Multi-modal Sensor Registration for Vehicle Perception via Deep Neural Networks

The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.Comment: 7 pages, double column, IEEE format, accepted at IEEE HPEC 201

Indian Legal NLP Benchmarks : A Survey

Availability of challenging benchmarks is the key to advancement of AI in a specific field.Since Legal Text is significantly different than normal English text, there is a need to create separate Natural Language Processing benchmarks for Indian Legal Text which are challenging and focus on tasks specific to Legal Systems. This will spur innovation in applications of Natural language Processing for Indian Legal Text and will benefit AI community and Legal fraternity. We review the existing work in this area and propose ideas to create new benchmarks for Indian Legal Natural Language Processing

FIDES: Enhancing Trust in Reconfigurable Based Hardware Systems

Extensive use of third party IP cores (e.g., HDL, netlist) and open source tools in the FPGA application design and development process in conjunction with the inadequate bitstream protection measures have raised crucial security concerns in the past for reconfigurable hardware systems. Designing high fidelity and secure methodologies for FPGAs are still infancy and in particular, there are almost no concrete methods/techniques that can ensure trust in FPGA applications not entirely designed and/or developed in a trusted environment. This work strongly suggests the need for an anomaly detection capability within the FPGAs that can continuously monitor the behavior of the underlying FPGA IP cores and the communication activities of IP cores with other IP cores or peripherals for any abnormalities. To capture this need, we propose a technique called FIDelity Enhancing Security (FIDES) methodology for FPGAs that uses a combination of access control policies and behavior learning techniques for anomaly detection. FIDES essentially comprises of two components: (i) {\em Trusted Wrappers}, a layer of monitors with sensing capabilities distributed across the FPGA fabric; these wrappers embed the output of each IP core $i$ with a tag $\tau_i$ according to the pre-defined security policy $\Pi$ and also verifies the embeddings of each input to the IP core to detect any violation of policies. The use of tagging and tracking enables us to capture the normal interactions of each IP core with its environment (e.g., other IP cores, memory, OS or I/O ports). {\em Trusted Wrappers} also monitors the statistical properties exhibited by each IP core module on execution such as power consumption, number of clock cycles and timing variations to detect any anomalous operations; (ii) a {\em Trusted Anchor} that monitors the communication between the IP cores and the peripherals with regard to the centralized security policies $\Psi$ as well as the statistical properties produced by the peripherals. We target FIDES architecture on a Xilinx Zynq 7020 device implemented with a red-black system comprising of sensitive and non-sensitive IP cores. Our results show that FIDES implementation leads to only 1-2\% overhead in terms of the logic resources per wrapper and incurs minimal latency per wrapper for tag verification and embedding. On the other hand, as compared to the baseline implementation, when all the communications within the system are routed to the Trusted Anchor for centralized policy checking and verification, a latency of 1.5X clock cycles is observed; this clearly manifests the advantage of using distributed wrappers as opposed to centralized policy checking

WILSON’S DISEASE: ATYPICAL IMAGING FEATURES

Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities) and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present

Wilson’s disease: Atypical Imaging features

Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17-year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities) and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present

Reversible dementia: The imitation game

Rapidly progressive dementia (RPD) is an emergency in behavioural or cognitive neurology. Many rare neuroinfections like Neurosyphilis may be missed, if they are not thoroughly evaluated. We report a patient with subacute onset and progressive cognitive decline, extrapyramidal involvement and myoclonic jerks who was initially suspected as probable autoimmune encephalitis or Creutzfeldt-Jakob disease (CJD). Investigations revealed positive serum and cerebrospinal fluid (CSF) Venereal Disease Research Laboratory test (VDRL). On treatment with penicillin, he developed Jarisch-Herxheimer reaction and was treated symptomatically. After two weeks of penicillin, he improved significantly and except for mild short term memory recall, he is asymptomatic for last two years

Clinical, molecular imaging and biomarker concordance in the diagnosis of Alzheimer’s disease and vascular dementia

BackgroundThe CSF and plasma biomarkers may help clinicians in differentiating between Alzheimer and Vascular dementia. Apart from biopsy, FDG PET, MRI Brain and clinical examination gives a reliable diagnosis of AD and VaD.AimsTo evaluate the correlation of molecular imaging (FDG PET brain) with CSF Alzheimer profile and Plasma hemostatic biomarkers in Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD) and Vascular dementia (VaD).Methods Neuropsychological assessment, MRI brain, FDG-PET brain, CSF biomarkers of AD (Aβ42 and total tau) and plasma hemostatic biomarkers (Fibrinogen and D dimer) were done for evaluation.Results FDG PET Brain, plasma fibrinogen and D dimer were done in 68 patients. CSF biomarkers were done in 46 patients. Clinical-PET discordance was found in 7 patients. One patient of MCI-VaSC had a normal PET study with elevated hemoststic biomarkers. Those with clinical diagnosis of Alzheimer’s disease either had normal hemostatic biomarkers and supporting Alzheimer profile CSF biomarkers where they were done. The discordant vascular group had elevated plasma hemostatic biomarker with normal CSF profile. Even those who were reported as FTD in PET imaging had Alzheimer profile and normal hemostatic factors.ConclusionFDG PET brain findings were concordant with the CSF biomarkers (CSF Aβ42, Total tau and Tau/Aβ42 ratio) in Alzheimer’s disease and Haemostatic biomarkers (Plasma Fibrinogen and D dimer) in vascular dementia. In clinical and molecular imaging discordance, biomarkers help in making a reliable diagnosis which favours the clinical assessment

Clinical, molecular imaging and biomarker concordance in the diagnosis of Alzheimer’s disease and vascular dementia

Background The CSF and plasma biomarkers may help clinicians in differentiating between Alzheimer and Vascular dementia. Apart from biopsy, FDG PET, MRI Brain and clinical examination gives a reliable diagnosis of AD and VaD. Aims To evaluate the correlation of molecular imaging (FDG PET brain) with CSF Alzheimer profile and Plasma hemostatic biomarkers in Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD) and Vascular dementia (VaD). Methods Neuropsychological assessment, MRI brain, FDG-PET brain, CSF biomarkers of AD (Aβ42 and total tau) and plasma hemostatic biomarkers (Fibrinogen and D dimer) were done for evaluation. Results FDG PET Brain, plasma fibrinogen and D dimer were done in 68 patients. CSF biomarkers were done in 46 patients. Clinical-PET discordance was found in 7 patients. One patient of MCI-VaSC had a normal PET study with elevated haemostatic biomarkers. Those with clinical diagnosis of Alzheimer’s disease either had normal hemostatic biomarkers and supporting Alzheimer profile CSF biomarkers where they were done. The discordant vascular group had elevated plasma hemostatic biomarker with normal CSF profile. Even those who were reported as FTD in PET imaging had Alzheimer profile and normal hemostatic factors. Conclusion FDG PET brain findings were concordant with the CSF biomarkers (CSF Aβ42, Total tau and Tau/Aβ42 ratio) in Alzheimer’s disease and Haemostatic biomarkers (Plasma Fibrinogen and D dimer) in vascular dementia. In clinical and molecular imaging discordance, biomarkers help in making a reliable diagnosis which favours the clinical assessment