We study off-policy learning (OPL) in contextual bandits, which plays a key role in a wide range of real-world applications such as recommendation systems and online advertising. Typical OPL in contextual bandits assumes an unconstrained environment where a policy can select the same item infinitely. However, in many practical applications, including coupon allocation and e-commerce, limited supply constrains items through budget limits on distributed coupons or inventory restrictions on products. In these settings, greedily selecting the item with the highest expected reward for the current user may lead to early depletion of that item, making it unavailable for future users who could potentially generate higher expected rewards. As a result, OPL methods that are optimal in unconstrained settings may become suboptimal in limited supply settings. To address the issue, we provide a theoretical analysis showing that conventional greedy OPL approaches may fail to maximize the policy performance, and demonstrate that policies with superior performance must exist in limited supply settings. Based on this insight, we introduce a novel method called Off-Policy learning with Limited Supply (OPLS). Rather than simply selecting the item with the highest expected reward, OPLS focuses on items with relatively higher expected rewards compared to the other users, enabling more efficient allocation of items with limited supply. Our empirical results on both synthetic and real-world datasets show that OPLS outperforms existing OPL methods in contextual bandit problems with limited supply.

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space. For example, it might choose a set of furniture pieces (a bed and a drawer) from available items (bed, drawer, chair, etc.) for interior design sales. This setting is widespread in fields such as recommender systems and healthcare, yet OPE/L of CCB remains unexplored in the relevant literature. Typical OPE/L methods such as regression and importance sampling can be applied to the CCB problem, however, they face significant challenges due to high bias or variance, exacerbated by the exponential growth in the number of available subsets. To address these challenges, we introduce a concept of factored action space, which allows us to decompose each subset into binary indicators. This formulation allows us to distinguish between the “main effect” derived from the main actions, and the “residual effect”, originating from the supplemental actions, facilitating more effective OPE. Specifically, our estimator, called OPCB, leverages an importance sampling-based approach to unbiasedly estimate the main effect, while employing regression-based approach to deal with the residual effect with low variance. OPCB achieves substantial variance reduction compared to conventional importance sampling methods and bias reduction relative to regression methods under certain conditions, as illustrated in our theoretical analysis. Experiments demonstrate OPCB’s superior performance over typical methods in both OPE and OPL.

UNLABELLED: Tandem mass spectrometry (MS/MS or MS(n)) is a potent technique for characterizing N-glycan structures. GlycanAnalysis searches a glycan database to support the identification of glycan structures from MS/MS spectra. It also calculates diagnostic ions of glycan structures registered in a glycan database (GlycomeDB or KEGG GLYCAN) and searches for MS/MS spectra of N-glycans that match diagnostic ions to determine the structures. This program functions as a plug-in for Mass++, a freeware mass spectrum visualization and analysis program. AVAILABILITY AND IMPLEMENTATION: The executable files of Mass++ are available for free at http://www.first-ms3d.jp/english/. The GlycanAnalysis plug-in is included in the standard package of Mass++ for Windows. CONTACT: [email protected] or [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary material are available at Bioinformatics online.

BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

This paper presents solutions to reduce measurement error and test time in an AC characteristic test of a source-synchronous device. In order to realize such a solution, we developed a multi strobe circuit which detects a phase difference between output clock and output data at a test cycle in real time without strobe scanning. We implemented a digital delay locked loop for precise multi strobe circuit. We achieved less than 1/5 the measurement error and less than 1/16 the test time compared with the conventional test method

The anterior cruciate ligament (ACL) is one of the important ligaments which control the knee function. In general, the ACL contributes to diminish anteoposterior instability. When some accidents can cause injury of it, the ACL reconstruction is done to recover knee function. The ACL insertion (position of the ACL attachment) is important in the reconstruction, because it determines the knee function after the surgery. Our final goal is to develop a navigation system to provide a criterion of the ACL insertion from information of the condyle shape. The system requires a standardization method of the ACL insertion. Therefore, this study proposes a standardization method by using fuzzy logic to deal with information of the condyle shape. In this experiment, this method was applied for 14 cadaver knees. As a result, the deviation of the angle and distance rate of the ACL insertion on our method were within 13.4 degree and 12.1%, respectively.

We propose a template-matching method in which a parametric template space is constructed from a given set of template images then quickly matched to a reference image. In this method geometrical changes in an image object due to translation, rotation or scaling, and non-geometrical changes such as illumination variations or individual variation between objects are represented by template images in the parametric template space. The method also provides a subpixel matching scheme that yields a high-precision estimation of the object position. Experiments using real images have confirmed the effectiveness of the method.

This paper describes a method for controlling the trajectory tracking of a mobile object, enabling the mobile object to accomplish some task involving a target object. A sequence of image feature points is used to express the reference trajectory that the mobile object should track and the mobile object is controlled by a feedback control law derived from the sequence of image feature points. This control is robust and flexible because the mobile object tracks a reference trajectory in an object-centered coordinate system of the target object. Moreover, the trajectory can be taught with only an image sequence. Simulation showed that a mobile object can track a trajectory described in an object-centered coordinate system.

The authors provide an object representation method that describes 3-D shape and pose using a set of covariant quantities based on a group theoretical approach. These covariant quantities can produce coordinate-invariant features that represent an object's shape and coordinate variant features that represent its pose. First they formulate covariant quantities that are linearly transformed when the coordinate system is changed. Then they show that the invariant features capture coordinate-independent shape information and provide descriptors for indexing into a database of stored models. The variant features are obtained from the covariant quantities in terms of pose equations. A sufficient number of equations (at least three) can determine the object pose. The authors describe two pose estimation algorithms that use these equations, and present numerical simulation results which demonstrate that the algorithms yield close approximations of the object poses.