derive attractive risk/return profiles for both policyholders and the equity holders.
Metabarcoding of PalEnDNA as An Efficient Tool to Recover Ancient Bacterial Diversity

Abstract Past bacterial diversity of a paleosol was reconstructed using metabarcoding of paleo environmental DNA (PalEnDNA). The paleosol was subsampled from a sediment core which was excavated from a palaeo beach-ridge located 2.6 km away from present sea shore and identified that it was deposited under marine influence ∼6000 years ago, using geological proxies. The bacterial community contained 37 bacterial phyla and dominated by Proteobacteria, followed by Bacteroidetes, Firmicutes, Actinobacteria, Verrucomicrobia, and Chloroflexi. The bacterial community was a mix-up of marine and terrestrial population, and thereby diversity was higher than marine populations. The result shows metabarcoding of PalEnDNA can effectively reconstruct past bacterial community structure.
Distortion-Aware Monocular Depth Estimation for Omnidirectional Images

Image distortion is a main challenge for tasks on panoramas. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) network to estimate dense depth maps from indoor panoramas. First, we introduce a distortion-aware module to extract semantic features from omnidirectional images. Specifically, we exploit deformable convolution to adjust its sampling grids to geometric distortions on panoramas. We also utilize a strip pooling module to sample against horizontal distortion introduced by inverse gnomonic projection. Second, we introduce a plug-and-play spherical-aware weight matrix for our loss function to handle the uneven distribution of areas projected from a sphere. Experiments on the 360D dataset show that the proposed method can effectively extract semantic features from distorted panoramas and alleviate the supervision bias caused by distortion. It achieves the state-of-the-art performance on the 360D dataset with high efficiency.
Generating Fact Checking Summaries for Web Claims

We present SUMO, a neural attention-based approach that learns to establish correctness of textual claims based on evidence in the form of text documents (e.g., news articles or web documents). SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim. Prior approaches to address the problem of fact checking and evidence extraction have relied on simple concatenation of claim and document word embeddings as an input to claim driven attention weight computation. This is done so as to extract salient words and sentences from the documents that help establish the correctness of the claim. However this design of claim-driven attention fails to capture the contextual information in documents properly. We improve on the prior art by using improved claim and title guided hierarchical attention to model effective contextual cues. We show the efficacy of our approach on political, healthcare, and environmental datasets.
Maximal Couplings of the Metropolis-Hastings Algorithm

Couplings play a central role in the analysis of Markov chain Monte Carlo algorithms and appear increasingly often in the algorithms themselves, e.g. in convergence diagnostics, parallelization, and variance reduction techniques. Existing couplings of the Metropolis-Hastings algorithm handle the proposal and acceptance steps separately and fall short of the upper bound on one-step meeting probabilities given by the coupling inequality. This paper introduces maximal couplings which achieve this bound while retaining the practical advantages of current methods. We consider the properties of these couplings and examine their behavior on a selection of numerical examples.
Sliding-Window QPS (SW-QPS)

In this work, we first propose a parallel batch switching algorithm called Small-Batch Queue-Proportional Sampling (SB-QPS). Compared to other batch switching algorithms, SB-QPS significantly reduces the batch size without sacrificing the throughput performance and hence has much lower delay when traffic load is light to moderate. It also achieves the lowest possible time complexity of O(1) per matching computation per port, via parallelization. We then propose another algorithm called Sliding-Window QPS (SW-QPS). SW-QPS retains and enhances all benefits of SB-QPS, and reduces the batching delay to zero via a novel switching framework called sliding-window switching. In addition, SW-QPS computes matchings of much higher qualities, as measured by the resulting throughput and delay performances, than QPS-1, the state-of-the-art regular switching algorithm that builds upon the same underlying bipartite matching algorithm.
Combinatorics and Geometry for the Many-ported, Distributed and Shared Memory Architecture

Manycore SoC architectures based on on-chip shared memory are preferred for flexible and programmable solutions in many application domains. However, the development of many ported memory is becoming increasingly challenging as we approach the end of Moores Law while systems requirements demand larger shared memory and more access ports. Memory can no longer be designed simply to minimize single transaction access time, but must take into account the functionality on the SoC. In this paper we examine a common large memory usage in SoC, where the memory is used as storage for large buffers that are then moved for time scheduled processing. We merge two aspects of many ported memory design, combinatorial analysis of interconnect, and geometric analysis of critical paths, extending both to show that in this case the SoC performance benefits significantly from a hierarchical, distributed and staged architecture with lower-radix switches and fractal randomization of memory bank addressing, along with judicious and geometry aware application of speed up. The results presented show the new architecture supports 20% higher throughput with 20% lower latency and 30% less interconnection area at approximately the same power consumption. We demonstrate the flexibility and scalability of this architecture on silicon from a physical design perspective by taking the design through layout. The architecture enables a much easier implementation flow that works well with physically irregular port access and memory dominant layout, which is a common issue in real designs.
Neuralizing Efficient Higher-order Belief Propagation

Graph neural network models have been extensively used to learn node representations for graph structured data in an end-to-end setting. These models often rely on localized first order approximations of spectral graph convolutions and hence are unable to capture higher-order relational information between nodes. Probabilistic Graphical Models form another class of models that provide rich flexibility in incorporating such relational information but are limited by inefficient approximate inference algorithms at higher order. In this paper, we propose to combine these approaches to learn better node and graph representations. First, we derive an efficient approximate sum-product loopy belief propagation inference algorithm for higher-order PGMs. We then embed the message passing updates into a neural network to provide the inductive bias of the inference algorithm in end-to-end learning. This gives us a model that is flexible enough to accommodate domain knowledge while maintaining the computational advantage. We further propose methods for constructing higher-order factors that are conditioned on node and edge features and share parameters wherever necessary. Our experimental evaluation shows that our model indeed captures higher-order information, substantially outperforming state-of-the-art $k$-order graph neural networks in molecular datasets.