Estimating Post-Synaptic Effects for Online Training of Feed-Forward SNNs

Facilitating online learning in spiking neural networks (SNNs) is a key step in developing event-based models that can adapt to changing environments and learn from continuous data streams in real-time. Although forward-mode differentiation enables online learning, its computational requirements restrict scalability. This is typically addressed through approximations that limit learning in deep models. In this study, we propose Online Training with Postsynaptic Estimates (OTPE) for training feed-forward SNNs, which approximates Real-Time Recurrent Learning (RTRL) by incorporating temporal dynamics not captured by current approximations, such as Online Training Through Time (OTTT) and Online Spatio-Temporal Learning (OSTL). We show improved scaling for multi-layer networks using a novel approximation of temporal effects on the subsequent layer's activity. This approximation incurs minimal overhead in the time and space complexity compared to similar algorithms, and the calculation of temporal effects remains local to each layer. We characterize the learning performance of our proposed algorithms on multiple SNN model configurations for rate-based and time-based encoding. OTPE exhibits the highest directional alignment to exact gradients, calculated with backpropagation through time (BPTT), in deep networks and, on time-based encoding, outperforms other approximate methods. We also observe sizeable gains in average performance over similar algorithms in offline training of Spiking Heidelberg Digits with equivalent hyper-parameters (OTTT/OSTL - 70.5%; OTPE - 75.2%; BPTT - 78.1%)

Correlation of blood pressure with Body Mass Index (BMI) and Waist to Hip Ratio (WHR) in middle aged men

Obesity and cardiovascular risks are closely associated. Hypertension is themost common and early complication of obesity. Obesity is measured with different parameters like Body Mass Index, Waist to Hip Ratio etc. In the present study we have tried to link parameters of obesity with hypertension. We have found that in hypertensive middle aged Indian males diastolic blood pressure showed a better correlation with Waist to Hip Ratio rather than with Body Mass Index.KEW WORDS: Obesity; Hypertension; Body Mass Index (BMI); Waist to Hip Ratio (WHR

Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks

The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and energy savings for neural network inference, facilitating the use of DNNs on edge computing platforms. Recent efforts at quantizing DNNs have employed a range of techniques encompassing progressive quantization, step-size adaptation, and gradient scaling. This paper proposes a new quantization approach for mixed precision convolutional neural networks (CNNs) targeting edge-computing. Our method establishes a new pareto frontier in model accuracy and memory footprint demonstrating a range of quantized models, delivering best-in-class accuracy below 4.3 MB of weights (wgts.) and activations (acts.). Our main contributions are: (i) hardware-aware heterogeneous differentiable quantization with tensor-sliced learned precision, (ii) targeted gradient modification for wgts. and acts. to mitigate quantization errors, and (iii) a multi-phase learning schedule to address instability in learning arising from updates to the learned quantizer and model parameters. We demonstrate the effectiveness of our techniques on the ImageNet dataset across a range of models including EfficientNet-Lite0 (e.g., 4.14MB of wgts. and acts. at 67.66% accuracy) and MobileNetV2 (e.g., 3.51MB wgts. and acts. at 65.39% accuracy)

Controversies in the management of primary sclerosing cholangitis

Primary sclerosing cholangitis (PSC) remains a rare but significant disease, which affects mainly young males in association with inflammatory bowel disease. There have been few advances in the understanding of the pathogenesis of the condition and no therapeutics with proven mortality benefit aside from liver transplantation. There remain areas of controversy in the management of PSC which include the differentiation from other cholangiopathies, in particular immunoglobulin G4 related sclerosing cholangitis, the management of dominant biliary strictures, and the role of ursodeoxycholic acid. In addition, the timing of liver transplantation in PSC remains difficult to predict with standard liver severity scores. In this review, we address these controversies and highlight the latest evidence base in the management of PSC

Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators

The energy efficiency of neuromorphic hardware is greatly affected by the energy of storing, accessing, and updating synaptic parameters. Various methods of memory organisation targeting energy-efficient digital accelerators have been investigated in the past, however, they do not completely encapsulate the energy costs at a system level. To address this shortcoming and to account for various overheads, we synthesize the controller and memory for different encoding schemes and extract the energy costs from these synthesized blocks. Additionally, we introduce functional encoding for structured connectivity such as the connectivity in convolutional layers. Functional encoding offers a 58% reduction in the energy to implement a backward pass and weight update in such layers compared to existing index-based solutions. We show that for a 2 layer spiking neural network trained to retain a spatio-temporal pattern, bitmap (PB-BMP) based organization can encode the sparser networks more efficiently. This form of encoding delivers a 1.37x improvement in energy efficiency coming at the cost of a 4% degradation in network retention accuracy as measured by the van Rossum distance.Comment: submitted to ISCAS202

Recent advances in biosensing approaches for point-of-care breast cancer diagnostics: challenges and future prospects

