Evaluating sentiment in financial news articles: Working paper series--11-10

We investigate the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% vs 50.4% without sentiment) and our simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively)

Expressions of CXCL12/CXCR4 in Oral Premalignant and Malignant Lesions

Objective. The chemokine receptor CXCR4 and its ligand CXCL12 have been suggested to play important roles in the initiation or progression of cancers. The goal of the present study was to investigate alterations of CXCL12/CXCR4 in oral premalignant lesions and oral squamous cell carcinoma (OSCC). Methods. In 13 normal oral epithelia, 24 dysplastic oral leukoplakia (OLK), and 40 OSCC specimens, expressions of CXCL12 and CXCR4 were evaluated by immunohistochemistry. Results. CXCR4 was expressed in 37.5% of OLK and 60% of OSCC. CXCL12 was detected in 50% of OLK and 62.5% of OSCC. In OLK, CXCR4 positive ratio showed no significant difference from normal epithelia, but the CXCL12 positive ratio was significantly higher. Significant relationship between CXCL12 and CXCR4 was found both in OLK and OSCC. Conclusion. Our results indicated that CXCL12/CXCR4 axis may play roles from early steps of oral malignant transformation and contribute to the progress of oral carcinogenesis

Measuring Quantum Entanglement from Local Information by Machine Learning

Entanglement is a key property in the development of quantum technologies and in the study of quantum many-body simulations. However, entanglement measurement typically requires quantum full-state tomography (FST). Here we present a neural network-assisted protocol for measuring entanglement in equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST, it can learn comprehensive entanglement quantities from single-qubit or two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed (PT) moments, and coherence. It is also exciting that our neural network is able to learn the future entanglement dynamics using only single-qubit traces from the previous time. In addition, we perform experiments using a nuclear spin quantum processor and train an adoptive neural network to study entanglement in the ground and dynamical states of a one-dimensional spin chain. Quantum phase transitions (QPT) are revealed by measuring static entanglement in ground states, and the entanglement dynamics beyond measurement time is accurately estimated in dynamical states. These precise results validate our neural network. Our work will have a wide range of applications in quantum many-body systems, from quantum phase transitions to intriguing non-equilibrium phenomena such as quantum thermalization.Comment: 5 pages, 4 figures. All comments are welcom

All-trans retinoic acid restores gap junctional intercellular communication between oral cancer cells with upregulation of Cx32 and Cx43 expressions in vitro

Objective: All-trans retinoic acid (ATRA) has been demonstrated to inhibit tumor growth by restoration of gap junctional intercellular communication (GJIC) via upregulation of connexin (Cx) expression in some solid tumors. However, the relationship between ATRA and GJIC remains unclear in oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the effect of ATRA on the GJIC function of OSCC. Study design: We measured the effects of ATRA on the viability and cell cycle distribution of SCC9 and Tca8113 OSCC cells. The GJIC function was observed using the scrape-loading dye transfer technique, and the mRNA and protein levels of Cx32 and Cx43 were detected by qRT-PCR, Western blot, and immunofluorescence assays. Results: ATRA inhibited the growth of OSCC cells in a dose- and time-dependent manner (P <0.05) and caused cell cycle arrest. ATRA-treated cells showed a 2.69-fold and 2.06-fold enhancement of GJIC in SCC9 and Tca8113 cells, respectively (P <0.05). Moreover, ATRA induced upregulation of Cx32 and Cx43 at both the mRNA and protein levels in OSCC cells. Conclusion: Our results indicated that restoration of GJIC via enhanced Cx32 and Cx43 expression might serve as a novel mechanism for the anti-tumor effect of ATRA in OSCC

Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In this paper, we propose a novel method based on deep learning for timing analysis of modularized nuclear detectors without explicit needs of labelling event data. By taking advantage of the inner time correlation of individual detectors, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets. In the toy experiment, the neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several neural network models (FC, CNN and LSTM) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely.Comment: 25 pages, 10 figure

Influence of the Arctic Oscillation on the Vertical Distribution of Wintertime Ozone in the Stratosphere and Upper Troposphere over Northern Hemisphere

The influence of the Arctic Oscillation (AO) on the vertical distribution of stratospheric ozone in the Northern Hemisphere in winter is analyzed using observations and an offline chemical transport model. Positive ozone anomalies are found at low latitudes (0–30°N) and there are three negative anomaly centers in the northern mid- and high latitudes during positive AO phases. The negative anomalies are located in the Arctic middle stratosphere (~30 hPa, 70–90°N), Arctic upper troposphere/lower stratosphere (UTLS, 150–300 hPa, 70–90°N), and mid-latitude UTLS (70–300 hPa, 30–60°N). Further analysis shows that anomalous dynamical transport related to AO variability primarily controls these ozone changes. During positive AO events, positive ozone anomalies between 0–30°N at 50–150 hPa are related to the weakened meridional transport of the Brewer–Dobson circulation (BDC) and enhanced eddy transport. The negative ozone anomalies in the Arctic middle stratosphere are also caused by the weakened BDC, while the negative ozone anomalies in the Arctic UTLS are caused by the increased tropopause height, weakened BDC vertical transport, weaker exchange between the mid-latitudes and the Arctic, and enhanced ozone depletion via heterogeneous chemistry. The negative ozone anomalies in the mid-latitude UTLS are due mainly to enhanced eddy transport from the mid-latitudes to the equatorward of 30°N, while the transport of ozone-poor air from the Arctic to the mid-latitudes makes a minor contribution. Interpreting AO-related variability of stratospheric ozone, especially in the UTLS, would be helpful for the prediction of tropospheric ozone variability caused by AO