Student Modeling and Analysis in Adaptive Instructional Systems

There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-the-art review of 11 years of research (2010-2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability

NeuroSim Simulator for Compute-in-Memory Hardware Accelerator: Validation and Benchmark

Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware accelerators. A simulator with options of various mainstream and emerging memory technologies, architectures, and networks can be a great convenience for fast early-stage design space exploration of CIM hardware accelerators. DNN+NeuroSim is an integrated benchmark framework supporting flexible and hierarchical CIM array design options from a device level, to a circuit level and up to an algorithm level. In this study, we validate and calibrate the prediction of NeuroSim against a 40-nm RRAM-based CIM macro post-layout simulations. First, the parameters of a memory device and CMOS transistor are extracted from the foundry’s process design kit (PDK) and employed in the NeuroSim settings; the peripheral modules and operating dataflow are also configured to be the same as the actual chip implementation. Next, the area, critical path, and energy consumption values from the SPICE simulations at the module level are compared with those from NeuroSim. Some adjustment factors are introduced to account for transistor sizing and wiring area in the layout, gate switching activity, post-layout performance drop, etc. We show that the prediction from NeuroSim is precise with chip-level error under 1% after the calibration. Finally, the system-level performance benchmark is conducted with various device technologies and compared with the results before the validation. The general conclusions stay the same after the validation, but the performance degrades slightly due to the post-layout calibration

CT-Guided Stellate Ganglion Pulsed Radiofrequency Stimulation for Facial and Upper Limb Postherpetic Neuralgia

Objective: Postherpetic neuralgia (PHN) is the most common complication of herpes zoster, manifesting as a persistent, spontaneous, knife-like pain or paroxysmal burning that seriously affects a patient’s quality of life. An effective treatment of PHN is lacking. This retrospective study examined the efficacy and safety of stellate ganglion (SG) pulsed radiofrequency (PRF) on facial and upper limb PHN.Methods: Eighty-four patients with PHN on the face or upper limbs were enrolled for the study. Patients were randomly divided into two surgical groups according to the order of enrollment; one group underwent SG block (SG-B group, n = 42) and the other underwent SG pulsed radiofrequency (SG-P group, n = 42). After surgery, patients were followed at 1 week, 2 weeks, 1 month, 3 months, and 6 months. Observation at each follow-up included basic patient characteristics, visual analog scale (VAS), quality of life (QOL) using Physical Component Summary (PCS), and Mental Component Summary (MCS) to assess, total effective rate, complications and side effects.Results: Compared with preoperative values, VAS decreased in both groups after surgery (P < 0.05). In the SG-B group, VAS increased after 1 month, while in the SG-P group, VAS gradually decreased at later follow-up time points. VAS decreased more significantly in the SG-P group after 1 month (P < 0.05). PCS and MCS increased in both groups after the operation, and the difference was significant compared with preoperative values (P < 0.05). The total effective rates of the SG-B and SG-P groups were 64.3 and 83.3%, respectively. The total effective rate of the SG-P group was higher than that of the SG-B group (P < 0.05). The incidence of complications and side effects in the SG-B group was higher than that in the SG-P group (P < 0.05).Conclusion: SG pulsed radiofrequency treatment of facial and upper limb PHN is safe and effective. It is a treatment method worth promoting

Neurological Diseases With Autism Spectrum Disorder: Role of ASD Risk Genes

Autism spectrum disorder (ASD) is frequently comorbid with other neurological disorders such as intellectual disability (ID) or global development delay (GDD) and epilepsy. The pathogenesis of ASD is complex. So far, studies have identified more than 1000 ASD risk genes. Most of them were also reported to relate with other neurological diseases, and only several of them have been confirmed as pathogenic genes for autism. Little is known about the roles of these risk genes in neurological diseases with ASD. In the present study, we recruited a cohort of 158 neurological disorder probands with 163 variants of 48 ASD risk genes. Of these, 50 individuals (31.6%) were diagnosed with ASD. In the ASD patient subset, we identified several rarely reported candidate genes including DOLK, USH2A, and HUWE1. In a comparison of patients with neurological disorders with and without ASD, we found that ID/GDD was frequently comorbid with ASD whereas epilepsy was more common in the non-ASD group. Statistical analyses of all possible risk factors implicated that variants in synaptic genes, especially non-voltage-gated ion channel genes and in transcriptional and chromosome genes were related to ASD, but none of the investigated environmental factors was. Our results are useful for the future diagnosis and prognosis of patients with neurological disorders and emphasize the utility of genetic screening