Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition

Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in the target technology. The primary challenge is the significant difference in dimensionality and distribution of feature sets between the source and the target technologies. In this paper, we propose TRANSFER, a generic framework for knowledge transfer between a source and a target technology. TRANSFER uses a language-based representation of a hand gesture, which captures a temporal combination of concepts such as handshape, location, and movement that are semantically related to the meaning of a word. By utilizing a pre-specified syntactic structure and tokenizer, TRANSFER segments a hand gesture into tokens and identifies individual components using a token recognizer. The tokenizer in this language-based recognition system abstracts the low-level technology-specific characteristics to the machine interface, enabling the design of a discriminator that learns technology-invariant features essential for recognition of gestures in both source and target technologies. We demonstrate the usage of TRANSFER for three different scenarios: a) transferring knowledge across technology by learning gesture models from video and recognizing gestures using WiFi, b) transferring knowledge from video to accelerometer, and d) transferring knowledge from accelerometer to WiFi signals

The Expert Knowledge combined with AI outperforms AI Alone in Seizure Onset Zone Localization using resting state fMRI

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI were collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8& and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7%. Conversely, a DL only model yielded an accuracy of less than 50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter and end in vascular regions are seen as the most discriminative expert identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE, but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and pre-operative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.Comment: Accepted in Frontiers in Neurology journal, section Artificial Intelligenc

Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy

Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.Comment: This paper is currently under review in IEEE Journa

High Fidelity Fast Simulation of Human in the Loop Human in the Plant (HIL-HIP) Systems

Non-linearities in simulation arise from the time variance in wireless mobile networks when integrated with human in the loop, human in the plant (HIL-HIP) physical systems under dynamic contexts, leading to simulation slowdown. Time variance is handled by deriving a series of piece wise linear time invariant simulations (PLIS) in intervals, which are then concatenated in time domain. In this paper, we conduct a formal analysis of the impact of discretizing time-varying components in wireless network-controlled HIL-HIP systems on simulation accuracy and speedup, and evaluate trade-offs with reliable guarantees. We develop an accurate simulation framework for an artificial pancreas wireless network system that controls blood glucose in Type 1 Diabetes patients with time varying properties such as physiological changes associated with psychological stress and meal patterns. PLIS approach achieves accurate simulation with greater than 2.1 times speedup than a non-linear system simulation for the given dataset.Comment: To appear in ACM MSWIM 202

EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures

Gestures that share similarities in their forms and are related in their meanings, should be easier for learners to recognize and incorporate into their existing lexicon. In that regard, to be more readily accepted as standard by the Deaf and Hard of Hearing community, technical gestures in American Sign Language (ASL) will optimally share similar in forms with their lexical neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical relations within a set of technical gestures. We use automated identification for 3 unique sub-lexical properties in ASL- location, handshape and movement. EdGCon assigned an iconicity rating based on the lexical property similarities of the new gesture with an existing set of technical gestures and the relatedness of the meaning of the new technical word to that of the existing set of technical words. We collected 30 ad hoc crowdsourced technical gestures from different internet websites and tested them against 31 gestures from the DeafTEC technical corpus. We found that EdGCon was able to correctly auto-assign the iconicity ratings 80.76% of the time.Comment: Accepted for publication in ACM SAC 202

Budesonide Oral Suspension Improves Symptomatic, Endoscopic, and Histologic Parameters Compared With Placebo in Patients With Eosinophilic Esophagitis

Background & Aims Pharmacologic treatment of eosinophilic esophagitis (EoE) is limited to off-label use of corticosteroids not optimized for esophageal delivery. We performed a randomized, controlled phase 2 trial to assess the ability of budesonide oral suspension (BOS), a novel muco-adherent topical steroid formulation, to reduce symptoms and esophageal eosinophilia in adolescents and adults with EoE. Methods In this multicenter, randomized, double-blind, placebo-controlled, parallel-group trial, 93 EoE patients between the ages of 11 and 40 years with dysphagia and active esophageal eosinophilia were randomized to receive either BOS 2 mg or placebo twice daily for 12 weeks. Co-primary outcomes were change in Dysphagia Symptom Questionnaire (DSQ) score from baseline, and proportion of patients with a histologic response (≤6 eosinophils/high-power field) after treatment. Endoscopic severity scores and safety parameters were assessed. Results At baseline, mean DSQ scores were 29.3 and 29.0, and mean peak eosinophil counts were 156 and 130 per hpf in the BOS and placebo groups, respectively. After treatment, DSQ scores were 15.0 and 21.5, and mean peak eosinophil counts were 39 and 113 per high-power field, respectively (P < .05 for all). For BOS vs placebo, change in DSQ score was −14.3 vs −7.5 (P = .0096), histologic response rates were 39% vs 3% (P < .0001), and change in endoscopic severity score was −3.8 vs 0.4 (P < .0001). Adverse events were similar between groups. Conclusions Treatment with BOS was well tolerated in adolescent and young adult patients with EoE and resulted in improvement in symptomatic, endoscopic, and histologic parameters using validated outcome instruments

