Strategies to prevent falls in the elderly: effect of a 10-week Taiji training program on proprioception, functional strength and mobility, and postural adaptation

The impact of elderly falls on the Canadian health care system is widespread. Balance and motor coordination are commonly affected during the aging process due to declining proprioception (Ribeiro & Oliveira, 2007). In addition, there is slower walking speed and shorter stride length among fallers (Wolfson, Judge, Whipple, & King, 1995). Robinovitch et al. (2013) reported that 41% of falls in long term care homes were attributed to incorrect weight shifting. Considering the strong relationship between falls in the elderly and declining proprioception (Mion et al. 1989), the purpose of this study was to examine the effects of a 10-week Taiji training program on ankle proprioception, functional lower extremity strength and mobility and postural adaptation of older adults at risk of falls. A sample of 32 older adults (M = 66.5, SD = 4.94) participated in this study. Sixteen participants were conveniently assigned to the Taiji group; practiced Taiji Quan 6-form twice weekly for 60 minutes for 10-weeks, and completed their weekly Taiji logbook. The remaining 16 participants in the control group; continued their regular activities except Taiji and completed their weekly logbook. All the participants completed pre and post assessments of postural control on an AMTI force platform, functional mobility on the Adapted Timed Up and Go Test (ATGUG), ankle joint proprioception i.e., perception of joint movement sensation, on a tilting platform, and functional strength of lower extremities on the Chair Stand test. A two by two mixed factorial ANOVA indicated significant changes with large effect size for proprioception (backward angle), lower extremity strength (repetitions), functional mobility (ATGUG 5 and ATGUG 4) and medium effect size for functional mobility (ATGUG 2). Changes in the proprioception variable suggest that Taiji may be a valuable alternative to traditional exercise programs. As Taiji exercises are beneficial in enhancing ankle joint backward movement perception and it also increases the efficacy of body movement by promoting protective effects against declining physical functions. Future studies should implement randomized controlled design and a larger sample size

End-to-End Neural Network Compression via ℓ1ℓ2\frac{\ell_1}{\ell_2} Regularized Latency Surrogates

Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quantization) for each layer either manually or via neural architecture search (NAS) which can be computationally expensive. We address this problem by providing an end-to-end technique that optimizes for model's Floating Point Operations (FLOPs) or for on-device latency via a novel $\frac{\ell_1}{\ell_2}$ latency surrogate. Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization. Crucially, it is fast and runs in almost the same amount of time as single model training; which is a significant training speed-up over standard NAS methods. For BERT compression on GLUE fine-tuning tasks, we achieve $50\%$ reduction in FLOPs with only $1\%$ drop in performance. For compressing MobileNetV3 on ImageNet-1K, we achieve $15\%$ reduction in FLOPs, and $11\%$ reduction in on-device latency without drop in accuracy, while still requiring $3\times$ less training compute than SOTA compression techniques. Finally, for transfer learning on smaller datasets, our technique identifies $1.2\times$-$1.4\times$ cheaper architectures than standard MobileNetV3, EfficientNet suite of architectures at almost the same training cost and accuracy

Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks

Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (SB) of DNNs - bias towards learning only the simplest features - is a key reason for this brittleness, another recent line of work has surprisingly found that diverse/ complex features are indeed learned by the backbone, and their brittleness is due to the linear classification head relying primarily on the simplest features. To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones. Namely, simple features are replicated several times in the learned representations while complex features might not be replicated. This phenomenon, we term Feature Replication Hypothesis, coupled with the Implicit Bias of SGD to converge to maximum margin solutions in the feature space, leads the models to rely mostly on the simple features for classification. To mitigate this bias, we propose Feature Reconstruction Regularizer (FRR) to ensure that the learned features can be reconstructed back from the logits. The use of {\em FRR} in linear layer training (FRR-L) encourages the use of more diverse features for classification. We further propose to finetune the full network by freezing the weights of the linear layer trained using FRR-L, to refine the learned features, making them more suitable for classification. Using this simple solution, we demonstrate up to 15% gains in OOD accuracy on the recently introduced semi-synthetic datasets with extreme distribution shifts. Moreover, we demonstrate noteworthy gains over existing SOTA methods on the standard OOD benchmark DomainBed as well

