125 research outputs found
ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning
Real-Time Bidding (RTB) is an important mechanism in modern online
advertising systems. Advertisers employ bidding strategies in RTB to optimize
their advertising effects subject to various financial requirements, especially
the return-on-investment (ROI) constraint. ROIs change non-monotonically during
the sequential bidding process, and often induce a see-saw effect between
constraint satisfaction and objective optimization. While some existing
approaches show promising results in static or mildly changing ad markets, they
fail to generalize to highly dynamic ad markets with ROI constraints, due to
their inability to adaptively balance constraints and objectives amidst
non-stationarity and partial observability. In this work, we specialize in
ROI-Constrained Bidding in non-stationary markets. Based on a Partially
Observable Constrained Markov Decision Process, our method exploits an
indicator-augmented reward function free of extra trade-off parameters and
develops a Curriculum-Guided Bayesian Reinforcement Learning (CBRL) framework
to adaptively control the constraint-objective trade-off in non-stationary ad
markets. Extensive experiments on a large-scale industrial dataset with two
problem settings reveal that CBRL generalizes well in both in-distribution and
out-of-distribution data regimes, and enjoys superior learning efficiency and
stability.Comment: Accepted by SIGKDD 202
Improved Density Peak Clustering Algorithm Based on Choosing Strategy Automatically for Cut-off Distance and Cluster Centre
Due to the defect of quick search density peak clustering algorithm required an artificial attempt to determine the cut-off distance and circle the clustering centres, density peak clustering algorithm based on choosing strategy automatically for cut-off distance and cluster center (CSA-DP) is proposed. The algorithm introduces the improved idea of determining cut-off distance and clustering centres, according to the approximate distance that maximum density sample point to minimum density sample point and the variation of similarity between the points which may be clustering centres. First, obtaining the sample point density according to the k-nearest neighbour samples and tapping the sample sorting of the distance to the maximum density point; then finding the turning position of density trends and determining the cut-off distance on the basis of the turning position; finally, in view of the density peak clustering algorithm, finding the data points which may be the centres of the cluster, comparing the similarity between them and determining the final clustering centres. The simulation results show that the improved algorithm proposed in this paper can automatically determine the cut-off distance, circle the centres, and make the clustering results become more accurate. In the end, this paper makes an empirical analysis on the stock of 147 bio pharmaceutical listed companies by using the improved algorithm, which provides a reliable basis for the classification and evaluation of listed companies. It has a wide range of applicability
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language
Models (LLMs). As LLMs find applications across diverse domains, the
reliability and accuracy of their outputs become vital. We define the
Factuality Issue as the probability of LLMs to produce content inconsistent
with established facts. We first delve into the implications of these
inaccuracies, highlighting the potential consequences and challenges posed by
factual errors in LLM outputs. Subsequently, we analyze the mechanisms through
which LLMs store and process facts, seeking the primary causes of factual
errors. Our discussion then transitions to methodologies for evaluating LLM
factuality, emphasizing key metrics, benchmarks, and studies. We further
explore strategies for enhancing LLM factuality, including approaches tailored
for specific domains. We focus two primary LLM configurations standalone LLMs
and Retrieval-Augmented LLMs that utilizes external data, we detail their
unique challenges and potential enhancements. Our survey offers a structured
guide for researchers aiming to fortify the factual reliability of LLMs.Comment: 62 pages; 300+ reference
Field-free spin-orbit torque switching enabled by interlayer Dzyaloshinskii-Moriya interaction
Perpendicularly magnetized structures that are switchable using a spin
current under field-free conditions can potentially be applied in spin-orbit
torque magnetic random-access memory(SOT-MRAM).Several structures have been
developed;however,new structures with a simple stack structure and MRAM
compatibility are urgently needed.Herein,a typical structure in a perpendicular
spin-transfer torque MRAM,the Pt/Co multilayer and its synthetic
antiferromagnetic counterpart with perpendicular magnetic anisotropy, was
observed to possess an intrinsic interlayer chiral interaction between
neighboring magnetic layers,namely the interlayer Dzyaloshinskii-Moriya
interaction (DMI) effect. Furthermore, using a current parallel to the
eigenvector of the interlayer DMI, we switched the perpendicular magnetization
of both structures without a magnetic field, owing to the additional
symmetry-breaking introduced by the interlayer DMI. This SOT switching scheme
realized in the Pt/Co multilayer and its synthetic antiferromagnet structure
may open a new avenue toward practical perpendicular SOT-MRAM and other SOT
devices
Omecamtiv mecarbil in chronic heart failure with reduced ejection fraction, GALACTICâHF: baseline characteristics and comparison with contemporary clinical trials
Aims:
The safety and efficacy of the novel selective cardiac myosin activator, omecamtiv mecarbil, in patients with heart failure with reduced ejection fraction (HFrEF) is tested in the Global Approach to Lowering Adverse Cardiac outcomes Through Improving Contractility in Heart Failure (GALACTICâHF) trial. Here we describe the baseline characteristics of participants in GALACTICâHF and how these compare with other contemporary trials.
Methods and Results:
Adults with established HFrEF, New York Heart Association functional class (NYHA)ââ„âII, EF â€35%, elevated natriuretic peptides and either current hospitalization for HF or history of hospitalization/ emergency department visit for HF within a year were randomized to either placebo or omecamtiv mecarbil (pharmacokineticâguided dosing: 25, 37.5 or 50âmg bid). 8256 patients [male (79%), nonâwhite (22%), mean age 65âyears] were enrolled with a mean EF 27%, ischemic etiology in 54%, NYHA II 53% and III/IV 47%, and median NTâproBNP 1971âpg/mL. HF therapies at baseline were among the most effectively employed in contemporary HF trials. GALACTICâHF randomized patients representative of recent HF registries and trials with substantial numbers of patients also having characteristics understudied in previous trials including more from North America (n = 1386), enrolled as inpatients (n = 2084), systolic blood pressureâ<â100âmmHg (n = 1127), estimated glomerular filtration rate <â30âmL/min/1.73 m2 (n = 528), and treated with sacubitrilâvalsartan at baseline (n = 1594).
Conclusions:
GALACTICâHF enrolled a wellâtreated, highârisk population from both inpatient and outpatient settings, which will provide a definitive evaluation of the efficacy and safety of this novel therapy, as well as informing its potential future implementation
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