Timely and accurate diagnosis of breast cancer is essential for efficient treatment and the best possible survival rates. Biosensors have emerged as a smart diagnostic platform for the detection of biomarkers specific to the onset, recurrence, and therapeutic drug monitoring of breast cancer. There have been exciting recent developments, including significant improvements in the validation, sensitivity, specificity, and integration of sample processing steps to develop point-of-care (POC) integrated micro-total analysis systems for clinical settings. The present review highlights various biosensing modalities (electrical, optical, piezoelectric, mass, and acoustic sensing). It provides deep insights into their design principles, signal amplification strategies, and comparative performance analysis. Finally, this review emphasizes the status of existing integrated micro-total analysis systems (μ-TAS) for personalized breast cancer therapeutics and associated challenges and outlines the approach required to realize their successful translation into clinical settings

Sensitivity of an Ultrasonic Technique for Axial Stress Determination

In machine assembly it is often required that bolts used to fasten machine parts be installed with specific design preloads. Because it is inconvenient to measure preload directly, preload specifications are usually based on some more easily measured quantity with which the level of preload may be correlated. Most often this quantity is the torque to be applied to the bolt at installation. Studies by Blake and Kurtz [1] and Heyman [2] have shown that when bolts are torqued into place, the fraction of applied torque which translates into useful preload is small and widely variable. This is so because the large majority of applied torque is absorbed in overcoming friction in the bolt’s threads and at the underside of the bolt’s head. Consequently, even though the torque to install different bolts may be identical, small variations in frictional conditions from one installation to the next can result in large variations in preload. The unreliability of torque as an indicator of preload has been the motivating factor behind the development of a number of alternate methods of measurement [2–5]

Tumor radiomic features complement clinico-radiological factors in predicting long-term local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancers

OBJECTIVE: To study if pre-treatment CT texture features in locally advanced squamous cell carcinoma of laryngo-pharynx can predict long-term local control and laryngectomy free survival (LFS). METHODS: Image texture features of 60 patients treated with chemoradiation (CTRT) within an ethically approved study were studied on contrast-enhanced images using a texture analysis research software (TexRad, UK). A filtration-histogram technique was used where the filtration step extracted and enhanced features of different sizes and intensity variations corresponding to a particular spatial scale filter (SSF): SSF = 0 (without filtration), SSF = 2 mm (fine texture), SSF = 3-5 mm (medium texture) and SSF = 6 mm (coarse texture). Quantification by statistical and histogram technique comprised mean intensity, standard-deviation, entropy, mean positive pixels, skewness and kurtosis. The ability of texture analysis to predict LFS or local control was determined using Kaplan-Meier analysis and multivariate cox model. RESULTS: Median follow-up of patients was 24 months (95% CI:20-28). 39 (65%) patients were locally controlled at last follow-up. 10 (16%) had undergone salvage laryngectomy after CTRT. For both local control & LFS, threshold optimal cut-off values of texture features were analyzed. Medium filtered-texture feature that were associated with poorer laryngectomy free survival were entropy ≥4.54, (p = 0.006), kurtosis ≥4.18; p = 0.019, skewness ≤-0.59, p = 0.001, and standard deviation ≥43.18; p = 0.009). Inferior local control was associated with medium filtered features entropy ≥4.54; p 0.01 and skewness ≤ - 0.12; p = 0.02. Using fine filters, entropy ≥4.29 and kurtosis ≥-0.27 were also associated with inferior local control (p = 0.01 for both parameters). Multivariate analysis showed medium filter entropy as an independent predictor for LFS and local control (p < 0.001 & p = 0.001). CONCLUSION: Medium texture entropy is a predictor for inferior local control and laryngectomy free survival in locally advanced laryngo-pharyngeal cancer and this can complement clinico-radiological factors in predicting prognosticating these tumors. ADVANCES IN KNOWLEDGE: Texture features play an important role as a surrogate imaging biomarker for predicting local control and laryngectomy free survival in locally advanced laryngo-pharyngeal tumors treated with definitive chemoradiation

Molecular Valves for Controlling Gas Phase Transport Made from Discrete Angstrom-Sized Pores in Graphene

An ability to precisely regulate the quantity and location of molecular flux is of value in applications such as nanoscale 3D printing, catalysis, and sensor design. Barrier materials containing pores with molecular dimensions have previously been used to manipulate molecular compositions in the gas phase, but have so far been unable to offer controlled gas transport through individual pores. Here, we show that gas flux through discrete angstrom-sized pores in monolayer graphene can be detected and then controlled using nanometer-sized gold clusters, which are formed on the surface of the graphene and can migrate and partially block a pore. In samples without gold clusters, we observe stochastic switching of the magnitude of the gas permeance, which we attribute to molecular rearrangements of the pore. Our molecular valves could be used, for example, to develop unique approaches to molecular synthesis that are based on the controllable switching of a molecular gas flux, reminiscent of ion channels in biological cell membranes and solid state nanopores.Comment: to appear in Nature Nanotechnolog