Grounding Design to Prevent Electrostatic Accumulation in Foldable Displays

In traditional polymer organic light emitting diode (P-OLED) displays, electrostatic charge buildup can occur near the edge of the display, leading to abnormalities such as green flashes, vertical crosstalk, or a greenish display. To mitigate this problem, a discharge path is established to release electrostatic charges by using silver dotting on the edge of the display that connects to a conductive black matrix and provides a grounding path. However, for foldable displays, the silver dotting can crack due to the movement and sliding of different layers as the device is folded and unfolded, causing disconnection from ground. This disclosure describes a foldable display that implements an electrostatic discharge path as a grounding mechanism to avoid electrostatic charge accumulation at the edge of the display. The grounding design includes silver printing on the trim area of the device that is linked to a conductive pressure sensitive adhesive (PSA) to release the electrostatic charge via the device enclosure

Current correlators for general gauge mediation

In the gauge mediation mechanism, the effects of the hidden sector are characterized by a set of correlation functions of the global symmetry current of the hidden sector. We present methods to compute these correlators in cases with strongly coupled hidden sectors. Several examples are presented to demonstrate the technique explicitly

Defending Against Transfer Attacks From Public Models

Adversarial attacks have been a looming and unaddressed threat in the industry. However, through a decade-long history of the robustness evaluation literature, we have learned that mounting a strong or optimal attack is challenging. It requires both machine learning and domain expertise. In other words, the white-box threat model, religiously assumed by a large majority of the past literature, is unrealistic. In this paper, we propose a new practical threat model where the adversary relies on transfer attacks through publicly available surrogate models. We argue that this setting will become the most prevalent for security-sensitive applications in the future. We evaluate the transfer attacks in this setting and propose a specialized defense method based on a game-theoretic perspective. The defenses are evaluated under 24 public models and 11 attack algorithms across three datasets (CIFAR-10, CIFAR-100, and ImageNet). Under this threat model, our defense, PubDef, outperforms the state-of-the-art white-box adversarial training by a large margin with almost no loss in the normal accuracy. For instance, on ImageNet, our defense achieves 62% accuracy under the strongest transfer attack vs only 36% of the best adversarially trained model. Its accuracy when not under attack is only 2% lower than that of an undefended model (78% vs 80%). We release our code at https://github.com/wagner-group/pubdef.Comment: Under submission. Code available at https://github.com/wagner-group/pubde

Multi-Modal Gaze Following in Conversational Scenarios

Gaze following estimates gaze targets of in-scene person by understanding human behavior and scene information. Existing methods usually analyze scene images for gaze following. However, compared with visual images, audio also provides crucial cues for determining human behavior.This suggests that we can further improve gaze following considering audio cues. In this paper, we explore gaze following tasks in conversational scenarios. We propose a novel multi-modal gaze following framework based on our observation ``audiences tend to focus on the speaker''. We first leverage the correlation between audio and lips, and classify speakers and listeners in a scene. We then use the identity information to enhance scene images and propose a gaze candidate estimation network. The network estimates gaze candidates from enhanced scene images and we use MLP to match subjects with candidates as classification tasks. Existing gaze following datasets focus on visual images while ignore audios.To evaluate our method, we collect a conversational dataset, VideoGazeSpeech (VGS), which is the first gaze following dataset including images and audio. Our method significantly outperforms existing methods in VGS datasets. The visualization result also prove the advantage of audio cues in gaze following tasks. Our work will inspire more researches in multi-modal gaze following estimation

The effect of fermented buckwheat on producing L-carnitine enriched oyster mushroom

