A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
Paper predicts bearing degradation stages for pharmaceutical industry maintenance.
Are large scale research programs that include many projects more productive than smaller ones with fewer projects? This problem of economy of scale is particularly relevant for understanding recent mergers in particular in the pharmaceutical industry. We present a quantitative theory based on the characterization of d…
Study models growth of unorganized retail in Indian pharma sector amid organized and e-retail competition.
This study improves valuation of post-revenue biopharmaceutical assets using Pfizer's data.
Study finds WACC negatively impacts firm profitability in Bangladesh's food industry.
Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error approach by individual experiences of pharmaceutical scientists, which is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important cap…
Federated learning platform for drug discovery without sharing data.
AI enhances personalized drug development and decision-making in pharma.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
Study shows how China's stock market reflects economic demand changes during COVID-19.
Quantum machine learning boosts drug discovery efficiency.
Unified approach for sequence design combining likelihood-free inference and black-box optimization.
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential leads are ultimately discarded due to the toxicity they induce. In this paper, we propose a novel machine learning approach for the prediction o…
This paper introduces a reinforcement learning framework for A/B testing with dynamic causal effects.
Variational autoencoders (VAEs) defined over SMILES string and graph-based representations of molecules promise to improve the optimization of molecular properties, thereby revolutionizing the pharmaceuticals and materials industries. However, these VAEs are hindered by the non-unique nature of SMILES strings and the c…
We analyze a database comprising quarterly sales of 55624 pharmaceutical products commercialized by 3939 pharmaceutical firms in the period 1992--2001. We study the probability density function (PDF) of growth in firms and product sales and find that the width of the PDF of growth decays with the sales as a power law w…
Bayesian optimization speeds up bioprocess development across scales.
Decomposes financial networks to reveal cause-effect hierarchies during crises.
RL platform enhances user journeys in healthcare apps.
With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic propertie…
In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimp…
In this paper, we seek a clinically-relevant latent code for representing the spectrum of macular disease. Towards this end, we construct retina-VAE, a variational autoencoder-based model that accepts a patient profile vector (pVec) as input. The pVec components include clinical exam findings and demographic informatio…
SNAPO optimizes policies for complex sequential decisions using differentiable simulation.
Study reveals holiday effect on China's time-honored brands, especially alcoholic beverages.
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Teaches reproducible research to medical students and postgrads.
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Improves industry classification for diversified companies.
New model uses pretrained biochemical language models to generate drug compounds.
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
Develops MIS, a probabilistic model for multi-industry classification.
Study finds environmental liability insurance reduces industrial carbon emissions.
Analyzes Indian chemical industry post-Covid.
Predicts stock price changes based on clinical trial announcements.
The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States. In this work, we analyze medical and pharmaceutical claims data to draw insights on characteristics of patients who are more prone to adverse outcomes after an initial synthetic opioid prescription…
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace---the offensive …
We provide complete source code for building a fundamental industry classification based on publically available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssr…
The Industrial Internet of Things drastically increases connectivity of devices in industrial applications. In addition to the benefits in efficiency, scalability and ease of use, this creates novel attack surfaces. Historically, industrial networks and protocols do not contain means of security, such as authentication…
We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Ret…
Quantum computing offers financial industry new optimization and risk management tools.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
Tool converts industrial systems to RL environments for optimization.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
Deep learning predicts employment changes and industry health.
Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters o…
Industry lacks tools to secure ML systems, study finds.
Study shows insurance industry in North Macedonia declined 10% due to COVID-19.