15 research outputs found
Large Language Models for Supply Chain Optimization
Supply chain operations traditionally involve a variety of complex decision
making problems. Over the last few decades, supply chains greatly benefited
from advances in computation, which allowed the transition from manual
processing to automation and cost-effective optimization. Nonetheless, business
operators still need to spend substantial efforts in \emph{explaining} and
interpreting the optimization outcomes to stakeholders. Motivated by the recent
advances in Large Language Models (LLMs), we study how this disruptive
technology can help bridge the gap between supply chain automation and human
comprehension and trust thereof. We design \name{} -- a framework that accepts
as input queries in plain text, and outputs insights about the underlying
optimization outcomes. Our framework does not forgo the state-of-the-art
combinatorial optimization technology, but rather leverages it to
quantitatively answer what-if scenarios (e.g., how would the cost change if we
used supplier B instead of supplier A for a given demand?). Importantly, our
design does not require sending proprietary data over to LLMs, which can be a
privacy concern in some circumstances. We demonstrate the effectiveness of our
framework on a real server placement scenario within Microsoft's cloud supply
chain. Along the way, we develop a general evaluation benchmark, which can be
used to evaluate the accuracy of the LLM output in other scenarios
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
This technical report presents AutoGen, a new framework that enables
development of LLM applications using multiple agents that can converse with
each other to solve tasks. AutoGen agents are customizable, conversable, and
seamlessly allow human participation. They can operate in various modes that
employ combinations of LLMs, human inputs, and tools. AutoGen's design offers
multiple advantages: a) it gracefully navigates the strong but imperfect
generation and reasoning abilities of these LLMs; b) it leverages human
understanding and intelligence, while providing valuable automation through
conversations between agents; c) it simplifies and unifies the implementation
of complex LLM workflows as automated agent chats. We provide many diverse
examples of how developers can easily use AutoGen to effectively solve tasks or
build applications, ranging from coding, mathematics, operations research,
entertainment, online decision-making, question answering, etc.Comment: 28 page
Pyglmnet : Python implementation of elastic-net regularized generalized linear models
Graceful handling of small Hessian term in coordinate descent solver that led to exploding update term
Ensure full compatibility of GLM class with scikit-lear
Low-Resource Neural Adaptation: A Unified Data Adaptation Framework for Neural Networks
Thesis (Ph.D.)--University of Washington, 2022Many machine learning (ML) models are trained on specific datasets for specific tasks. While traditional transfer learning can adapt to new datasets when labeled data are adequate, adapting to small datasets is still a challenging task. Researchers have applied multi-task learning, meta-learning, weakly-supervised learning, self-supervision, generative adversarial training, and active learning for various data adaptation applications. However, a unified data adaptation framework has yet to be developed. This study proposes a unified framework that can adapt to small datasets in a dynamic environment. Our framework, with a versatile encoder and various decoders, can simultaneously learn from source datasets and estimate confidence for novel data samples. We apply the framework to real-world medical imaging, affective computing, eye-tracking analysis, and database management applications
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Automated quantitative assessment of amorphous calcifications: Towards improved malignancy risk stratification
BackgroundAmorphous calcifications noted on mammograms (i.e., small and indistinct calcifications that are difficult to characterize) are associated with high diagnostic uncertainty, often leading to biopsies. Yet, only 20% of biopsied amorphous calcifications are cancer. We present a quantitative approach for distinguishing between benign and actionable (high-risk and malignant) amorphous calcifications using a combination of local textures, global spatial relationships, and interpretable handcrafted expert features.MethodOur approach was trained and validated on a set of 168 2D full-field digital mammography exams (248 images) from 168 patients. Within these 248 images, we identified 276 image regions with segmented amorphous calcifications and a biopsy-confirmed diagnosis. A set of local (radiomic and region measurements) and global features (distribution and expert-defined) were extracted from each image. Local features were grouped using an unsupervised k-means clustering algorithm. All global features were concatenated with clustered local features and used to train a LightGBM classifier to distinguish benign from actionable cases.ResultsOn the held-out test set of 60 images, our approach achieved a sensitivity of 100%, specificity of 35%, and a positive predictive value of 38% when the decision threshold was set to 0.4. Given that all of the images in our test set resulted in a recommendation of a biopsy, the use of our algorithm would have identified 15 images (25%) that were benign, potentially reducing the number of breast biopsies.ConclusionsQuantitative analysis of full-field digital mammograms can extract subtle shape, texture, and distribution features that may help to distinguish between benign and actionable amorphous calcifications
Remote, tablet-based assessment of gaze following : a nationwide infant twin study
Introduction: Much of our understanding of infant psychological development relies on an in-person, laboratory-based assessment. This limits research generalizability, scalability, and equity in access. One solution is the development of new, remotely deployed assessment tools that do not require real-time experimenter supervision. Methods: The current nationwide (Sweden) infant twin study assessed participants remotely via their caregiver's tablets (N = 104, ages 3 to 17 months). To anchor our findings in previous research, we used a gaze-following task where experimental and age effects are well established. Results: Closely mimicking results from conventional eye tracking, we found that a full head movement elicited more gaze following than isolated eye movements. Furthermore, predictably, we found that older infants followed gaze more frequently than younger infants. Finally, while we found no indication of genetic contributions to gaze-following accuracy, the latency to disengage from the gaze cue and orient toward a target was significantly more similar in monozygotic twins than in dizygotic twins, an indicative of heritability. Discussion: Together, these results highlight the potential of remote assessment of infants' psychological development, which can improve generalizability, inclusion, and scalability in developmental research
Modified DBSCAN algorithm on oculomotor fixation identification
This paper modifies the DBSCAN algorithm to identify fixations and saccades. This method combines advantages from dispersion-based algorithms, such as resilience to noise and intuitive fixational structure, and from velocity-based algorithms, such as the ability to deal appropriately with smooth pursuit (SP) movements