Online Mixed Discrete and Continuous Optimization: Algorithms, Regret Analysis and Applications

We study an online mixed discrete and continuous optimization problem where a decision maker interacts with an unknown environment for a number of $T$ rounds. At each round, the decision maker needs to first jointly choose a discrete and a continuous actions and then receives a reward associated with the chosen actions. The goal for the decision maker is to maximize the accumulative reward after $T$ rounds. We propose algorithms to solve the online mixed discrete and continuous optimization problem and prove that the algorithms yield sublinear regret in $T$. We show that a wide range of applications in practice fit into the framework of the online mixed discrete and continuous optimization problem, and apply the proposed algorithms to solve these applications with regret guarantees. We validate our theoretical results with numerical experiments

Similarity Principle and its Acoustical Verification

This study finds a similarity principle the waves emanated from the same source are similar to each other as long as two wave receivers are close enough to each other the closer to each other the wave receivers are the more similar to each other the received waves are We define the similarity mathematically and verify the similarity principle by acoustical experiment

Link prediction using discrete-time quantum walk

Predviđanje veze jedno je od ključnih pitanja složenih mreža koje trenutačno privlači pozor mnogih istraživača. Do sada su predložene mnoge metode predviđanja veze. Klasično slučajno gibanje predstavlja učinkoviti alat koji se uvelike rabi u proučavanju problema predviđanja veze. Kvantno gibanje je kvantni analog klasičnog slučajnog gibanja. Rezultati mnogih istraživanja pokazuju da kvantni algoritmi koji rabe kvantno gibanje nadmašujuju svoje klasične kopije u mnogim primjenama, kao što su, na primjer, usklađivanje i istraživanje grafikona. Međutim, malo je istraživanja o predviđanju veze na temelju kvantnog gibanja, a posebice kvantnog gibanja u diskretnom vremenu. U ovom se radu predlaže nova metoda predviđanja veze zasnovana na kvantnom gibanju u diskretnom vremenu. Rezultati eksperimenta pokazuju da je točnost predviđanja našom metodom bolja nego tipičnim metodama. Vremenska složenost naše metode koja se izvodi na klasičnim računalima, u usporedbi s metodama baziranim na klasičnom slučajnom gibanju, malo je bolja. No, naša se metoda može znatno ubrzati izvođenjem na kvantnim računalima.Link prediction is one of the key issues of complex networks, which attracts much research interest currently. Many link prediction methods have been proposed so far. The classical random walk as an effective tool has been widely used to study the link prediction problems. Quantum walk is the quantum analogue of classical random walk. Numerous research results show that quantum algorithms using quantum walk outperform their classical counterparts in many applications, such as graph matching and searching. But there have been few studies of the link prediction based on quantum walk, especially on discrete-time quantum walk. This paper proposes a new link prediction method based on discrete-time quantum walk. Experiment results show that prediction accuracy of our method is better than the typical methods. The time complexity of our method running on classical computers, compared with the methods based on classical random walk, is slightly higher. But our method can be greatly accelerated by executing on quantum computers

6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features

The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is proposed to resolve the symmetric ambiguity by calculating edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.Comment: 16 pages,20 figure

Hybrid Representation Learning for Cognitive Diagnosis in Late-Life Depression Over 5 Years with Structural MRI

Late-life depression (LLD) is a highly prevalent mood disorder occurring in older adults and is frequently accompanied by cognitive impairment (CI). Studies have shown that LLD may increase the risk of Alzheimer's disease (AD). However, the heterogeneity of presentation of geriatric depression suggests that multiple biological mechanisms may underlie it. Current biological research on LLD progression incorporates machine learning that combines neuroimaging data with clinical observations. There are few studies on incident cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this paper, we describe the development of a hybrid representation learning (HRL) framework for predicting cognitive diagnosis over 5 years based on T1-weighted sMRI data. Specifically, we first extract prediction-oriented MRI features via a deep neural network, and then integrate them with handcrafted MRI features via a Transformer encoder for cognitive diagnosis prediction. Two tasks are investigated in this work, including (1) identifying cognitively normal subjects with LLD and never-depressed older healthy subjects, and (2) identifying LLD subjects who developed CI (or even AD) and those who stayed cognitively normal over five years. To the best of our knowledge, this is among the first attempts to study the complex heterogeneous progression of LLD based on task-oriented and handcrafted MRI features. We validate the proposed HRL on 294 subjects with T1-weighted MRIs from two clinically harmonized studies. Experimental results suggest that the HRL outperforms several classical machine learning and state-of-the-art deep learning methods in LLD identification and prediction tasks

