77 research outputs found
Relationship between Lake Whatcom algae density, water quality and filtration rate at the Bellingham Water Treatment Plant, WA
During the summer of 2009, the Bellingham drinking water treatment plant experienced severe reductions in filtration rates, resulting in mandatory water restrictions. Since then, summer water filtration rates continued to approach critical levels. In 2011, I conducted a study to investigate the phytoplankton and ambient water quality patterns in Lake Whatcom source water to see if any parameters could be used to predict low water filtration rates. In addition, I evaluated water quality and phytoplankton cell densities at different depths at the intake located in Lake Whatcom to see if drawing source water from different depths could help reduce water filtration problems. Water quality and algae samples were collected at the treatment plant screen house and at the intake in Lake Whatcom between June 15 and November 30. During the study period, 62 algal taxa were collected at the screen house. Aphanocapsa/Aphanothece (Cyanobacteria) dominated the algal cell density and Cyclotella and Stephanodiscus (Bacilliariophyta) dominated the algal biovolume, but no single taxon was a unique predictor of low water filtration rates. Among the water quality parameters, nitrate/nitrite had the strongest correlation with filtration rates. Hierarchical cluster analysis was conducted using the first four principal components generated using water quality and algal taxa cell densities, omitting filtration rates and redundant variables. Hierarchical clustering resulted in two distinct clusters that were associated with low and high filtration rates. The samples from the low filtration rate group were characterized by higher water temperatures, conductivities, and alkalinity levels; lower turbidities, nitrate/nitrite and ammonium concentrations; higher cell densities of Aphanocapsa/Aphanothece, Stephanodiscus, Fragilaria, Synedra, Thalassiosira, Naviculoid group, Scenedesmus, Chlamydomonas, Elakatothrix, Cryptomonas, Gymnodinium and Peridinium. Cell morphological characteristics, including mucilage production, elongated cell and presence of fiber threads, were shared by many of the dominant algae in the low filtration rate group and may contribute to the slow filtration. The water quality and phytoplankton community were similar between the screen house and intake sites. In addition, the water column at the intake site was usually unstratified and well-mixed, creating high degree of uniformity in the water quality and phytoplankton data at all depths. Varying the intake depth, which is currently at 10 meters in Basin 2 of Lake Whatcom, is not likely to reduce the effects of problematic algae on water filtration rates
Mutually Guided Few-shot Learning for Relational Triple Extraction
Knowledge graphs (KGs), containing many entity-relation-entity triples,
provide rich information for downstream applications. Although extracting
triples from unstructured texts has been widely explored, most of them require
a large number of labeled instances. The performance will drop dramatically
when only few labeled data are available. To tackle this problem, we propose
the Mutually Guided Few-shot learning framework for Relational Triple
Extraction (MG-FTE). Specifically, our method consists of an entity-guided
relation proto-decoder to classify the relations firstly and a relation-guided
entity proto-decoder to extract entities based on the classified relations. To
draw the connection between entity and relation, we design a proto-level fusion
module to boost the performance of both entity extraction and relation
classification. Moreover, a new cross-domain few-shot triple extraction task is
introduced. Extensive experiments show that our method outperforms many
state-of-the-art methods by 12.6 F1 score on FewRel 1.0 (single-domain) and
20.5 F1 score on FewRel 2.0 (cross-domain).Comment: Accepted by ICASSP 202
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
With the continuous increase of users and items, conventional recommender
systems trained on static datasets can hardly adapt to changing environments.
The high-throughput data requires the model to be updated in a timely manner
for capturing the user interest dynamics, which leads to the emergence of
streaming recommender systems. Due to the prevalence of deep learning-based
recommender systems, the embedding layer is widely adopted to represent the
characteristics of users, items, and other features in low-dimensional vectors.
However, it has been proved that setting an identical and static embedding size
is sub-optimal in terms of recommendation performance and memory cost,
especially for streaming recommendations. To tackle this problem, we first
rethink the streaming model update process and model the dynamic embedding size
search as a bandit problem. Then, we analyze and quantify the factors that
influence the optimal embedding sizes from the statistics perspective. Based on
this, we propose the \textbf{D}ynamic \textbf{E}mbedding \textbf{S}ize
\textbf{S}earch (\textbf{DESS}) method to minimize the embedding size selection
regret on both user and item sides in a non-stationary manner. Theoretically,
we obtain a sublinear regret upper bound superior to previous methods.
