183,393 research outputs found
Epitope prediction improved by multitask support vector machines
Motivation: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role in the
identification of T-cell epitopes. Statistical and machine learning methods, in
particular, are widely used to score candidate epitopes based on their
similarity with known epitopes and non epitopes. The genes coding for the MHC
molecules, however, are highly polymorphic, and statistical methods have
difficulties to build models for alleles with few known epitopes. In this case,
recent works have demonstrated the utility of leveraging information across
alleles to improve the performance of the prediction. Results: We design a
support vector machine algorithm that is able to learn epitope models for all
alleles simultaneously, by sharing information across similar alleles. The
sharing of information across alleles is controlled by a user-defined measure
of similarity between alleles. We show that this similarity can be defined in
terms of supertypes, or more directly by comparing key residues known to play a
role in the peptide-MHC binding. We illustrate the potential of this approach
on various benchmark experiments where it outperforms other state-of-the-art
methods
Financial deepening and economic growth
The core of Shapley-Shubik games and general equilibrium models with a Venn diagram is applied for a theory on the role of real finance in economic growth among advanced economies. Then the dynamic computable general equilibrium (DCGE) models for Germany, France, UK, Japan and USA are constructed to assess the validity of the over financing hypothesis that reappeared after the financial crisis of 2008. Actual financial deepening ratios observed in the non-consolidated balance sheet of the OECD exceeded by factors of 3.5, 2.4, 5.1, 11.6 and 4.8 to the optimal financial deepening ratios implied by DCGE models respectively in these countries because of excessive leveraging and bubbles up to 19 times of GDP which were responsible for this great recession. Containing such massive fluctuations for macroeconomic stability and growth in these economies is not possible in conventional fiscal and monetary policy models and requires a DCGE analysis like this along with adoption of separating equilibria strategy in line of Miller-Stiglitz-Roth mechanisms to avoid asymmetric information problems in process of financial intermediation so that the gap between actual and optimal ratios of financial deepening remain as small as possible
Leveraging Information Sharing to Increase Supply Chain Configurability
As supply chains evolve beyond the confines of individual organizations, collaboration has become the Holy Grail in supply chain technology. It plays a key role in achieving flexibility and responsiveness. Information sharing between partners is a key determinant of collaboration. This paper investigates information sharing in four different supply chains—3PL, VMI, CPFR, and supply networks—and compares their information sharing structures, shared data objects, and information flow models. The results show how the various parameters of an information flow model constrain the level of collaboration. Further, the modeling exercise provides insights on how to configure a collaborative supply chain by leveraging information sharing
CSI-Free Optimization of Reconfigurable Intelligent Surfaces with Interference by Using Multiport Network Theory
Reconfigurable Intelligent Surfaces (RIS) will play a pivotal role in
next-generation wireless systems. Despite efforts to minimize pilot overhead
associated with channel estimation, the necessity of configuring the RIS
multiple times before obtaining reliable Channel State Information (CSI) may
significantly diminish their benefits. Therefore, we propose a CSI-free
approach that explores the feasibility of optimizing the RIS for the uplink of
a communication system in the presence of interfering users without relying on
CSI estimation but leveraging solely some a priori statistical knowledge of the
channel. In this context, we consider a multiport network model that accounts
for several aspects overlooked by traditional RIS models used in Communication
Theory, such as mutual coupling among scattering elements and the presence of
structural scattering. The proposed approach targets the maximization of the
average achievable rate and is shown to achieve performance that, in some
cases, can be very close to the case where the RIS is optimized leveraging
perfect CSI.Comment: 12 pages, 8 figure
LLMs as Counterfactual Explanation Modules: Can ChatGPT Explain Black-box Text Classifiers?
Large language models (LLMs) are increasingly being used for tasks beyond
text generation, including complex tasks such as data labeling, information
extraction, etc. With the recent surge in research efforts to comprehend the
full extent of LLM capabilities, in this work, we investigate the role of LLMs
as counterfactual explanation modules, to explain decisions of black-box text
classifiers. Inspired by causal thinking, we propose a pipeline for using LLMs
to generate post-hoc, model-agnostic counterfactual explanations in a
principled way via (i) leveraging the textual understanding capabilities of the
LLM to identify and extract latent features, and (ii) leveraging the
perturbation and generation capabilities of the same LLM to generate a
counterfactual explanation by perturbing input features derived from the
extracted latent features. We evaluate three variants of our framework, with
varying degrees of specificity, on a suite of state-of-the-art LLMs, including
ChatGPT and LLaMA 2. We evaluate the effectiveness and quality of the generated
counterfactual explanations, over a variety of text classification benchmarks.
Our results show varied performance of these models in different settings, with
a full two-step feature extraction based variant outperforming others in most
cases. Our pipeline can be used in automated explanation systems, potentially
reducing human effort
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning
In recent years, self-supervised learning has played a pivotal role in
advancing machine learning by allowing models to acquire meaningful
representations from unlabeled data. An intriguing research avenue involves
developing self-supervised models within an information-theoretic framework,
but many studies often deviate from the stochasticity assumptions made when
deriving their objectives. To gain deeper insights into this issue, we propose
to explicitly model the representation with stochastic embeddings and assess
their effects on performance, information compression and potential for
out-of-distribution detection. From an information-theoretic perspective, we
seek to investigate the impact of probabilistic modeling on the information
bottleneck, shedding light on a trade-off between compression and preservation
of information in both representation and loss space. Emphasizing the
importance of distinguishing between these two spaces, we demonstrate how
constraining one can affect the other, potentially leading to performance
degradation. Moreover, our findings suggest that introducing an additional
bottleneck in the loss space can significantly enhance the ability to detect
out-of-distribution examples, only leveraging either representation features or
the variance of their underlying distribution.Comment: Under review by AISTATS 202
A Multimodal Learning Framework for Comprehensive 3D Mineral Prospectivity Modeling with Jointly Learned Structure-Fluid Relationships
This study presents a novel multimodal fusion model for three-dimensional
mineral prospectivity mapping (3D MPM), effectively integrating structural and
fluid information through a deep network architecture. Leveraging Convolutional
Neural Networks (CNN) and Multilayer Perceptrons (MLP), the model employs
canonical correlation analysis (CCA) to align and fuse multimodal features.
Rigorous evaluation on the Jiaojia gold deposit dataset demonstrates the
model's superior performance in distinguishing ore-bearing instances and
predicting mineral prospectivity, outperforming other models in result
analyses. Ablation studies further reveal the benefits of joint feature
utilization and CCA incorporation. This research not only advances mineral
prospectivity modeling but also highlights the pivotal role of data integration
and feature alignment for enhanced exploration decision-making
Video Game Genre Classification Based on Deep Learning
Video games have played a more and more important role in our life. While the genre classification is a deeply explored research subject by leveraging the strength of deep learning, the automatic video game genre classification has drawn little attention in academia. In this study, we compiled a large dataset of 50,000 video games, consisting of the video game covers, game descriptions and the genre information. We explored three approaches for genre classification using deep learning techniques. First, we developed five image-based models utilizing pre-trained computer vision models such as MobileNet, ResNet50 and Inception, based on the game covers. Second, we developed two text-based models, using Long-short Term Memory (LSTM) model and the Universal Sentence Encoder model, based on the game descriptions. For the third approach, we constructed a multi-modal fusion model, which concatenates extracted features from one image-based model and one text-based model. We analysed our results and revealed some challenges that exist in the task of genre classification for video games. Some future works are also proposed
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