Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method

Differentially Private Sharpness-Aware Training

Training deep learning models with differential privacy (DP) results in a degradation of performance. The training dynamics of models with DP show a significant difference from standard training, whereas understanding the geometric properties of private learning remains largely unexplored. In this paper, we investigate sharpness, a key factor in achieving better generalization, in private learning. We show that flat minima can help reduce the negative effects of per-example gradient clipping and the addition of Gaussian noise. We then verify the effectiveness of Sharpness-Aware Minimization (SAM) for seeking flat minima in private learning. However, we also discover that SAM is detrimental to the privacy budget and computational time due to its two-step optimization. Thus, we propose a new sharpness-aware training method that mitigates the privacy-optimization trade-off. Our experimental results demonstrate that the proposed method improves the performance of deep learning models with DP from both scratch and fine-tuning. Code is available at https://github.com/jinseongP/DPSAT.Comment: ICML 202

Conformational heterogeneity of molecules physisorbed on a gold surface at room temperature

A quantitative single-molecule tip-enhanced Raman spectroscopy (TERS) study at room temperature remained a challenge due to the rapid structural dynamics of molecules exposed to air. Here, we demonstrate the hyperspectral TERS imaging of single or a few brilliant cresyl blue (BCB) molecules at room temperature, along with quantitative spectral analyses. Robust chemical imaging is enabled by the freeze-frame approach using a thin Al2O3 capping layer, which suppresses spectral diffusions and inhibits chemical reactions and contamination in air. For the molecules resolved spatially in the TERS image, a clear Raman peak variation up to 7.5 cm(-1) is observed, which cannot be found in molecular ensembles. From density functional theory-based quantitative analyses of the varied TERS peaks, we reveal the conformational heterogeneity at the single-molecule level. This work provides a facile way to investigate the single-molecule properties in interacting media, expanding the scope of single-molecule vibrational spectroscopy studies. Tip-enhanced vibrational spectroscopy at room temperature is complicated by molecular conformational dynamics, photobleaching, contaminations, and chemical reactions in air. This study demonstrates that a sub-nm protective layer of Al2O3 provides robust conditions for probing single-molecule conformations

Quantitative local probing of polarization with application on HfO 2 ‐based thin films

Owing to their switchable spontaneous polarization, ferroelectric materials have been applied in various fields, such as information technologies, actuators, and sensors. In the last decade, as the characteristic sizes of both devices and materials have decreased significantly below the nanoscale, the development of appropriate characterization tools became essential. Recently, a technique based on conductive atomic force microscopy (AFM), called AFM‐positive‐up‐negative‐down (PUND), is employed for the direct measurement of ferroelectric polarization under the AFM tip. However, the main limitation of AFM‐PUND is the low frequency (i.e., on the order of a few hertz) that is used to initiate ferroelectric hysteresis. A significantly higher frequency is required to increase the signal‐to‐noise ratio and the measurement efficiency. In this study, a novel method based on high‐frequency AFM‐PUND using continuous waveform and simultaneous signal acquisition of the switching current is presented, in which polarization–voltage hysteresis loops are obtained on a high‐polarization BiFeO3 nanocapacitor at frequencies up to 100 kHz. The proposed method is comprehensively evaluated by measuring nanoscale polarization values of the emerging ferroelectric Hf0.5Zr0.5O2 under the AFM tip

Band gap opening by two-dimensional manifestation of Peierls instability in graphene

Using first-principles calculations of graphene having high-symmetry distortion or defects, we investigate band gap opening by chiral symmetry breaking, or intervalley mixing, in graphene and show an intuitive picture of understanding the gap opening in terms of local bonding and antibonding hybridizations. We identify that the gap opening by chiral symmetry breaking in honeycomb lattices is an ideal two-dimensional (2D) extension of the Peierls metal-insulator transition in 1D linear lattices. We show that the spontaneous Kekule distortion, a 2D version of the Peierls distortion, takes place in biaxially strained graphene, leading to structural failure. We also show that the gap opening in graphene antidots and armchair nanoribbons, which has been attributed usually to quantum confinement effects, can be understood with the chiral symmetry breaking

One metal is enough: A nickel complex reduces nitrate anions to nitrogen gas

A stepwise reduction sequence from nitrate to dinitrogen gas at a single nickel center was discovered. A PNP nickel scaffold (PNP - = N[2-P i Pr 2 -4-Me-C 6 H 3 ] 2 ) emerged as a universal platform for the deoxygenation of NO x substrates. In these reactions carbon monoxide acts as the oxygen acceptor and forms CO 2 to provide the necessary chemical driving force. Whereas the first two oxygens are removed from the Ni-nitrate and Ni-nitrite complexes with CO, the deoxygenation of NO requires a disproportionation reaction with another NO molecule to form NO 2 and N 2 O. The final deoxygenation of nitrous oxide is accomplished by the Ni-NO complex and generates N 2 and Ni-NO 2 in a relatively slow, but clean reaction. This sequence of reactions is the first example of the complete denitrification of nitrate at a single metal-site and suggests a new paradigm of connecting CO and NO x as an effective reaction pair for NO x removal. © The Royal Society of Chemistry 2019

Exploring Diverse Feature Extractions for Adversarial Audio Detection

Although deep learning models have exhibited excellent performance in various domains, recent studies have discovered that they are highly vulnerable to adversarial attacks. In the audio domain, malicious audio examples generated by adversarial attacks can cause significant performance degradation and system malfunctions, resulting in security and safety concerns. However, compared to recent developments in the audio domain, the properties of the adversarial audio examples and defenses against them still remain largely unexplored. In this study, to provide a deeper understanding of the adversarial robustness in the audio domain, we first investigate traditional and recent feature extractions in terms of adversarial attacks. We show that adversarial audio examples generated from different feature extractions exhibit different noise patterns, and thus can be distinguished by a simple classifier. Based on the observation, we extend existing adversarial detection methods by proposing a new detection method that detects adversarial audio examples using an ensemble of diverse feature extractions. By combining the frequency and self-supervised feature representations, the proposed method provides a high detection rate against both white-box and black-box adversarial attacks. Our empirical results demonstrate the effectiveness of the proposed method in speech command classification and speaker recognition

Polymer coated aptamer-gold nanoconstruct for scavenging ROS and capturing proinflammatory cytokine TNF-α

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