L-carnitine is biological compound which serves intake of long chain fatty acids into mitochondria. In market, L-carnitine is considered as nutritious supplements for weight-loss. L-carnitine is synthesized in human organ, but most of L-carnitine which human intakes are originated from meat based foods. Oyster mushroom (Pleurotus ostreatus), the second popular edible mushroom in the world, is the main source of L-carnitine after meat and pork. Recently, there were many efforts to study designer foods of which functional ingredients were increased. However most of studies were focused on dairy products. In this study, the fermented buckwheat by Rhizopus oligosporus that contained high L-carnitine contents were used to cultivate oyster mushroom. L-carnitine contents in oyster mushroom were quantified by LC-ESI-MS. Mushroom grown on buckwheat medium had 3.17 to 23.88% higher L-carnitine concentration than normal medium. The mushroom size was increased when 20% (w/w) of buckwheat was added to basal medium. The lightness of mushroom pileus (L*) significantly increased among all the treatments. These results demonstrate that buckwheat and fermented buckwheat is novel substrates to produce L-carnitine enriched functional mushroom.OAIID:RECH_ACHV_DSTSH_NO:A201702463RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A079459CITE_RATE:FILENAME:태경.pdfDEPT_NM:국제농업기술학과EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/34dfad8a-5bc9-41cd-8160-c7846937fa22/linkCONFIRM:

Impaired formation of high-order gephyrin oligomers underlies gephyrin dysfunction-associated pathologies

Gephyrin is critical for the structure, function, and plasticity of inhibitory synapses. Gephyrin mutations have been linked to various neurological disorders; however, systematic analyses of the functional consequences of these mutations are lacking. Here, we performed molecular dynamics simulations of gephyrin to predict how six reported point mutations might change the structural stability and/or function of gephyrin. Additional in silico analyses revealed that the A91T and G375D mutations reduce the binding free energy of gephyrin oligomer formation. Gephyrin A91T and G375D displayed altered clustering patterns in COS-7 cells and nullified the inhibitory synapse-promoting effect of gephyrin in cultured neurons. However, only the G375D mutation reduced gephyrin interaction with GABAA receptors and neuroligin-2 in mouse brain; it also failed to normalize deficits in GABAergic synapse maintenance and neuronal hyperactivity observed in hippocampal dentate gyrus-specific gephyrin-deficient mice. Our results provide insights into biochemical, cell-biological, and network-activity effects of the pathogenic G375D mutation. © 2021 The Author(s)1

Single-domain stripe order in a high-temperature superconductor

The coupling of spin, charge and lattice degrees of freedom results in the emergence of novel states of matter across many classes of strongly correlated electron materials. A model example is unconventional superconductivity, which is widely believed to arise from the coupling of electrons via spin excitations. In cuprate high-temperature superconductors, the interplay of charge and spin degrees of freedom is also reflected in a zoo of charge and spin-density wave orders that are intertwined with superconductivity. A key question is whether the different types of density waves merely coexist or are indeed directly coupled. Here we profit from a neutron scattering technique with superior beam-focusing that allows us to probe the subtle spin-density wave order in the prototypical high-temperature superconductor La$${}_{1.88}$$Sr$${}_{0.12}$$CuO$${}_{4}$$ under applied uniaxial pressure to demonstrate that the two density waves respond to the external tuning parameter in the same manner. Our result shows that suitable models for high-temperature superconductivity must equally account for charge and spin degrees of freedom via uniaxial charge-spin stripe fluctuations

Weak-signal extraction enabled by deep-neural-network denoising of diffraction data

Removal or cancellation of noise has wide-spread applications for imaging and acoustics. In every-day-life applications, denoising may even include generative aspects which are unfaithful to the ground truth. For scientific applications, however, denoising must reproduce the ground truth accurately. Here, we show how data can be denoised via a deep convolutional neural network such that weak signals appear with quantitative accuracy. In particular, we study X-ray diffraction on crystalline materials. We demonstrate that weak signals stemming from charge ordering, insignificant in the noisy data, become visible and accurate in the denoised data. This success is enabled by supervised training of a deep neural network with pairs of measured low- and high-noise data. This way, the neural network learns about the statistical properties of the noise. We demonstrate that using artificial noise (such as Poisson and Gaussian) does not yield such quantitatively accurate results. Our approach thus illustrates a practical strategy for noise filtering that can be applied to challenging acquisition problems.Comment: 8 pages, 4 figure