Model-Agnostic Meta-Learning for Natural Language Understanding Tasks in Finance

Natural language understanding(NLU) is challenging for finance due to the lack of annotated data and the specialized language in that domain. As a result, researchers have proposed to use pre-trained language model and multi-task learning to learn robust representations. However, aggressive fine-tuning often causes over-fitting and multi-task learning may favor tasks with significantly larger amounts data, etc. To address these problems, in this paper, we investigate model-agnostic meta-learning algorithm(MAML) in low-resource financial NLU tasks. Our contribution includes: 1. we explore the performance of MAML method with multiple types of tasks: GLUE datasets, SNLI, Sci-Tail and Financial PhraseBank; 2. we study the performance of MAML method with multiple single-type tasks: a real scenario stock price prediction problem with twitter text data. Our models achieve the state-of-the-art performance according to the experimental results, which demonstrate that our method can adapt fast and well to low-resource situations.Comment: 13 pages, 6 figures, 8 table

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

Learn to Model Blurry Motion via Directional Similarity and Filtering

It is difficult to recover the motion field from a real-world footage given a mixture of camera shake and other photometric effects. In this paper we propose a hybrid framework by interleaving a Convolutional Neural Network (CNN) and a traditional optical flow energy. We first conduct a CNN architecture using a novel learnable directional filtering layer. Such layer encodes the angle and distance similarity matrix between blur and camera motion, which is able to enhance the blur features of the camera-shake footages. The proposed CNNs are then integrated into an iterative optical flow framework, which enable the capability of modeling and solving both the blind deconvolution and the optical flow estimation problems simultaneously. Our framework is trained end-to-end on a synthetic dataset and yields competitive precision and performance against the state-of-the-art approaches.</p

Viewing heterospecific facial expressions: an eye-tracking study of human and monkey viewers

Common facial expressions of emotion have distinctive patterns of facial muscle movements that are culturally similar among humans, and perceiving these expressions is associated with stereotypical gaze allocation at local facial regions that are characteristic for each expression, such as eyes in angry faces. It is, however, unclear to what extent this ‘universality’ view can be extended to process heterospecific facial expressions, and how ‘social learning’ process contributes to heterospecific expression perception. In this eye-tracking study, we examined face-viewing gaze allocation of human (including dog owners and non-dog owners) and monkey observers while exploring expressive human, chimpanzee, monkey and dog faces (positive, neutral and negative expressions in human and dog faces; neutral and negative expressions in chimpanzee and monkey faces). Human observers showed species- and experience-dependent expression categorization accuracy. Furthermore, both human and monkey observers demonstrated different face-viewing gaze distributions which were also species dependent. Specifically, humans predominately attended at human eyes but animal mouth when judging facial expressions. Monkeys’ gaze distributions in exploring human and monkey faces were qualitatively different from exploring chimpanzee and dog faces. Interestingly, the gaze behaviour of both human and monkey observers were further affected by their prior experience of the viewed species. It seems that facial expression processing is species dependent, and social learning may play a significant role in discriminating even rudimentary types of heterospecific expressions

Fear-related signals in the primary visual cortex

Neuronal responses in the primary visual cortex (V1) are driven by simple stimuli, but these stimulus-evoked responses can be markedly modulated by non-sensory factors such as attention and reward [1] and shaped by perceptual training [2]. In real-life situations, neutral visual stimuli can become emotionally tagged by experience, resulting in altered perceptual abilities to detect and discriminate these stimuli [3-5]. Human imaging [4] and electroencephalography (EEG) studies [6-9] have shown that visual fear learning (the acquisition of aversive emotion associated with a visual stimulus) affects the activities in visual cortical areas as early as V1. However, it remains elusive whether the fear-related activities seen in the early visual cortex have to do with feedback influences from other cortical areas; it is also unclear whether and how the response properties of V1 cells are modified during the fear learning. In the current study, we addressed these issues by recording from V1 of awake monkeys implanted with an array of microelectrodes. We found that responses of V1 neurons were rapidly modified when a given orientation of grating stimulus was repeatedly associated with an aversive stimulus. The output visual signals from V1 cells conveyed, from their response outset, fear-related signals that were specific to the fear-associated grating orientation and visual-field location. The specific fear signals were independent of neurons’ orientation preferences and were present even though the fear-associated stimuli were rendered invisible. Our findings suggest a bottom-up mechanism that allows for proactive labeling of visual inputs that are predictive of imminent danger

Few-photon single ionization of cold rubidium in the over-the-barrier regime

Photoionization of the rubidium (Rb) atoms cooled in a magneto-optical trap, characterized by the coexistence of the ground 5$S_{1/2}$ and the excited 5$P_{3/2}$ states, is investigated experimentally and theoretically with the 400 nm femtosecond laser pulses at intensities of $I=3\times10^9$ W/cm$^2$ - $4.5\times10^{12}$ W/cm$^2$. Recoil-ion momentum distribution (RIMD) of Rb$^+$ exhibits rich ring-like structures and their energies correspond to one-photon ionization of the 5$P_{3/2}$ state, two-photon and three-photon ionizations of the 5$S_{1/2}$ state, respectively. With the increasing of $I$, we find that experimental signals near zero-momentum (NZM) in RIMDs resulted from the 5$P_{3/2}$ state enhance dramatically and its peaked Rb$^+$ momenta dwindle obviously while that from the 5$S_{1/2}$ state is maintained. Meanwhile, the ion-yield ratio of the 5$S_{1/2}$ over the 5$P_{3/2}$ states varies from $I$ to $I^{1.5}$ as $I$ increases. These features indicate a transition from perturbative ionization to strong-perturbative ionization for the 5$P_{3/2}$ state. Numerical simulations by solving the time-dependent Schr\"odinger equation (TDSE) can qualitatively explain the measurements of RIMD, photoion angular distributions, as well as ion-yield ratio. However, some discrepancies still exist, especially for the NZM dip, which could stem from the electron-electron correlation that is neglected in the present TDSE simulations since we have adopted the single-active-electron approximation

Preliminary Evidence of Sex Differences in Cortical Thickness Following Acute Mild Traumatic Brain Injury

The main objective of this study was to evaluate sex differences in cortical thickness after acute mild traumatic brain injury (mTBI) and its associations with clinical outcomes. Thirty-two patients with mTBI at acute phase (2.4 ± 1.3 days post-injury) and 30 healthy controls were enrolled. All the participants underwent comprehensive neurocognitive assessments and MRI to assess cortical thickness. Significant sex differences were determined by using variance analysis of factorial design. Relations between the cortical thickness and clinical assessments were measured with the Spearman Correlation. Results revealed that patients with mTBI had significantly reduced cortical thickness in the left entorhinal cortex while increased cortical thickness in the left precuneus cortex and right lateral occipital cortex, compared with healthy controls. The interaction effect of the group × sex on cortical thickness was significant. Female patients had significant thicker cortical thickness in the left caudal anterior cingulate cortex (ACC) than male patients and had higher scores on Posttraumatic stress disorder Checklist—Civilian Version (PCL-C). Spearman correlational analysis showed a significantly positive correlations between the cortical thickness of the left caudal ACC and PCL-C ratings in female patients. Sex differences in cortical thickness support its potential as a neuroimaging phenotype for investigating the differences in clinical profiles of mild TBI between women and men