A Deep Learning Approach to Structured Signal Recovery

In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mildly nonlinear measurements, that learns a structured representation from training data, and that efficiently computes a signal estimate. In particular, we apply a stacked denoising autoencoder (SDA), as an unsupervised feature learner. SDA enables us to capture statistical dependencies between the different elements of certain signals and improve signal recovery performance as compared to the CS approach

Privacy-Preserving Social Ambiance Measure From Free-Living Speech Associates With Chronic Depressive and Psychotic Disorders

A social interaction consists of contributions by the individual, the environment and the interaction between the two. Ideally, to enable effective assessment and interventions for social isolation, an issue inherent to depressive and psychotic illnesses, the isolation must be identified in real-time and at an individual level. However, research addressing sociability deficits is largely focused on determining loneliness, rather than isolation, and lacks focus on the richness of the social environment the individual revolves in. In this paper, We describe the development of an automated, objective and privacy-preserving Social Ambiance Measure (SAM) that converts unconstrained audio recordings collected from wrist-worn audio-bands into four levels, ranging from none to active. The ambiance levels are based on the number of simultaneous speakers, which is a proxy for overall social activity in the environment. Results show that social ambiance patterns and time spent at each ambiance level differed between participants with depressive or psychotic disorders and healthy controls. Individuals with depression/psychosis spent less time in diverse environments and less time in moderate/active ambiance levels. Moreover, social ambiance patterns are found associated with the severity of self-reported depression, anxiety symptoms and personality traits. The results in this paper suggest that objectively measured social ambiance can be used as a marker of sociability, and holds potential to be leveraged to better understand social isolation and develop effective interventions for sociability challenges, thus improving mental health outcomes

Functional outcome of patients undergoing lumbar discectomy

Background: Sciatica resulting from a lumbar intervertebral disc herniation is the most common cause of radicular leg pain in adult working population. It can be treated with both conservative and operative methods. In our study, surgical treatment of lumbar disc prolapse has been done by open discectomy. We wish to assess the outcome of surgery in patients with lumbar disc prolapse undergoing lumbar discectomy.Methods: 40 patients were included in this study and were followed up for up to 1 year postoperatively. We assessed the outcome of each patient with ODI and VAS post-operatively and on follow-up at 3 weeks, 6 months and 1 year. Subjective evaluation of the patient’s satisfaction at the final follow-up was also done.Results: We found that males had higher incidence of PIVD with an average duration of symptoms before surgery about 8.62 months. Left side was most involved and level l4-l5 was most involved level. The mean ODI and VAS score pre-operatively were 26.85±4.20 and 7.73±0.88 respectively, which changed to 4.48±5.15 and 1.70±1.57, respectively at 1 year post-operative follow-up. These were statistically highly significant. Most of the patients (34) gave a subjective evaluation as excellent at 1 year follow-up.Conclusions: Our study established that open discectomy has a satisfactory functional outcome and leads to a significant improvement in the patients’ quality of life

Use of dorsalis pedis artery flap in coverage of distal lower leg defects

Soft tissue defect in the distal one third of leg have always posed a challenge for reconstructive surgeons. Such wounds are difficult to manage due the tenuous blood supply, limited subcutaneous cover over the tendons and bones. The aim of our study is to investigate the outcome of Dorsalis pedis artery flap for the coverage of such defects. In the present study, we share our clinical experience with the use of dorsalis pedis artery flap for the coverage of defect in the distal one third leg. This is a series of 4 cases where dorsalis pedis artery flap was used to cover lower one third defect. One case had focal squamous cell carcinoma due to long standing post burns contracture in distal one third of leg anteriorly. Other 3 cases had chronic non healing ulcer in the malleolar region. Patient outcome was assessed according to patients’ age distribution, duration of surgery, hospital stay, and post-operative complications. All 4 patients had excellent outcome with no major donor site complications, infection, and graft loss. Donor site was closed with split thickness skin graft. One patient developed a minor raw area over the dorsum of foot which healed secondarily. Although a potential risk in applying this flap is insufficient venous drainage, no problems with blood inflow or outflow were encountered in the present case series. The flaps survived, and the patient had good postoperative outcome. Hence dorsalis pedis flap can be used for the coverage of the distal foot as a good option

Medial plantar artery flap: a versatile workhorse flap for foot reconstruction, our experience

Soft tissue defect in the foot is commonly seen as it is more prone to trophic ulcers since it is the main weight bearing area of the body. Reconstruction of the weight bearing area of the foot requires the provision of a stable, supple, durable and preferably sensate skin coverage. Following Sir Gilli’s principle of replacing like with like, medial plantar artery flap provides an anatomically similar, glabrous skin for coverage on the plantar surface. In the present study, we share our clinical experience with the use of medial plantar artery flap for coverage of soft tissue defect over sole of foot. At our institution, a total of 10 patients presented with soft tissue defect of the sole, underwent medial plantar artery flap coverage. All the 10 patients were diagnosed cases of type 2 DM. patient outcome was assessed according to patients’ age distribution, duration of surgery, hospital stay, and post operative complications. Out of all the 10 patients, 5 were male and 5 were female. All the flaps healed uneventfully without major complications like partial flap necrosis. Donor site was covered with split thickness skin graft. There was suture site dehience in 2 cases which healed with secondary healing. Medial plantar artery flap has been described as an optimal reconstructive option for this type of soft tissue defect.

Linking convolutional kernel size to generalization bias in face analysis CNNs

Training dataset biases are by far the most scrutinized factors when explaining algorithmic biases of neural networks. In contrast, hyperparameters related to the neural network architecture have largely been ignored even though different network parameterizations are known to induce different implicit biases over learned features. For example, convolutional kernel size is known to affect the frequency content of features learned in CNNs. In this work, we present a causal framework for linking an architectural hyperparameter to out-of-distribution algorithmic bias. Our framework is experimental, in that we train several versions of a network with an intervention to a specific hyperparameter, and measure the resulting causal effect of this choice on performance bias when a particular out-of-distribution image perturbation is applied. In our experiments, we focused on measuring the causal relationship between convolutional kernel size and face analysis classification bias across different subpopulations (race/gender), with respect to high-frequency image details. We show that modifying kernel size, even in one layer of a CNN, changes the frequency content of learned features significantly across data subgroups leading to biased generalization performance even in the presence of a balanced dataset.Comment: WACV 202