LCNN: Lookup-based Convolutional Neural Network

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.Comment: CVPR 1

Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images

In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network ($N^3$) that learns to map a single image to a state in a Newtonian scenario. Our experimental evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes 6806 videos aligned with Newtonian scenarios represented using game engines, and 4516 still images with their ground truth dynamics

Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.Comment: To appear in AAAI 201

Quantum secret sharing without entanglement

After analysing the main quantum secret sharing protocol based on the entanglement states, we propose an idea to directly encode the qubit of quantum key distributions, and then present a quantum secret sharing scheme where only product states are employed. As entanglement, especially the inaccessable multi-entangled state, is not necessary in the present quantum secret sharing protocol, it may be more applicable when the number of the parties of secret sharing is large. Its theoretic efficiency is also doubled to approach 100%.Comment: 2 tables, to appear in Phys. Lett.

بررسی نیازهای آموزشی توانبخشی مادران کودکان اتستیک

چکیده مقدمه: اُتيسم اختلال عصب شناختي رو به گسترشي است كه ابعاد مختلف عملكرد كودك و خانواده را تحت تاثير قرار مي‌دهد و نيازمند ارايه خدمات توانبخشي و درماني چند بعدي و گاه مادام‌العمر است لذا پژوهش حاضر با هدف بررسی نیازهای مادران کودکان اتیستیک انجام شده است. مواد و روش‌ها:این پژوهش از نوع نیازسنجی است. گروه نمونه، والدین 41 نفر از کودکان اتیسم را شامل می‌شود که به صورت نمونه‌گیری در دسترس از سه کلینیک اتیسم شهرستان مشهد، انتخاب شدند. شرکت‌کنندگان، پرسش‌نامه پژوهشگرساخته، شامل نُه مقوله آموزشی- توانبخشی را کامل کردند. پایایی کل پرسش‌نامه (نُه مقوله) به شیوه ضریب آلفای کرونباخ 94/0= a محاسبه شد. يافته‌ها: نتایج تی تک نمونه‌ای نشان داد که والدین کودکان اتیسم، همه مقوله‌های آموزشی- توانبخشی را مورد نیاز ارزیابی کردند و چهار مقوله آشنایی با مسایل خودیاری کودک، آشنایی با مهارت‌های بازی کودک و بازی کردن والدین با کودک، آشنایی با مشکلات شناختی و ذهنی کودک و آموزش مهارت‌های ارتباطی را بسیار مورد نیاز ارزیابی کردند. همچنین، نتایج تحلیل واریانس چند متغیره نشان داد که سن کودک، ترتیب تولد کودک، سن مادر و میزان تحصیلات والدین، در اولویت بندی نیازها، تأثیری نداشته است. نتيجه‌گيري: به كارگيري نتايج و اولويت‌هاي مستخرج از نيازسنجي حاضر مي‌تواند متخصصان و درمانگران را جهت ارايه خدمات توان‌بخشي و روانشناختي از اولويت بالا يا در حد بسيار مورد نياز تا اولويت‌هاي بعدي يا در حد مورد نياز ياري رسانند. . کلیدواژه‌ها: کودکان اتستیک، نیازهای آموزشی توانبخشی، نیازسنج

Quantum key distribution for d-level systems with generalized Bell states

Using the generalized Bell states and controlled not gates, we introduce an enatanglement-based quantum key distribution (QKD) of d-level states (qudits). In case of eavesdropping, Eve's information gain is zero and a quantum error rate of (d-1)/d is introduced in Bob's received qudits, so that for large d, comparison of only a tiny fraction of received qudits with the sent ones can detect the presence of Eve.Comment: 8 pages, 3 figures, REVTEX, references added, extensive revision, to appear in Phys. Rev.