Optical conductivity of black phosphorus with a tunable electronic structure

Black phosphorus (BP) is a two-dimensional layered material composed of phosphorus atoms. Recently, it was demonstrated that external perturbations such as an electric field close the band gap in few-layer BP, and can even induce a band inversion, resulting in an insulator phase with a finite energy gap or a Dirac semimetal phase characterized by two separate Dirac nodes. At the transition between the two phases, a semi-Dirac state appears in which energy disperses linearly along one direction and quadratically along the other. In this work, we study the optical conductivity of few-layer BP using a lattice model and the corresponding continuum model, incorporating the effects of an external electric field and finite temperature. We find that the low-frequency optical conductivity scales a power law that differs depending on the phase, which can be utilized as an experimental signature of few-layer BP in different phases. We also systematically analyze the evolution of the material parameters as the electric field increases, and the consequence on the power-law behavior of the optical conductivity.Comment: 14 pages, 11 figure

Robust Multi-bit Natural Language Watermarking through Invariant Features

Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large language models. However, these contents are susceptible to illegal piracy and potential misuse without proper security measures. This calls for a secure watermarking system to guarantee copyright protection through leakage tracing or ownership identification. To effectively combat piracy and protect copyrights, a multi-bit watermarking framework should be able to embed adequate bits of information and extract the watermarks in a robust manner despite possible corruption. In this work, we explore ways to advance both payload and robustness by following a well-known proposition from image watermarking and identify features in natural language that are invariant to minor corruption. Through a systematic analysis of the possible sources of errors, we further propose a corruption-resistant infill model. Our full method improves upon the previous work on robustness by +16.8% point on average on four datasets, three corruption types, and two corruption ratios. Code available at https://github.com/bangawayoo/nlp-watermarking.Comment: ACL 2023 lon

Self-Distilled Self-Supervised Representation Learning

State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN models. Striving to maximize the mutual information of two views of an image, existing works apply a contrastive loss to the final representations. Motivated by self-distillation in the supervised regime, we further exploit this by allowing the intermediate representations to learn from the final layer via the contrastive loss. Through self-distillation, the intermediate layers are better suited for instance discrimination, making the performance of an early-exited sub-network not much degraded from that of the full network. This renders the pretext task easier also for the final layer, lead to better representations. Our method, Self-Distilled Self-Supervised Learning (SDSSL), outperforms competitive baselines (SimCLR, BYOL and MoCo v3) using ViT on various tasks and datasets. In the linear evaluation and k-NN protocol, SDSSL not only leads to superior performance in the final layers, but also in most of the lower layers. Furthermore, positive and negative alignments are used to explain how representations are formed more effectively. Code will be available.Comment: 15 page

Seismic Behavior of Batter Pile Foundation: Kinematic Response

We carried out centrifuge tests to clarify the seismic behavior of batter-pile foundations. A vertical-pile foundation and a batter-pile foundation without the presence of a superstructure were installed parallel to each other in a soil container filled with dry sand, and were excited simultaneously. Through a comparison of the acceleration and displacement response of the footing, as well as the axial and bending strain of the piles for the two pile foundations, the kinematic response of the seismic behavior of the batter-pile foundation was experimentally investigated

Effects of Frequency Offset on MC/CDMA System Performance

In this letter, the effects of frequency offset on a multicarrier code-division multiple-access system are theoretically analyzed and verified by computer simulations for downlink channel. Both equal gain combining and maximal ratio combining are considered in combining subcarrier signals in the analysis

Unifying Vision-Language Representation Space with Single-tower Transformer

Contrastive learning is a form of distance learning that aims to learn invariant features from two related representations. In this paper, we explore the bold hypothesis that an image and its caption can be simply regarded as two different views of the underlying mutual information, and train a model to learn a unified vision-language representation space that encodes both modalities at once in a modality-agnostic manner. We first identify difficulties in learning a generic one-tower model for vision-language pretraining (VLP), and propose OneR as a simple yet effective framework for our goal. We discover intriguing properties that distinguish OneR from the previous works that learn modality-specific representation spaces such as zero-shot object localization, text-guided visual reasoning and multi-modal retrieval, and present analyses to provide insights into this new form of multi-modal representation learning. Thorough evaluations demonstrate the potential of a unified modality-agnostic VLP framework.Comment: AAAI 2023, 11 page

ConcatPlexer: Additional Dim1 Batching for Faster ViTs

Transformers have demonstrated tremendous success not only in the natural language processing (NLP) domain but also the field of computer vision, igniting various creative approaches and applications. Yet, the superior performance and modeling flexibility of transformers came with a severe increase in computation costs, and hence several works have proposed methods to reduce this burden. Inspired by a cost-cutting method originally proposed for language models, Data Multiplexing (DataMUX), we propose a novel approach for efficient visual recognition that employs additional dim1 batching (i.e., concatenation) that greatly improves the throughput with little compromise in the accuracy. We first introduce a naive adaptation of DataMux for vision models, Image Multiplexer, and devise novel components to overcome its weaknesses, rendering our final model, ConcatPlexer, at the sweet spot between inference speed and accuracy. The ConcatPlexer was trained on ImageNet1K and CIFAR100 dataset and it achieved 23.5% less GFLOPs than ViT-B/16 with 69.5% and 83.4% validation accuracy, respectively

Change of Industrial Strategies and Government-Business Relationsip in India

India has emerged as the Asias new economic power. Many studies have applauded its significant economic development. Recently, however, Indian economy has also experienced sluggish growth and has faced pessimistic prediction. This paper will explore the reasons behind these divergent views by investigating Indias industrial strategies and the structural characteristics of economic governance through an examination of the relationship between Indian government and businesses by firm type and industrial sector

Transmit Power Allocation for a Downlink Two-User Interference Channel

We develop the optimal transmit power allocation scheme that maximizes the total throughput for a downlink twouser interference channel. The derived optimal scheme allocates the total power to one user in better channel state, as in the greedy scheme, when the degree of interference between users exceeds a certain threshold. When it is less than the threshold, on the contrary, the transmit power is divided into two users, as in the water-filling scheme. Numerical results are presented to verify the optimality of the derived scheme and to show throughput gains over the greedy and water-filling schemes.This work was supported in part by the National Research Laboratory (NRL) Program and Brain Korea 21 Project

Optical transitions of a single nodal ring in SrAs3_3: radially and axially resolved characterization

We perform polarized optical reflection measurements on a single nodal-ring semimetal $\rm{SrAs_3}$. For the radial and axial directions of the ring, the optical conductivity $\sigma_1(\omega)$ exhibits a flat absorption $\sigma^{\mathrm{flat}}$ over a certain frequency range. In addition, a prominent optical peak appears at 2$\Delta_{\mathrm{SOC}}$ = 30 meV. For comparison, we theoretically calculate $\sigma_1(\omega)$ using an effective model Hamiltonian and first-principles calculations, which successfully reproduces the data for both directions. The $\sigma^{\mathrm{flat}}$ establishes that the universal power-law of optical conductivity holds robustly in the nodal ring. Furthermore, key quantities of the nodal ring such as the band overlap energy, average ring radius, ring ellipticity, and the SOC-gap are determined from this comparative study. As temperature increases, $\sigma_1(\omega)$ shows a substantial change, suggesting that a $T$-driven evolution occurs in the nodal ring.Comment: 6 pages, 4 figures + supplemental material (18 pages, 7 figures