Acidochromic organic photovoltaic integrated device

Tremendous efforts have been devoted to boosting the power conversion efficiency (PCE) of organic solar cells (OSCs) via the introduction of cathode interlayers (CILs). However, CIL materials have limited diversity and the development of multifunctional devices is largely neglected. Herein, an acidochromic organic photovoltaic integrated device is firstly proposed by introducing an acid-sensitive stimulating-reaction organic molecule as both the CIL of OSCs and the sensor of monitoring environmental acidity. The oxazolidine unit of acidochromic molecule can form a ring-opening structure after acid treatment, resulting in the remarkable color change with the direct reflection of pH value of ecological environment. The additive-free PM6:Y6 OSCs using the acidochromic molecule as the CIL achieve an excellent PCE of above 15.29 %, which is 47 % higher than that of the control device. The PCE can even maintain above 92 % after treating CIL with various strong acids (pH = 1). Moreover, the color of acidified films and the degraded performance of acidified OSCs can be easily restored by alkaline treatment. The successful application of CIL in other highly efficient photovoltaic systems proves its good universality. This work triggers the promising application of acidochromic molecules in solar cells as CIL with the additional function of recognition of acid environment

SLOTH: Structured Learning and Task-based Optimization for Time Series Forecasting on Hierarchies

Multivariate time series forecasting with hierarchical structure is widely used in real-world applications, e.g., sales predictions for the geographical hierarchy formed by cities, states, and countries. The hierarchical time series (HTS) forecasting includes two sub-tasks, i.e., forecasting and reconciliation. In the previous works, hierarchical information is only integrated in the reconciliation step to maintain coherency, but not in forecasting step for accuracy improvement. In this paper, we propose two novel tree-based feature integration mechanisms, i.e., top-down convolution and bottom-up attention to leverage the information of the hierarchical structure to improve the forecasting performance. Moreover, unlike most previous reconciliation methods which either rely on strong assumptions or focus on coherent constraints only,we utilize deep neural optimization networks, which not only achieve coherency without any assumptions, but also allow more flexible and realistic constraints to achieve task-based targets, e.g., lower under-estimation penalty and meaningful decision-making loss to facilitate the subsequent downstream tasks. Experiments on real-world datasets demonstrate that our tree-based feature integration mechanism achieves superior performances on hierarchical forecasting tasks compared to the state-of-the-art methods, and our neural optimization networks can be applied to real-world tasks effectively without any additional effort under coherence and task-based constraint

SuperScaler: Supporting Flexible DNN Parallelization via a Unified Abstraction

With the growing model size, deep neural networks (DNN) are increasingly trained over massive GPU accelerators, which demands a proper parallelization plan that transforms a DNN model into fine-grained tasks and then schedules them to GPUs for execution. Due to the large search space, the contemporary parallelization plan generators often rely on empirical rules that couple transformation and scheduling, and fall short in exploring more flexible schedules that yield better memory usage and compute efficiency. This tension can be exacerbated by the emerging models with increasing complexity in their structure and model size. SuperScaler is a system that facilitates the design and generation of highly flexible parallelization plans. It formulates the plan design and generation into three sequential phases explicitly: model transformation, space-time scheduling, and data dependency preserving. Such a principled approach decouples multiple seemingly intertwined factors and enables the composition of highly flexible parallelization plans. As a result, SuperScaler can not only generate empirical parallelization plans, but also construct new plans that achieve up to 3.5X speedup compared to state-of-the-art solutions like DeepSpeed, Megatron and Alpa, for emerging DNN models like Swin-Transformer and AlphaFold2, as well as well-optimized models like GPT-3