229 research outputs found
CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition
Driven by the wave of urbanization in recent decades, the research topic
about migrant behavior analysis draws great attention from both academia and
the government. Nevertheless, subject to the cost of data collection and the
lack of modeling methods, most of existing studies use only questionnaire
surveys with sparse samples and non-individual level statistical data to
achieve coarse-grained studies of migrant behaviors. In this paper, a partially
supervised cross-domain deep learning model named CD-CNN is proposed for
migrant/native recognition using mobile phone signaling data as behavioral
features and questionnaire survey data as incomplete labels. Specifically,
CD-CNN features in decomposing the mobile data into location domain and
communication domain, and adopts a joint learning framework that combines two
convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN
employs a three-step algorithm for training, in which the co-training step is
of great value to partially supervised cross-domain learning. Comparative
experiments on the city Wuxi demonstrate the high predictive power of CD-CNN.
Two interesting applications further highlight the ability of CD-CNN for
in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc
Full Bayesian Significance Testing for Neural Networks
Significance testing aims to determine whether a proposition about the
population distribution is the truth or not given observations. However,
traditional significance testing often needs to derive the distribution of the
testing statistic, failing to deal with complex nonlinear relationships. In
this paper, we propose to conduct Full Bayesian Significance Testing for neural
networks, called \textit{n}FBST, to overcome the limitation in relationship
characterization of traditional approaches. A Bayesian neural network is
utilized to fit the nonlinear and multi-dimensional relationships with small
errors and avoid hard theoretical derivation by computing the evidence value.
Besides, \textit{n}FBST can test not only global significance but also local
and instance-wise significance, which previous testing methods don't focus on.
Moreover, \textit{n}FBST is a general framework that can be extended based on
the measures selected, such as Grad-\textit{n}FBST, LRP-\textit{n}FBST,
DeepLIFT-\textit{n}FBST, LIME-\textit{n}FBST. A range of experiments on both
simulated and real data are conducted to show the advantages of our method.Comment: Published as a conference paper at AAAI 202
Self-Supervised Deconfounding Against Spatio-Temporal Shifts: Theory and Modeling
As an important application of spatio-temporal (ST) data, ST traffic
forecasting plays a crucial role in improving urban travel efficiency and
promoting sustainable development. In practice, the dynamics of traffic data
frequently undergo distributional shifts attributed to external factors such as
time evolution and spatial differences. This entails forecasting models to
handle the out-of-distribution (OOD) issue where test data is distributed
differently from training data. In this work, we first formalize the problem by
constructing a causal graph of past traffic data, future traffic data, and
external ST contexts. We reveal that the failure of prior arts in OOD traffic
data is due to ST contexts acting as a confounder, i.e., the common cause for
past data and future ones. Then, we propose a theoretical solution named
Disentangled Contextual Adjustment (DCA) from a causal lens. It differentiates
invariant causal correlations against variant spurious ones and deconfounds the
effect of ST contexts. On top of that, we devise a Spatio-Temporal
sElf-superVised dEconfounding (STEVE) framework. It first encodes traffic data
into two disentangled representations for associating invariant and variant ST
contexts. Then, we use representative ST contexts from three conceptually
different perspectives (i.e., temporal, spatial, and semantic) as
self-supervised signals to inject context information into both
representations. In this way, we improve the generalization ability of the
learned context-oriented representations to OOD ST traffic forecasting.
