Existing system dealing with online complaint provides a final decision without explanations. We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various f
unctions, we propose to identify the role of each clause and classify them into different types of fraud element. We construct a large labeled dataset originated from a real finance service platform. We build an element identification model on top of BERT and propose additional two modules to utilize the context of complaint text for better element label classification, namely, global context encoder and label refiner. Experimental results show the effectiveness of our model.
Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, w
e take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.
Compounds with kagome lattice usually host many exotic quantum states, including the quantum spin liquid, non-trivial topological Dirac bands and a strongly renormalized flat band, etc. Recently an interesting vanadium based kagome family $A$V$_{3}$S
b$_{5}$ ($A$ = K, Rb, or Cs) was discovered, and these materials exhibit multiple interesting properties, including unconventional saddle-point driven charge density wave (CDW) state, superconductivity, etc. Furthermore, some experiments show anomalous Hall effect which inspires that there might be some chiral flux current states. Here we report scanning tunneling measurements by using spin polarized tips. Although we have observed clearly the $2times2$ and $1times4$ CDW orders, the well-designed experiments with refined spin polarized tips do not reveal any trace of the chiral flux current phase in CsV$_3$Sb$_5$. Thus it remains debatable whether this state really exists in CsV$_3$Sb$_5$ and we may need additional scenario to explain the anomalous Hall effect.
In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them the antiferromagnetic (AF) order is the most prominent one. In the region wher
e superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF fluctuations are present up to a much higher doping and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin polarized scanning tunneling microscope, for the first time, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi2Sr2CaCu2O8+{delta} (Bi2212). Remarkably the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.
Existing research for image captioning usually represents an image using a scene graph with low-level facts (objects and relations) and fails to capture the high-level semantics. In this paper, we propose a Theme Concepts extended Image Captioning (T
CIC) framework that incorporates theme concepts to represent high-level cross-modality semantics. In practice, we model theme concepts as memory vectors and propose Transformer with Theme Nodes (TTN) to incorporate those vectors for image captioning. Considering that theme concepts can be learned from both images and captions, we propose two settings for their representations learning based on TTN. On the vision side, TTN is configured to take both scene graph based features and theme concepts as input for visual representation learning. On the language side, TTN is configured to take both captions and theme concepts as input for text representation re-construction. Both settings aim to generate target captions with the same transformer-based decoder. During the training, we further align representations of theme concepts learned from images and corresponding captions to enforce the cross-modality learning. Experimental results on MS COCO show the effectiveness of our approach compared to some state-of-the-art models.
Logical reasoning of text requires understanding critical logical information in the text and performing inference over them. Large-scale pre-trained models for logical reasoning mainly focus on word-level semantics of text while struggling to captur
e symbolic logic. In this paper, we propose to understand logical symbols and expressions in the text to arrive at the answer. Based on such logical information, we not only put forward a context extension framework but also propose a data augmentation algorithm. The former extends the context to cover implicit logical expressions following logical equivalence laws. The latter augments literally similar but logically different instances to better capture logical information, especially logical negative and conditional relationships. We conduct experiments on ReClor dataset. The results show that our method achieves the state-of-the-art performance, and both logic-driven context extension framework and data augmentation algorithm can help improve the accuracy. And our multi-model ensemble system is the first to surpass human performance on both EASY set and HARD set of ReClor.
The pairing mechanism in cuprates remains as one of the most challenging issues in the field of condensed matter physics. The unique 3d9 electron orbital of the Cu2+ ionic states in cuprates is supposed to be the major player for the occurrence of su
perconductivity. Recently, superconductivity at about 9-15 K was discovered in infinite layer thin films of nickelate Nd1-xSrxNiO2 (x=0.1-0.2) which is believed to have the similar 3d9 orbital electrons. The key issue concerned here is about the superconducting gap function. Here we report the first set data of single particle tunneling measurements on the superconducting nickelate thin films. We find predominantly two types of tunneling spectra, one shows a V-shape feature which can be fitted very well by a d-wave gap function with gap maximum of about 3.9 meV, another one exhibits a full gap of about 2.35 meV. Some spectra demonstrate mixed contributions of these two components. Our results suggest that the newly found Ni-based superconductors play as close analogs to cuprates, and thus demonstrate the commonality of unconventional superconductivity.
Recent experiments on layered {alpha}-In2Se3 have confirmed its room-temperature ferroelectricity under ambient condition. This observation renders {alpha}-In2Se3 an excellent platform for developing two-dimensional (2D) layered-material based electr
onics with nonvolatile functionality. In this letter, we demonstrate non-volatile memory effect in a hybrid 2D ferroelectric field effect transistor (FeFET) made of ultrathin {alpha}-In2Se3 and graphene. The resistance of graphene channel in the FeFET is tunable and retentive due to the electrostatic doping, which stems from the electric polarization of the ferroelectric {alpha}-In2Se3. The electronic logic bit can be represented and stored with different orientations of electric dipoles in the top-gate ferroelectric. The 2D FeFET can be randomly re-written over more than $10^5$ cycles without losing the non-volatility. Our approach demonstrates a protype of re-writable non-volatile memory with ferroelectricity in van de Waals 2D materials.
The superconducting state is achieved by the condensation of Cooper pairs and is protected by the superconducting gap. The pairing interaction between the two electrons of a Cooper pair determines the superconducting gap function. Thus, it is very pi
votal to detect the gap structure for understanding the mechanism of superconductivity. In cuprate superconductors, it has been well established that the superconducting gap may have a d-wave function {Delta} = {Delta}_0cos2{theta}. This gap function has an alternative sign change by every pi/2 in the momentum space when the in-plane azimuthal angle theta is scanned. It is very hard to visualize this sign change. Early experiments for recommending or proving this d-wave gap function were accomplished by the specially designed phase sensitive measurements based on the Josephson effect. Here we report the measurements of scanning tunneling spectroscopy in one of the model cuprate system Bi2Sr2CaCu2O8+{delta} and conduct the analysis of phase-referenced quasiparticle interference (QPI). Due to the unique quasiparticle excitations in the superconducting state of cuprate, we have seen the seven basic scattering vectors that connect each pair of the terminals of the banana-shaped contour of constant quasiparticle energy (CCE). The phase-referenced QPI clearly visualizes the sign change of the d-wave gap. Our results illustrate a very effective way for determining the sign change of unconventional superconductors.
Nanoscaled room-temperature ferroelectricity is ideal for developing advanced non-volatile high-density memories. However, reaching the thin film limit in conventional ferroelectrics is a long-standing challenge due to the possible critical thickness
effect. Van der Waals materials, thanks to their stable layered structure, saturate interfacial chemistry and weak interlayer couplings, are promising for exploring ultra-thin two-dimensional (2D) ferroelectrics and device applications. Here, we demonstrate a switchable room-temperature ferroelectric diode built upon a 2D ferroelectric {alpha}-In2Se3 layer as thin as 5 nm in the form of graphene/{alpha}-In2Se3 heterojunction. The intrinsic out-of-plane ferroelectricity of the {alpha}-In2Se3 thin layers is evidenced by the observation of reversible spontaneous electric polarization with a relative low coercive electric field of ~$2 X 10^5 V/cm$ and a typical ferroelectric domain size of around tens ${mu}m^2$. Owing to the out-of-plane ferroelectricity of the {alpha}-In2Se3 layer, the Schottky barrier at the graphene/{alpha}-In2Se3 interface can be effectively tuned by switching the electric polarization with an applied voltage, leading to a pronounced switchable double diode effect with an on/off ratio of ~$10^4$. Our results offer a new way for developing novel nanoelectronic devices based on 2D ferroelectrics.
