Adjective Density as a Text Formality Characteristic for Automatic Text Classification: A Study Based on the British National Corpus

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Probing the connectivity of neural circuits at single-neuron resolution using high-throughput DNA sequencing

There is growing excitement in determining the complete connectivity diagram of the brain—the "connectome". So far, the complete connectome has been established for only one organism, C. elegans, with 302 neurons connected by about 7000 synapses—and even this was a heroic task, requiring over 50 person-years of labor. Like all current approaches, this reconstruction was based on microscopy. Unfortunately, microscopy is poorly suited to the study of neural connectivity because brains are macroscopic structures, whereas synapses are microscopic. Nevertheless, there are several large-scale projects underway to scale up high-throughput microscopic approaches to the connectome.
Here we present a completely novel method for determining the brain's wiring diagram based on high-throughput DNA sequencing technology, which has not previously been applied in the context of neural connectivity. The appeal of using sequencing is that it is getting faster and cheaper exponentially: it will soon be routine to sequence an entire human genome (~3B nucleotides) within one day for $1000.
Our approach has three main components. First, we express a unique sequence of nucleotides—a DNA "barcode"—in individual neurons. A barcode consisting of a random string of even 30 nucleotides can uniquely label 10^{18} neurons, far more than the number of neurons in a mouse brain (fewer than 100 million). Second, we use a specially engineered transsynaptic virus to transport “host” barcodes from one neuron to synaptically coupled partners; after transsynaptic spread, each neuron contains copies of "invader" barcodes from other synaptically coupled neurons, as well its own "host" barcode. Third, we join pairs of host and invader barcodes into single pieces of DNA suitable for high-throughput sequencing. 
Modern sequencing technology could in principle yield the connectivity diagram of the entire mouse brain. