139 research outputs found
Two classes of minimal generic fundamental invariants for tensors
Motivated by the problems raised by B\"{u}rgisser and Ikenmeyer, we discuss
two classes of minimal generic fundamental invariants for tensors of order 3.
The first one is defined on , where . We study
its construction by obstruction design introduced by B\"{u}rgisser and
Ikenmeyer, which partially answers one problem raised by them. The second one
is defined on . We study its evaluation on the matrix multiplication
tensor and unit tensor when
. The evaluation on the unit tensor leads to the definition of Latin
cube and 3-dimensional Alon-Tarsi problem. We generalize some results on Latin
square to Latin cube, which enrich the understanding of 3-dimensional
Alon-Tarsi problem. It is also natural to generalize the constructions to
tensors of other orders. We illustrate the distinction between even and odd
dimensional generalizations by concrete examples. Finally, some open problems
in related fields are raised.Comment: Some typos were changed.New publication information has been update
Recombinant Paraprobiotics as a New Paradigm for Treating Gastrointestinal Nematode Parasites of Humans
Gastrointestinal nematodes (GINs) of humans, e.g., hookworms, negatively impact childhood growth, cognition, nutrition, educational attainment, income, productivity, and pregnancy. Hundreds of millions of people are targeted with mass drug administration (MDA) of donated benzimidazole anthelmintics. However, benzimidazole efficacy against GINs is suboptimal, and reduced/low efficacy has been seen. Developing an anthelmintic for human MDA is daunting: it must be safe, effective, inexpensive, stable without a cold chain, and massively scalable. Bacillus thuringiensis crystal protein 5B (Cry5B) has anthelmintic properties that could fill this void. Here, we developed an active pharmaceutical ingredient (API) containing B. thuringiensis Cry5B compatible with MDA. We expressed Cry5B in asporogenous B. thuringiensis during vegetative phase, forming cytosolic crystals. These bacteria with cytosolic crystals (BaCC) were rendered inviable (inactivated BaCC [IBaCC]) with food-grade essential oils. IBaCC potency was validated in vitro against nematodes. IBaCC was also potent in vivo against human hookworm infections in hamsters. IBaCC production was successfully scaled to 350 liters at a contract manufacturing facility. A simple fit-for-purpose formulation to protect against stomach digestion and powdered IBaCC were successfully made and used against GINs in hamsters and mice. A pilot histopathology study and blood chemistry workup showed that five daily consecutive doses of 200 mg/kg body weight Cry5B IBaCC (the curative single dose is 40 mg/kg) was nontoxic to hamsters and completely safe. IBaCC is a safe, inexpensive, highly effective, easy-to-manufacture, and scalable anthelmintic that is practical for MDA and represents a new paradigm for treating human GINs
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
VidPlat: A Tool for Fast Crowdsourcing of Quality-of-Experience Measurements
For video or web services, it is crucial to measure user-perceived quality of
experience (QoE) at scale under various video quality or page loading delays.
However, fast QoE measurements remain challenging as they must elicit
subjective assessment from human users. Previous work either (1) automates QoE
measurements by letting crowdsourcing raters watch and rate QoE test videos or
(2) dynamically prunes redundant QoE tests based on previously collected QoE
measurements. Unfortunately, it is hard to combine both ideas because
traditional crowdsourcing requires QoE test videos to be pre-determined before
a crowdsourcing campaign begins. Thus, if researchers want to dynamically prune
redundant test videos based on other test videos' QoE, they are forced to
launch multiple crowdsourcing campaigns, causing extra overheads to
re-calibrate or train raters every time.
This paper presents VidPlat, the first open-source tool for fast and
automated QoE measurements, by allowing dynamic pruning of QoE test videos
within a single crowdsourcing task. VidPlat creates an indirect shim layer
between researchers and the crowdsourcing platforms. It allows researchers to
define a logic that dynamically determines which new test videos need more QoE
ratings based on the latest QoE measurements, and it then redirects
crowdsourcing raters to watch QoE test videos dynamically selected by this
logic. Other than having fewer crowdsourcing campaigns, VidPlat also reduces
the total number of QoE ratings by dynamically deciding when enough ratings are
gathered for each test video. It is an open-source platform that future
researchers can reuse and customize. We have used VidPlat in three projects
(web loading, on-demand video, and online gaming). We show that VidPlat can
reduce crowdsourcing cost by 31.8% - 46.0% and latency by 50.9% - 68.8%
Impact of DEM Resolution and Spatial Scale: Analysis of Influence Factors and Parameters on Physically Based Distributed Model
Physically based distributed hydrological models were used to describe small-scale hydrological information in detail. However, the sensitivity of the model to spatially varied parameters and inputs limits the accuracy for application. In this paper, relevant influence factors and sensitive parameters were analyzed to solve this problem. First, a set of digital elevation model (DEM) resolutions and channel thresholds were generated to extract the hydrological influence factors. Second, a numerical relationship between sensitive parameters and influence factors was established to define parameters reasonably. Next, the topographic index (TI) was computed to study the similarity. At last, simulation results were analyzed in two different ways: (1) to observe the change regularity of influence factors and sensitive parameters through the variation of DEM resolutions and channel thresholds and (2) to compare the simulation accuracy of the nested catchment, particularly in the subcatchments and interior grids. Increasing the grid size from 250 m to 1000 m, the TI increased from 9.08 to 11.16 and the Nash-Sutcliffe efficiency (NSE) decreased from 0.77 to 0.75. Utilizing the parameters calculated by the established relationship, the simulation results show the same NSE in the outlet and a better NSE in the simple subcatchment than the calculated interior grids
Shadow Datasets, New challenging datasets for Causal Representation Learning
Discovering causal relations among semantic factors is an emergent topic in
representation learning. Most causal representation learning (CRL) methods are
fully supervised, which is impractical due to costly labeling. To resolve this
restriction, weakly supervised CRL methods were introduced. To evaluate CRL
performance, four existing datasets, Pendulum, Flow, CelebA(BEARD) and
CelebA(SMILE), are utilized. However, existing CRL datasets are limited to
simple graphs with few generative factors. Thus we propose two new datasets
with a larger number of diverse generative factors and more sophisticated
causal graphs. In addition, current real datasets, CelebA(BEARD) and
CelebA(SMILE), the originally proposed causal graphs are not aligned with the
dataset distributions. Thus, we propose modifications to them
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