225 research outputs found
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
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 rounds. We propose algorithms to solve the online
mixed discrete and continuous optimization problem and prove that the
algorithms yield sublinear regret in . 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
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