225 research outputs found
Restorative urbanism: redefining socio-ecosystems in Manila
This thesis explores coastal cities and the narrow zone lying between their developing urban areas and the waters of the open sea. Worldwide, there are more than 14 cities with populations over 10 million are situated along endangered coastlines. The fact that many of these continue to experience rapid growth, are economic centers of international importance, and are increasingly vulnerable to the impacts of climate change, is a matter of global concern. In this work I examine the implications and possibilities of restoring urban coastline defenses and ask how I can learn from past efforts while adapting such strategies to address other issues of pressing concern in such cities – issues such as social and economic inequities; affordable housing; food supplies; maintaining coastal livelihoods; management of waste; and equitable access to clean water
htSNPer1.0: software for haplotype block partition and htSNPs selection
BACKGROUND: There is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. RESULTS: A heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm. CONCLUSION: htSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
Retrieval-augmented language models (RALMs) represent a substantial
advancement in the capabilities of large language models, notably in reducing
factual hallucination by leveraging external knowledge sources. However, the
reliability of the retrieved information is not always guaranteed. The
retrieval of irrelevant data can lead to misguided responses, and potentially
causing the model to overlook its inherent knowledge, even when it possesses
adequate information to address the query. Moreover, standard RALMs often
struggle to assess whether they possess adequate knowledge, both intrinsic and
retrieved, to provide an accurate answer. In situations where knowledge is
lacking, these systems should ideally respond with "unknown" when the answer is
unattainable. In response to these challenges, we introduces Chain-of-Noting
(CoN), a novel approach aimed at improving the robustness of RALMs in facing
noisy, irrelevant documents and in handling unknown scenarios. The core idea of
CoN is to generate sequential reading notes for retrieved documents, enabling a
thorough evaluation of their relevance to the given question and integrating
this information to formulate the final answer. We employed ChatGPT to create
training data for CoN, which was subsequently trained on an LLaMa-2 7B model.
Our experiments across four open-domain QA benchmarks show that RALMs equipped
with CoN significantly outperform standard RALMs. Notably, CoN achieves an
average improvement of +7.9 in EM score given entirely noisy retrieved
documents and +10.5 in rejection rates for real-time questions that fall
outside the pre-training knowledge scope.Comment: Preprin
TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system
Recently, deep learning has been an area of intense research. However, as a
kind of computing-intensive task, deep learning highly relies on the scale of
GPU memory, which is usually prohibitive and scarce. Although there are some
extensive works have been proposed for dynamic GPU memory management, they are
hard to be applied to systems with multiple dynamic workloads, such as
in-database machine learning systems.
In this paper, we demonstrated TENSILE, a method of managing GPU memory in
tensor granularity to reduce the GPU memory peak, considering the multiple
dynamic workloads. TENSILE tackled the cold-starting and across-iteration
scheduling problem existing in previous works. We implement TENSILE on a deep
learning framework built by ourselves and evaluated its performance. The
experiment results show that TENSILE can save more GPU memory with less extra
time overhead than prior works in both single and multiple dynamic workloads
scenarios
Duet: efficient and scalable hybriD neUral rElation undersTanding
Learned cardinality estimation methods have achieved high precision compared
to traditional methods. Among learned methods, query-driven approaches face the
data and workload drift problem for a long time. Although both query-driven and
hybrid methods are proposed to avoid this problem, even the state-of-the-art of
them suffer from high training and estimation costs, limited scalability,
instability, and long-tailed distribution problem on high cardinality and
high-dimensional tables, which seriously affects the practical application of
learned cardinality estimators. In this paper, we prove that most of these
problems are directly caused by the widely used progressive sampling. We solve
this problem by introducing predicates information into the autoregressive
model and propose Duet, a stable, efficient, and scalable hybrid method to
estimate cardinality directly without sampling or any non-differentiable
process, which can not only reduces the inference complexity from O(n) to O(1)
compared to Naru and UAE but also achieve higher accuracy on high cardinality
and high-dimensional tables. Experimental results show that Duet can achieve
all the design goals above and be much more practical and even has a lower
inference cost on CPU than that of most learned methods on GPU
Ruthenium nanoclusters modified by zinc species towards enhanced electrochemical hydrogen evolution reaction
Ruthenium (Ru) has been considered a promising electrocatalyst for electrochemical hydrogen evolution reaction (HER) while its performance is limited due to the problems of particle aggregation and competitive adsorption of the reaction intermediates. Herein, we reported the synthesis of a zinc (Zn) modified Ru nanocluster electrocatalyst anchored on multiwalled carbon nanotubes (Ru-Zn/MWCNTs). The Ru-Zn catalysts were found to be highly dispersed on the MWCNTs substrate. Moreover, the Ru-Zn/MWCNTs exhibited low overpotentials of 26 and 119 mV for achieving current intensities of 10 and 100 mA cm−2 under alkaline conditions, respectively, surpassing Ru/MWCNTs with the same Ru loading and the commercial 5 wt% Pt/C (47 and 270 mV). Moreover, the Ru-Zn/MWCNTs showed greatly enhanced stability compared to Ru/MWCNTs with no significant decay after 10,000 cycles of CV sweeps and long-term operation for 90 h. The incorporation of Zn species was found to modify the electronic structure of the Ru active species and thus modulate the adsorption energy of the Had and OHad intermediates, which could be the main reason for the enhanced HER performance. This study provides a strategy to develop efficient and stable electrocatalysts towards the clean energy conversion field
Human Performance Modeling and Rendering via Neural Animated Mesh
We have recently seen tremendous progress in the neural advances for
photo-real human modeling and rendering. However, it's still challenging to
integrate them into an existing mesh-based pipeline for downstream
applications. In this paper, we present a comprehensive neural approach for
high-quality reconstruction, compression, and rendering of human performances
from dense multi-view videos. Our core intuition is to bridge the traditional
animated mesh workflow with a new class of highly efficient neural techniques.
We first introduce a neural surface reconstructor for high-quality surface
generation in minutes. It marries the implicit volumetric rendering of the
truncated signed distance field (TSDF) with multi-resolution hash encoding. We
further propose a hybrid neural tracker to generate animated meshes, which
combines explicit non-rigid tracking with implicit dynamic deformation in a
self-supervised framework. The former provides the coarse warping back into the
canonical space, while the latter implicit one further predicts the
displacements using the 4D hash encoding as in our reconstructor. Then, we
discuss the rendering schemes using the obtained animated meshes, ranging from
dynamic texturing to lumigraph rendering under various bandwidth settings. To
strike an intricate balance between quality and bandwidth, we propose a
hierarchical solution by first rendering 6 virtual views covering the performer
and then conducting occlusion-aware neural texture blending. We demonstrate the
efficacy of our approach in a variety of mesh-based applications and
photo-realistic free-view experiences on various platforms, i.e., inserting
virtual human performances into real environments through mobile AR or
immersively watching talent shows with VR headsets.Comment: 18 pages, 17 figure
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