Belief Tree Search for Active Object Recognition

Active Object Recognition (AOR) has been approached as an unsupervised learning problem, in which optimal trajectories for object inspection are not known and are to be discovered by reducing label uncertainty measures or training with reinforcement learning. Such approaches have no guarantees of the quality of their solution. In this paper, we treat AOR as a Partially Observable Markov Decision Process (POMDP) and find near-optimal policies on training data using Belief Tree Search (BTS) on the corresponding belief Markov Decision Process (MDP). AOR then reduces to the problem of knowledge transfer from near-optimal policies on training set to the test set. We train a Long Short Term Memory (LSTM) network to predict the best next action on the training set rollouts. We sho that the proposed AOR method generalizes well to novel views of familiar objects and also to novel objects. We compare this supervised scheme against guided policy search, and find that the LSTM network reaches higher recognition accuracy compared to the guided policy method. We further look into optimizing the observation function to increase the total collected reward of optimal policy. In AOR, the observation function is known only approximately. We propose a gradient-based method update to this approximate observation function to increase the total reward of any policy. We show that by optimizing the observation function and retraining the supervised LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201

A non-adiabatic approach to entanglement distribution over long distances

Entanglement distribution between trapped-atom quantum memories, viz. single atoms in optical cavities, is addressed. In most scenarios, the rate of entanglement distribution depends on the efficiency with which the state of traveling single photons can be transferred to trapped atoms. This loading efficiency is analytically studied for two-level, $V$-level, $\Lambda$-level, and double-$\Lambda$-level atomic configurations by means of a system-reservoir approach. An off-resonant non-adiabatic approach to loading $\Lambda$-level trapped-atom memories is proposed, and the ensuing trade-offs between the atom-light coupling rate and input photon bandwidth for achieving a high loading probability are identified. The non-adiabatic approach allows a broad class of optical sources to be used, and in some cases it provides a higher system throughput than what can be achieved by adiabatic loading mechanisms. The analysis is extended to the case of two double-$\Lambda$ trapped-atom memories illuminated by a polarization-entangled biphoton.Comment: 15 pages, 15 figure

220401

Internet-of-Things (IoT) devices equipped with temperature and humidity sensors, and cameras are increasingly deployed to monitor remote and human-unfriendly areas, e.g., farmlands, forests, rural highways or electricity infrastructures. Aerial data aggregators, e.g., autonomous drones, provide a promising solution for collecting sensory data of the IoT devices in human-unfriendly environments, enhancing network scalability and connectivity. The flexibility of a drone and favourable line-of-sight connection between the drone and IoT devices can be exploited to improve data reception at the drone. This article first discusses challenges of the drone-assisted data aggregation in IoT networks, such as incomplete network knowledge at the drone, limited buffers of the IoT devices, and lossy wireless channels. Next, we investigate the feasibility of onboard deep reinforcement learning-based solutions to allow a drone to learn its cruise control and data collection schedule online. For deep reinforcement learning in a continuous operation domain, deep deterministic policy gradient (DDPG) is suitable to deliver effective joint cruise control and communication decision, using its outdated knowledge of the IoT devices and network states. A case study shows that the DDPG-based framework can take advantage of the continuous actions to substantially outperform existing non-learning-based alternatives.This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV).info:eu-repo/semantics/publishedVersio

Cyclotides Isolated from an Ipecac Root Extract Antagonize the Corticotropin Releasing Factor Type 1 Receptor

Cyclotides are plant derived, cystine-knot stabilized peptides characterized by their natural abundance, sequence variability and structural plasticity. They are abundantly expressed in Rubiaceae, Psychotrieae in particular. Previously the cyclotide kalata B7 was identified to modulate the human oxytocin and vasopressin G protein-coupled receptors (GPCRs), providing molecular validation of the plants’ uterotonic properties and further establishing cyclotides as valuable source for GPCR ligand design. In this study we screened a cyclotide extract derived from the root powder of the South American medicinal plant ipecac (Carapichea ipecacuanha) for its GPCR modulating activity of the corticotropin-releasing factor type 1 receptor (CRF1R). We identified and characterized seven novel cyclotides. One cyclotide, caripe 8, isolated from the most active fraction, was further analyzed and found to antagonize the CRF1R. A nanomolar concentration of this cyclotide (260 nM) reduced CRF potency by ∼4.5-fold. In contrast, caripe 8 did not inhibit forskolin-, or vasopressin-stimulated cAMP responses at the vasopressin V2 receptor, suggesting a CRF1R-specific mode-of-action. These results in conjunction with our previous findings establish cyclotides as modulators of both classes A and B GPCRs. Given the diversity of cyclotides, our data point to other cyclotide-GPCR interactions as potentially important sources of drug-like molecules

HIV infection significantly reduces lipoprotein lipase which remains low after 6 months of antiretroviral therapy

Purpose of the study Fractional clearance rate of apolipoprotein B100-containing lipoproteins is reduced in HIV infection before and after antiretroviral (ARV) treatment [1]. We compared lipoprotein lipase (LPL) activity and gene expression in HIV-positive subjects before and 6 months after ARV with HIV-negative controls. Methods Fasting blood post heparin total and hepatic lipase activity,adiponectin, leptin, insulin, glucose, and lipid measurementswere made in 32 HIV-infected and 15 HIVnegative controls. LPL was estimated by subtractinghepatic lipase from total lipase. Adiponectin, LPL andhormone sensitive lipase (HSL) gene expression weremeasured from iliac crest subcutaneous fat biopsies.Patients were tested before, and 6 months after randomisation to AZT/3TC (n = 15) or TDF/FTC (n = 17) with EFV.Between-group comparison was by Mann-Whitney andpaired samples by the Wilcoxon signed rank tests. Summary of results There were no differences in gender, ethnicity, baseline BMI, regional fat distribution (whole body DEXA) and visceral (VAT) and subcutaneous fat (SAT) measured by abdominal CT scans between controls and patients. Trunk fat/BMI ratio, VAT and VAT:SAT ratio significantly increased after 6-month ARV therapy (p = 0.01). There were no differences between groups in serum NEFA,HOMA and leptin levels. Selected other results are shown in Table 1. Conclusion Post heparin lipoprotein lipase activity is reduced in HIV and does not return to control levels after 6 months of ARV therapy. AZT-containing regimens are associated with a greater increase in LPL, LPL gene expression and plasma adiponectin than TDF