Comprehensive experiments on four large-scale benchmark datasets demonstrate
that our STEVE consistently outperforms the state-of-the-art baselines across
various ST OOD scenarios.Comment: 14 pages, 9 figure
Distance measurements via the morphogen gradient of Bicoid in Drosophila embryos
<p>Abstract</p> <p>Background</p> <p>Patterning along the anterior-posterior (A-P) axis in <it>Drosophila </it>embryos is instructed by the morphogen gradient of Bicoid (Bcd). Despite extensive studies of this morphogen, how embryo geometry may affect gradient formation and target responses has not been investigated experimentally.</p> <p>Results</p> <p>In this report, we systematically compare the Bcd gradient profiles and its target expression patterns on the dorsal and ventral sides of the embryo. Our results support a hypothesis that proper distance measurement and the encoded positional information of the Bcd gradient are along the perimeter of the embryo. Our results also reveal that the dorsal and ventral sides of the embryo have a fundamentally similar relationship between Bcd and its target Hunchback (Hb), suggesting that Hb expression properties on the two sides of the embryo can be directly traced to Bcd gradient properties. Our 3-D simulation studies show that a curvature difference between the two sides of an embryo is sufficient to generate Bcd gradient properties that are consistent with experimental observations.</p> <p>Conclusions</p> <p>The findings described in this report provide a first quantitative, experimental evaluation of embryo geometry on Bcd gradient formation and target responses. They demonstrate that the physical features of an embryo, such as its shape, are integral to how pattern is formed.</p
Validating Multimedia Content Moderation Software via Semantic Fusion
The exponential growth of social media platforms, such as Facebook and
TikTok, has revolutionized communication and content publication in human
society. Users on these platforms can publish multimedia content that delivers
information via the combination of text, audio, images, and video. Meanwhile,
the multimedia content release facility has been increasingly exploited to
propagate toxic content, such as hate speech, malicious advertisements, and
pornography. To this end, content moderation software has been widely deployed
on these platforms to detect and blocks toxic content. However, due to the
complexity of content moderation models and the difficulty of understanding
information across multiple modalities, existing content moderation software
can fail to detect toxic content, which often leads to extremely negative
impacts.
We introduce Semantic Fusion, a general, effective methodology for validating
multimedia content moderation software. Our key idea is to fuse two or more
existing single-modal inputs (e.g., a textual sentence and an image) into a new
input that combines the semantics of its ancestors in a novel manner and has
toxic nature by construction. This fused input is then used for validating
multimedia content moderation software. We realized Semantic Fusion as DUO, a
practical content moderation software testing tool. In our evaluation, we
employ DUO to test five commercial content moderation software and two
state-of-the-art models against three kinds of toxic content. The results show
that DUO achieves up to 100% error finding rate (EFR) when testing moderation
software. In addition, we leverage the test cases generated by DUO to retrain
the two models we explored, which largely improves model robustness while
maintaining the accuracy on the original test set.Comment: Accepted by ISSTA 202
The Model 2.0 and Friends: An Interim Report
Last year, I reported on preliminary results of an anatomically-inspired deep learning model of the visual system and its role in explaining the face inversion effect. This year, I will report on new results and some variations on network architectures that we have explored, mainly as a way to generate discussion and get feedback. This is by no means a polished, final presentation!
We look forward to the group’s suggestions for these projects
Amino acid Formula induces Microbiota Dysbiosis and Depressive-Like Behavior in Mice
Amino acid formula (AAF) is increasingly consumed in infants with cow\u27s milk protein allergy; however, the long-term influences on health are less described. In this study, we established a mouse model by subjecting neonatal mice to an amino acid diet (AAD) to mimic the feeding regimen of infants on AAF. Surprisingly, AAD-fed mice exhibited dysbiotic microbiota and increased neuronal activity in both the intestine and brain, as well as gastrointestinal peristalsis disorders and depressive-like behavior. Furthermore, fecal microbiota transplantation from AAD-fed mice or AAF-fed infants to recipient mice led to elevated neuronal activations and exacerbated depressive-like behaviors compared to that from normal chow-fed mice or cow\u27s-milk-formula-fed infants, respectively. Our findings highlight the necessity to avoid the excessive use of AAF, which may influence the neuronal development and mental health of children
Gut microbial DL-endopeptidase alleviates Crohn's disease via the NOD2 pathway
The pattern-recognition receptor NOD2 senses bacterial muropeptides to regulate host immunity and maintain homeostasis. Loss-of-function mutations in NOD2 are associated with Crohn's disease (CD), but how the variations in microbial factors influence NOD2 signaling and host pathology is elusive. We demonstrate that the Firmicutes peptidoglycan remodeling enzyme, DL-endopeptidase, increased the NOD2 ligand level in the gut and impacted colitis outcomes. Metagenomic analyses of global cohorts (n = 857) revealed that DL-endopeptidase gene abundance decreased globally in CD patients and negatively correlated with colitis. Fecal microbiota from CD patients with low DL-endopeptidase activity predisposed mice to colitis. Administering DL-endopeptidase, but not an active site mutant, alleviated colitis via the NOD2 pathway. Therapeutically restoring NOD2 ligands with a DL-endopeptidase-producing Lactobacillus salivarius strain or mifamurtide, a clinical analog of muramyl dipeptide, exerted potent anti-colitis effects. Our study suggests that the depletion of DL-endopeptidase contributes to CD pathogenesis through NOD2 signaling, providing a therapeutically modifiable target
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