Gorlin Goltz syndrome: a rare case report

Gorlin-Goltz syndrome is uncommon multisystemic disease with an autosomal dominant trait, with complete penetrance and variable expressivity, though sporadic cases have been described. We report a case of 18 years old male patient having features of Gorlin Goltz syndrome. Gorlin-Goltz syndrome is characterized by multiple basal cell nevi or carcinomas, odontogenic keratocysts, palmar and/or plantar pits, calcification of the falx cerebri, and is associated with internal malignancies. It is important to know the major and minor criteria for the diagnosis and early preventive treatment of this syndrome

Decoding the learning curve of non-descent vaginal hysterectomy in the era of laparoscopy- experience at a Zonal Hospital

Background: Despite of the increasing popularity of laparoscopic hysterectomy, vaginal route still stays pertinent. Non descent vaginal hysterectomy (NDVH) involves d steep learning curve and hence, should be a fundamental part of every Gynaecology residency program. Objective of the study was to assess the learning curve of NDVH surgery skill at a Military Zonal Hospital by a single Specialist over a period of two years.Methods: Retrospective study conducted at Military Hospital, Agra between June 2015 to June 2017 on 30 patients who underwent NDVH for benign gynaecological conditions.Results: The average blood loss was noted to reduce from a mean of 285ml (±108.94) in the first 20 cases (Group 1) to 227ml (±110.89) in the next 10 cases (Group 2) despite of the average uterine size increasing from 8.5 (±1.43) weeks in Group 1 to 10.2 (±2.39) weeks in Group 2. The average time taken in minutes was also seen to reduce from 89.75 (±12.62) in Group 1 to 70.5 (±16.50) in Group 2 indicating an improvement of surgical skills. The average 24 hr post-operative haemoglobin fall of 0.8gm% was similar between the two groups.Conclusions: Acquiring NDVH skills is a slow but rewarding process. NDVH involves no incisions, no elaborate set-up, avoids complications of general anaesthesia and pneumo-peritoneum and displays similar results as of laparoscopy. In limited resource countries vaginal route may be the only available minimally invasive option for hysterectomy. Hence, it’s pertinent that Gynecologists are trained in the same.

An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network

The low-cost, user-friendly, and convenient nature of Automatic Fingerprint Recognition Systems (AFRS) makes them suitable for a wide range of applications. This spreading use of AFRS also makes them vulnerable to various security threats. Presentation Attack (PA) or spoofing is one of the threats which is caused by presenting a spoof of a genuine fingerprint to the sensor of AFRS. Fingerprint Presentation Attack Detection (FPAD) is a countermeasure intended to protect AFRS against fake or spoof fingerprints created using various fabrication materials. In this paper, we have proposed a Convolutional Neural Network (CNN) based technique that uses a Generative Adversarial Network (GAN) to augment the dataset with spoof samples generated from the proposed Open Patch Generator (OPG). This OPG is capable of generating realistic fingerprint samples which have no resemblance to the existing spoof fingerprint samples generated with other materials. The augmented dataset is fed to the DenseNet classifier which helps in increasing the performance of the Presentation Attack Detection (PAD) module for the various real-world attacks possible with unknown spoof materials. Experimental evaluations of the proposed approach are carried out on the Liveness Detection (LivDet) 2015, 2017, and 2019 competition databases. An overall accuracy of 96.20\%, 94.97\%, and 92.90\% has been achieved on the LivDet 2015, 2017, and 2019 databases, respectively under the LivDet protocol scenarios. The performance of the proposed PAD model is also validated in the cross-material and cross-sensor attack paradigm which further exhibits its capability to be used under real-world attack scenarios