Managing File Subsystem Data Streams for Databases on Networked Systems

One important activity for networked database systems that distribute data across several workstations is moving data between the file and network subsystems. It is possible to create data streams in the operating system kernel. If provided on a system, they allow user level processes to request transfer of data without having t copy it into the user space. This is particularly useful for data whose content or format is not modified during the transfer. In this paper we present a conservative criterion for access and control for the management of such data streams for databases in a networked environment, and define the implementation requirements for achieving the criterion. The approach is to maintain at least the current level of access management. We define the specific implementation semantics that this criterion entails. <P

Wireless Power Transmission

Wireless Power Transmission through inductive coupling is one of the new emerging technologies that will bring tremendous change in human life. Due to shortage of time and fast running life style it is difficult to carry the complete charging set which increases the demand of the wirelessly charged products. Wireless power transfer is one of the simplest and inexpensive ways of charging as it eliminate the use of conventional copper cables and current carrying wires. In this paper, a technique is devised for a wireless power transfer through induction, and a feasible design is modeled accordingly. The technique used in this paper is the inductive coupling as it the easiest method of high efficiency power transfer without using wired medium (eg, transformer). In this paper the result of experiment is given which is done to check wireless working of a simple application by glowing LED, and charging a mobile. Wireless power transfer is not much affected by placing hurdles likes books, hands and plastic between transceiver and receiver. This research work focuses on the study of wireless power transfer for the purpose of transferring cut and dried amount of energy at maximum efficiency

Regulation and Regulatory Role of WNT Signaling in Potentiating FSH Action during Bovine Dominant Follicle Selection

Follicular development occurs in wave like patterns in monotocous species such as cattle and humans and is regulated by a complex interaction of gonadotropins with local intrafollicular regulatory molecules. To further elucidate potential mechanisms controlling dominant follicle selection, granulosa cell RNA harvested from F1 (largest) and F2 (second largest) follicles isolated at predeviation (PD) and onset of diameter deviation (OD) stages of the first follicular wave was subjected to preliminary RNA transcriptome analysis. Expression of numerous WNT system components was observed. Hence experiments were performed to test the hypothesis that WNT signaling modulates FSH action on granulosa cells during follicular waves. Abundance of mRNA for WNT pathway members was evaluated in granulosa cells harvested from follicles at emergence (EM), PD, OD and early dominance (ED) stages of the first follicular wave. In F1 follicles, abundance of CTNNB1 and DVL1 mRNAs was higher and AXIN2 mRNA was lower at ED versus EM stages and DVL1 and FZD6 mRNAs were higher and AXIN2mRNA was lower in F1 versus F2 follicle at the ED stage. Bovine granulosa cells were treated in vitro with increasing doses of the WNT inhibitor IWR-1+/− maximal stimulatory dose of FSH. IWR-1 treatment blocked the FSH-induced increase in granulosa cell numbers and reduced the FSH-induced increase in estradiol. Granulosa cells were also cultured in the presence or absence of FSH +/− IWR-1 and hormonal regulation of mRNA for WNT pathway members and known FSH targets determined. FSH treatment increased CYP19A1, CCND2, CTNNB1, AXIN2and FZD6 mRNAs and the stimulatory effect on CYP19A1 mRNA was reduced by IWR-1. In contrast, FSH reduced CARTPT mRNA and IWR-1 partially reversed the inhibitory effect of FSH. Results support temporal and hormonal regulation and a potential role for WNT signaling in potentiating FSH action during dominant follicle selection