Empirical results across two recommendation tasks on four public datasets also
demonstrate that our approach can achieve better streaming recommendation
performance with lower memory cost and higher time efficiency.Comment: Accepted for publication on CIKM202
Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling
Current learning-based edge caching schemes usually suffer from dynamic
content popularity, e.g., in the emerging short video platforms, users' request
patterns shift significantly over time and across different edges. An intuitive
solution for a specific local edge cache is to collect more request histories
from other edge caches. However, uniformly merging these request histories may
not perform satisfactorily due to heterogeneous content distributions on
different edges. To solve this problem, we propose a collaborative edge caching
framework. First, we design a meta-learning-based collaborative strategy to
guarantee that the local model can timely meet the continually changing content
popularity. Then, we design an edge sampling method to select more "valuable"
neighbor edges to participate in the local training. To evaluate the proposed
framework, we conduct trace-driven experiments to demonstrate the effectiveness
of our design: it improves the average cache hit rate by up to
(normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo
2023 (ICME2023
PointCLIP V2: Adapting CLIP for Powerful 3D Open-world Learning
Contrastive Language-Image Pre-training (CLIP) has shown promising open-world
performance on 2D image tasks, while its transferred capacity on 3D point
clouds, i.e., PointCLIP, is still far from satisfactory. In this work, we
propose PointCLIP V2, a powerful 3D open-world learner, to fully unleash the
potential of CLIP on 3D point cloud data. First, we introduce a realistic shape
projection module to generate more realistic depth maps for CLIP's visual
encoder, which is quite efficient and narrows the domain gap between projected
point clouds with natural images. Second, we leverage large-scale language
models to automatically design a more descriptive 3D-semantic prompt for CLIP's
textual encoder, instead of the previous hand-crafted one. Without introducing
any training in 3D domains, our approach significantly surpasses PointCLIP by
+42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D
classification. Furthermore, PointCLIP V2 can be extended to few-shot
classification, zero-shot part segmentation, and zero-shot 3D object detection
in a simple manner, demonstrating our superior generalization ability for 3D
open-world learning. Code will be available at
https://github.com/yangyangyang127/PointCLIP_V2
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
To reduce the reliance on large-scale datasets, recent works in 3D
segmentation resort to few-shot learning. Current 3D few-shot semantic
segmentation methods first pre-train the models on `seen' classes, and then
evaluate their generalization performance on `unseen' classes. However, the
prior pre-training stage not only introduces excessive time overhead, but also
incurs a significant domain gap on `unseen' classes. To tackle these issues, we
propose an efficient Training-free Few-shot 3D Segmentation netwrok, TFS3D, and
a further training-based variant, TFS3D-T. Without any learnable parameters,
TFS3D extracts dense representations by trigonometric positional encodings, and
achieves comparable performance to previous training-based methods. Due to the
elimination of pre-training, TFS3D can alleviate the domain gap issue and save
a substantial amount of time. Building upon TFS3D, TFS3D-T only requires to
train a lightweight query-support transferring attention (QUEST), which
enhances the interaction between the few-shot query and support data.
Experiments demonstrate TFS3D-T improves previous state-of-the-art methods by
+6.93% and +17.96% mIoU respectively on S3DIS and ScanNet, while reducing the
training time by -90%, indicating superior effectiveness and efficiency.Comment: Code is available at https://github.com/yangyangyang127/TFS3
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has
propelled its application to diverse downstream vision tasks. To improve its
capacity on downstream tasks, few-shot learning has become a widely-adopted
technique. However, existing methods either exhibit limited performance or
suffer from excessive learnable parameters. In this paper, we propose APE, an
Adaptive Prior rEfinement method for CLIP's pre-trained knowledge, which
achieves superior accuracy with high computational efficiency. Via a prior
refinement module, we analyze the inter-class disparity in the downstream data
and decouple the domain-specific knowledge from the CLIP-extracted cache model.
On top of that, we introduce two model variants, a training-free APE and a
training-required APE-T. We explore the trilateral affinities between the test
image, prior cache model, and textual representations, and only enable a
lightweight category-residual module to be trained. For the average accuracy
over 11 benchmarks, both APE and APE-T attain state-of-the-art and respectively
outperform the second-best by +1.59% and +1.99% under 16 shots with x30 less
learnable parameters.Comment: Code is available at https://github.com/yangyangyang127/AP
Whatcom County Adult Correction Facilities Environmental Impact Statement
Whatcom County is concerned with the current poor conditions of its adult correctional facilities and has proposed to construct a new facility. The new facility is proposed to hold up to 2,450 beds by 2050. For operational purposes, a horizontally designed facility is preferred
Result Diversification in Search and Recommendation: A Survey
Diversifying return results is an important research topic in retrieval
systems in order to satisfy both the various interests of customers and the
equal market exposure of providers. There has been growing attention on
diversity-aware research during recent years, accompanied by a proliferation of
literature on methods to promote diversity in search and recommendation.
However, diversity-aware studies in retrieval systems lack a systematic
organization and are rather fragmented. In this survey, we are the first to
propose a unified taxonomy for classifying the metrics and approaches of
diversification in both search and recommendation, which are two of the most
extensively researched fields of retrieval systems. We begin the survey with a
brief discussion of why diversity is important in retrieval systems, followed
by a summary of the various diversity concerns in search and recommendation,
highlighting their relationship and differences. For the survey's main body, we
present a unified taxonomy of diversification metrics and approaches in
retrieval systems, from both the search and recommendation perspectives. In the
later part of the survey, we discuss the open research questions of
diversity-aware research in search and recommendation in an effort to inspire
future innovations and encourage the implementation of diversity in real-world
systems.Comment: 20 page
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Uplift modeling has shown very promising results in online marketing.
However, most existing works are prone to the robustness challenge in some
practical applications. In this paper, we first present a possible explanation
for the above phenomenon. We verify that there is a feature sensitivity problem
in online marketing using different real-world datasets, where the perturbation
of some key features will seriously affect the performance of the uplift model
and even cause the opposite trend. To solve the above problem, we propose a
novel robustness-enhanced uplift modeling framework with adversarial feature
desensitization (RUAD). Specifically, our RUAD can more effectively alleviate
the feature sensitivity of the uplift model through two customized modules,
including a feature selection module with joint multi-label modeling to
identify a key subset from the input features and an adversarial feature
desensitization module using adversarial training and soft interpolation
operations to enhance the robustness of the model against this selected subset
of features. Finally, we conduct extensive experiments on a public dataset and
a real product dataset to verify the effectiveness of our RUAD in online
marketing. In addition, we also demonstrate the robustness of our RUAD to the
feature sensitivity, as well as the compatibility with different uplift models
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