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

ELASTIC: Improving CNNs with Dynamic Scaling Policies

Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We argue that the scaling policy should be learned from data. In this paper, we introduce ELASTIC, a simple, efficient and yet very effective approach to learn a dynamic scale policy from data. We formulate the scaling policy as a non-linear function inside the network's structure that (a) is learned from data, (b) is instance specific, (c) does not add extra computation, and (d) can be applied on any network architecture. We applied ELASTIC to several state-of-the-art network architectures and showed consistent improvement without extra (sometimes even lower) computation on ImageNet classification, MSCOCO multi-label classification, and PASCAL VOC semantic segmentation. Our results show major improvement for images with scale challenges. Our code is available here: https://github.com/allenai/elasticComment: CVPR 2019 oral, code available https://github.com/allenai/elasti

Machine Learning and Similarity Network Approaches to Support Automatic Classification of Parkinson’s Diseases Using Accelerometer-based Gait Analysis

Parkinson’s Disease is a worldwide health problem, causing movement disorder and gait deficiencies. Automatic noninvasive techniques for Parkinson\u27s disease diagnosis is appreciated by patients, clinicians and neuroscientists. Gait offers many advantages compared to other biometrics specifically when data is collected using wearable devices; data collection can be performed through inexpensive technologies, remotely, and continuously. In this study, a new set of gait features associated with Parkinson’s Disease are introduced and extracted from accelerometer data. Then, we used a feature selection technique called maximum information gain minimum correlation (MIGMC). Using MIGMC, features are first reduced based on Information Gain method and then through Pearson correlation analysis and Tukey post-hoc multiple comparison test. The ability of several machine learning methods, including Support Vector Machine, Random Forest, AdaBoost, Bagging, and Naïve Bayes are investigated across different feature sets. Similarity Network analysis is also performed to validate our optimal feature set obtained using MIGMC technique. The effect of feature standardization is also investigated. Results indicates that standardization could improve all classifiers’ performance. In addition, the feature set obtained using MIGMC provided the highest classification performance. It is shown that our results from Similarity Network analysis are consistent with our results from the classification task, emphasizing on the importance of choosing an optimal set of gait features to help objective assessment and automatic diagnosis of Parkinson’s disease. Results illustrate that ensemble methods and specifically boosting classifiers had better performances than other classifiers. In summary, our preliminary results support the potential benefit of accelerometers as an objective tool for diagnostic purposes in PD

The Effect of Different Hormones and Antibiotics on Activity of AST Enzyme and its Isozymes in Wistar Rats

Background: One of the valuable tests for diagnosis of cardiovascular and liver diseases is measuring of AST activity. One of the main enzymes of transaminases group is aspartate aminotransferase. Previous Studies have shown that some alteration may occur in mitochondria function as the result of different disease or taking different medication; these changes in mitochondrial and cytosolic AST isozymes can be the sign of disorders. According to the role of steroid hormone in induction of its effects on protein synthesis genes, this study is conducted to shed some light on mechanisms and the interference of steroid hormones and antibiotics.Materials, Methods & Results: In this study, male Wistar rats were injected intramuscularly with Testosterone, progesterone and estradiol; while tetracycline and streptomycin injections were intraperitoneal. Testosterone, progesterone and estradiol injections were carried out in a short-term (15 days) and long-term (45 days) periods. Steroid hormones were dissolved in sesame in a way that by each injection, 0.2 mL sesame oil (containing specific amount of hormone) was injected to the rat. Control group was kept in the same condition except that their sesame oil injection contained no hormone. Tetracycline and Streptomycin injection was carried out for 5 days at 7 am and pm, at 50 mg/kg dosage intraperitoneally. In short- and long-term periods, rats were divided into four groups of 6-member. The concentrations were the same in the periods and 0.2 mL sesame oil was injected intramuscularly. 1 mg testosterone, 12 mg progesterone and 0.2 mg estradiol were intramuscularly injected to rats in group 2, 3 and 4, respectively [10]. Rats were divided into 9 six-member groups as follows: Group 1: intraperitoneal injection of 0.2 mL physiological serum; group 2: injection of 1 mg testosterone; group 3: injection of 1 mg testosterone + 50 mg/kg streptomycin; group 4: injection of 1 mg testosterone + 50 mg/kg tetracycline; group 5: injection of 0.2 mg estradiol; group 6: injection of 0.2 mg estradiol + 50 mg/kg streptomycin; group 7: injection of 0.2 mg estradiol + 50 mg/kg tetracycline; group 8: injection 50 mg/kg streptomycin; and group 9: injection of 50 mg/kg tetracycline. Serum concentration of AST enzyme was measured at the end of each period and the data were compared by SPSS software. all three steroid hormones had no significant impact on AST activity in short term. However, a significant effect was observed in long term in mean AST activities of the 4 groups. The group injected by testosterone exhibited 9% increases in comparison with the control group. Antibiotic-administrated groups showed lower activities as compared with hormone-injected groups. Steroid hormones and testosterone can enhance AST activity, in short-term and long-term, respectively by induction of protein enzyme. The second test confirmed this theory as the antibiotics decreased the AST activity enhancement by testosterone.Discussion: Based on the present study, steroid hormones can enhance the aspartate aminotransferase activity; and antibiotics can decrease the level of this liver enzyme by inhibition of polypeptide synthesis on related genes. This reaction could be due to interference of hormones and antibiotics effect which hinders the hormone effect along with the drug to activate the protein synthesis process

Bytes Are All You Need: Transformers Operating Directly On File Bytes

Modern deep learning approaches usually transform inputs into a modality-specific form. For example, the most common deep learning approach to image classification involves decoding image file bytes into an RGB tensor which is passed into a neural network. Instead, we investigate performing classification directly on file bytes, without the need for decoding files at inference time. Using file bytes as model inputs enables the development of models which can operate on multiple input modalities. Our model, \emph{ByteFormer}, achieves an ImageNet Top-1 classification accuracy of $77.33\%$ when training and testing directly on TIFF file bytes using a transformer backbone with configuration similar to DeiT-Ti ($72.2\%$ accuracy when operating on RGB images). Without modifications or hyperparameter tuning, ByteFormer achieves $95.42\%$ classification accuracy when operating on WAV files from the Speech Commands v2 dataset (compared to state-of-the-art accuracy of $98.7\%$). Additionally, we demonstrate that ByteFormer has applications in privacy-preserving inference. ByteFormer is capable of performing inference on particular obfuscated input representations with no loss of accuracy. We also demonstrate ByteFormer's ability to perform inference with a hypothetical privacy-preserving camera which avoids forming full images by consistently masking $90\%$ of pixel channels, while still achieving $71.35\%$ accuracy on ImageNet. Our code will be made available at https://github.com/apple/ml-cvnets/tree/main/examples/byteformer