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No full textRobust and efficient representations of dynamic stimuli in hierarchical neural networks via temporal smoothing
No full textIntroductionEfficient coding that minimizes informational redundancy of neural representations is a widely accepted neural coding principle. Despite the benefit, maximizing efficiency in neural coding can make neural representation vulnerable to random noise. One way to achieve robustness against random noise is smoothening neural responses. However, it is not clear whether the smoothness of neural responses can hold robust neural representations when dynamic stimuli are processed through a hierarchical brain structure, in which not only random noise but also systematic error due to temporal lag can be induced. MethodsIn the present study, we showed that smoothness via spatio-temporally efficient coding can achieve both efficiency and robustness by effectively dealing with noise and neural delay in the visual hierarchy when processing dynamic visual stimuli. ResultsThe simulation results demonstrated that a hierarchical neural network whose bidirectional synaptic connections were learned through spatio-temporally efficient coding with natural scenes could elicit neural responses to visual moving bars similar to those to static bars with the identical position and orientation, indicating robust neural responses against erroneous neural information. It implies that spatio-temporally efficient coding preserves the structure of visual environments locally in the neural responses of hierarchical structures. DiscussionThe present results suggest the importance of a balance between efficiency and robustness in neural coding for visual processing of dynamic stimuli across hierarchical brain structures
Surface protein-retractive and redox-degradable mesoporous organosilica nanoparticles for enhanced cancer therapy
No full textTargeted delivery along with controlled drug release is considered crucial in development of a drug delivery system (DDS) for efficient cancer treatment. In this paper, we present a strategy to obtain such a DDS by utilizing disulfide-incorporated mesoporous organosilica nanoparticles (MONs), which were engineered to minimize the surface interactions with proteins for better targeting and therapeutic performance. That is, after MONs were loaded with a chemodrug doxorubicin (DOX) through the inner pores, their outer surface was treated for conjugation to the glutathione-S-transferase (GST)-fused cell-specific affibody (Afb) (GST-Afb). These particles exhibited prompt responsivity to the SS bond-dissociating glutathione (GSH), which resulted in considerable degradation of the initial particle morphology and DOX release. As the protein adsorption to the MON surface appeared largely reduced, their targeting ability with GSH-stimulated therapeutic activities was demonstrated in vitro by employing two kinds of the GST-Afb protein, which target human cancer cells with the surface membrane receptor, HER2 or EGFR. Compared with unmodified control particles, the presented results show that our system can significantly enhance cancer-therapeutic outcomes of the loaded drug, offering a promising way of designing a more efficacious DDS
Linear and symmetric synaptic weight update characteristics by controlling filament geometry in oxide/suboxide HfOx bilayer memristive device for neuromorphic computing
No full textMemristive devices have been explored as electronic synaptic devices to mimic biological synapses for developing hardware-based neuromorphic computing systems. However, typical oxide memristive devices suffered from abrupt switching between high and low resistance states, which limits access to achieve various conductance states for analog synaptic devices. Here, we proposed an oxide/suboxide hafnium oxide bilayer memristive device by altering oxygen stoichiometry to demonstrate analog filamentary switching behavior. The bilayer device with Ti/HfO2/HfO2-x(oxygen-deficient)/Pt structure exhibited analog conductance states under a low voltage operation through controlling filament geometry as well as superior retention and endurance characteristics thanks to the robust nature of filament. A narrow cycle-to-cycle and device-to-device distribution were also demonstrated by the filament confinement in a limited region. The different concentrations of oxygen vacancies at each layer played a significant role in switching phenomena, as confirmed through X-ray photoelectron spectroscopy analysis. The analog weight update characteristics were found to strongly depend on the various conditions of voltage pulse parameters including its amplitude, width, and interval time. In particular, linear and symmetric weight updates for accurate learning and pattern recognition could be achieved by adopting incremental step pulse programming (ISPP) operation scheme which rendered a high-resolution dynamic range with linear and symmetry weight updates as a consequence of precisely controlled filament geometry. A two-layer perceptron neural network simulation with HfO2/HfO2-x synapses provided an 80% recognition accuracy for handwritten digits. The development of oxide/suboxide hafnium oxide memristive devices has the capacity to drive forward the development of efficient neuromorphic computing systems
Development of an offline OOH advertising recommendation system using negative sampling and deep interest network
No full textThe out-of-home (OOH) advertising market has been operated exclusively following the know-how of salespeople. Thus, it is difficult to make scientific decisions and systematically provide various options to advertisers. In this regard, this study develops an OOH advertising recommendation system by analyzing past OOH history data. The OOH advertising allocation problem has the characteristics that the training data are implicit feedback, and only one advertisement can be posted per offline billboard. This study proposes a recommendation system suitable for OOH history data using negative sampling and Deep Interest Network. The proposed recommendation system showed a higher performance than excisting models used for comparison purposes, and the findings of this study present implications for solving similar recommendation problems
Stop-loss adjusted labels for machine learning-based trading of risky assets
No full textSince the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk