Two-dimensional (2D) materials have many promising applications, but their scalable production remains challenging. Herein, we develop a glue-assisted grinding exfoliation (GAGE) method in which the adhesive polymer acts as a glue to massively produc
e 2D materials with large lateral sizes, high quality, and high yield. Density functional theory simulation shows that the exfoliation mechanism involves the competition between the binding energy of selected polymers and the 2D materials which is larger than the exfoliation energy of the layered materials. Taking h-BN as an example, the GAGE produces 2D h-BN with an average lateral size of 2.18 {mu}m and thickness of 3.91 nm. The method is also extended to produce various other 2D materials, including graphene, MoS2, Bi2O2Se, vermiculite, and montmorillonite. Two representative applications of thus-produced 2D materials have been demonstrated, including h-BN/polymer composites for insulating thermal conduction and MoS2 electrocatalysts for large-current-density hydrogen evolution, indicating the great potential of massively produced 2D materials.
The recent development of the terahertz waveguide makes it an excellent platform for integrating many intriguing functionalities, which offers tremendous potential to build compact and robust terahertz systems. In the context of next-generation high-
speed communication links at the terahertz band, engineering the dispersion and birefringence of terahertz waves is essential. Here, we experimentally demonstrate subwavelength birefringent waveguide gratings based on the low-loss cyclic olefin copolymer exploiting micro-machining fabrication techniques. Asymmetric cross-section and periodic-structural modulation along propagation direction are introduced to achieve birefringent THz grating for filtering and dispersion compensation. Because of strong index modulation in the subwavelength fiber, a high negative group velocity dispersion of -188 (-88) ps/mm/THz is achieved at 0.15 THz for x-polarization (y-polarization), i.e., 7.5 times increase compared to the state-of-the-art reported to date. Such high negative dispersion is realized in a 43 mm grating length, which is less than half of the length reported until now (e.g., 100 mm). Further, the subwavelength fiber grating filters two orthogonal polarization states and exhibits transmission dips with 8.5-dB and 7.5-dB extinction ratios for x and y polarization, respectively. Our experiment demonstrates the feasibility of using polymer-based terahertz gratings as a dispersion compensator in terahertz communications and steering polarized terahertz radiations for polarization-sensitive THz systems.
In recent years, many recommender systems using network embedding (NE) such as graph neural networks (GNNs) have been extensively studied in the sense of improving recommendation accuracy. However, such attempts have focused mostly on utilizing only
the information of positive user-item interactions with high ratings. Thus, there is a challenge on how to make use of low rating scores for representing users preferences since low ratings can be still informative in designing NE-based recommender systems. In this study, we present SiReN, a new sign-aware recommender system based on GNN models. Specifically, SiReN has three key components: 1) constructing a signed bipartite graph for more precisely representing users preferences, which is split into two edge-disjoint graphs with positive and negative edges each, 2) generating two embeddings for the partitioned graphs with positive and negative edges via a GNN model and a multi-layer perceptron (MLP), respectively, and then using an attention model to obtain the final embeddings, and 3) establishing a sign-aware Bayesian personalized ranking (BPR) loss function in the process of optimization. Through comprehensive experiments, we empirically demonstrate that SiReN consistently outperforms state-of-the-art NE-aided recommendation methods.
Approximate Policy Iteration (API) algorithms alternate between (approximate) policy evaluation and (approximate) greedification. Many different approaches have been explored for approximate policy evaluation, but less is understood about approximate
greedification and what choices guarantee policy improvement. In this work, we investigate approximate greedification when reducing the KL divergence between the parameterized policy and the Boltzmann distribution over action values. In particular, we investigate the difference between the forward and reverse KL divergences, with varying degrees of entropy regularization. We show that the reverse KL has stronger policy improvement guarantees, but that reducing the forward KL can result in a worse policy. We also demonstrate, however, that a large enough reduction of the forward KL can induce improvement under additional assumptions. Empirically, we show on simple continuous-action environments that the forward KL can induce more exploration, but at the cost of a more suboptimal policy. No significant differences were observed in the discrete-action setting or on a suite of benchmark problems. Throughout, we highlight that many policy gradient methods can be seen as an instance of API, with either the forward or reverse KL for the policy update, and discuss next steps for understanding and improving our policy optimization algorithms.
Despite inextricable ties between race and language, little work has considered race in NLP research and development. In this work, we survey 79 papers from the ACL anthology that mention race. These papers reveal various types of race-related bias i
n all stages of NLP model development, highlighting the need for proactive consideration of how NLP systems can uphold racial hierarchies. However, persistent gaps in research on race and NLP remain: race has been siloed as a niche topic and remains ignored in many NLP tasks; most work operationalizes race as a fixed single-dimensional variable with a ground-truth label, which risks reinforcing differences produced by historical racism; and the voices of historically marginalized people are nearly absent in NLP literature. By identifying where and how NLP literature has and has not considered race, especially in comparison to related fields, our work calls for inclusion and racial justice in NLP research practices.
We present a new approach to encourage neural machine translation to satisfy lexical constraints. Our method acts at the training step and thereby avoiding the introduction of any extra computational overhead at inference step. The proposed method co
mbines three main ingredients. The first one consists in augmenting the training data to specify the constraints. Intuitively, this encourages the model to learn a copy behavior when it encounters constraint terms. Compared to previous work, we use a simplified augmentation strategy without source factors. The second ingredient is constraint token masking, which makes it even easier for the model to learn the copy behavior and generalize better. The third one, is a modification of the standard cross entropy loss to bias the model towards assigning high probabilities to constraint words. Empirical results show that our method improves upon related baselines in terms of both BLEU score and the percentage of generated constraint terms.
Educational technologies, and the systems of schooling in which they are deployed, enact particular ideologies about what is important to know and how learners should learn. As artificial intelligence technologies -- in education and beyond -- have l
ed to inequitable outcomes for marginalized communities, various approaches have been developed to evaluate and mitigate AI systems disparate impact. However, we argue in this paper that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We draw on a lens of structural injustice informed by critical theory and Black feminist scholarship to critically interrogate several widely-studied and widely-adopted categories of educational AI systems and demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models performance. We close with alternative visions for a more equitable future for educational AI research.
In this paper, we consider the following Kirchhoff type equation $$ -left(a+ bint_{R^3}| abla u|^2right)triangle {u}+V(x)u=f(u),,,xinR^3, $$ where $a,b>0$ and $fin C(R,R)$, and the potential $Vin C^1(R^3,R)$ is positive, bounded and satisfies suitabl
e decay assumptions. By using a new perturbation approach together with a new version of global compactness lemma of Kirchhoff type, we prove the existence and multiplicity of bound state solutions for the above problem with a general nonlinearity. We especially point out that neither the corresponding Ambrosetti-Rabinowitz condition nor any monotonicity assumption is required for $f$. Moreover, the potential $V$ may not be radially symmetry or coercive. As a prototype, the nonlinear term involves the power-type nonlinearity $f(u) = |u|^{p-2}u$ for $pin (2, 6)$. In particular, our results generalize and improve the results by Li and Ye (J.Differential Equations, 257(2014): 566-600), in the sense that the case $pin(2,3]$ is left open there.
Compact and robust waveguide chips are crucial for new integrated terahertz applications, such as high-speed interconnections between processors and broadband short-range wireless communications. Progress on topological photonic crystals shows potent
ial to improve integrated terahertz systems that suffer from high losses around sharp bends. Robust terahertz topological transport through sharp bends on a silicon chip has been recently reported over a relatively narrow bandwidth. Here, we report the experimental demonstration of topological terahertz planar air-channel metallic waveguides which can be integrated into an on-chip interconnect. Our platform can be fabricated by a simple, cost-effective technique combining 3D-printing and gold-sputtering. The relative size of the measured topological bandgap is ~12.5%, which entails significant improvement over all-silicon terahertz topological waveguides (~7.8%). We further demonstrate robust THz propagation around defects and delay lines. Our work provides a promising path towards compact integrated terahertz devices as a next frontier for terahertz wireless communications.
The finding of bulk quantum oscillations in the bulk Kondo insulator SmB$_6$, which has been proposed to be a correlated topological insulator, proved a considerable surprise. The subsequent measurement of bulk quantum oscillations in other correlate
d insulators including YbB$_{12}$ have lent support to our discovery of a class of unconventional insulators that are host to bulk quantum oscillations, of which SmB$_6$ was the first example. Here we perform a series of experiments to examine evidence for the intrinsic character of bulk quantum oscillations in floating zone-grown single crystals of SmB$_6$ that have been the subject of our quantum oscillation studies thus far. We present results of experiments including chemical composition analysis, magnetisation, thermal conductivity, electrical transport, and heat capacity on floating zone-grown single crystals of SmB$_6$, and a series of quantum oscillation experiments as a function of magnetic field, temperature, and magnetic field-orientation on single crystals of floating-zone grown SmB$_6$, LaB$_6$, and elemental Aluminium. Results of these experimental studies establish the intrinsic origin of quantum oscillations from the bulk of pristine floating zone-grown single crystals of SmB$_6$. The origin of the underlying bulk Fermi surface that bears close similarity with the unhybridised Fermi surface in metallic hexaborides despite the bulk insulating character of SmB$_6$ is thus at the heart of a theoretical mystery.
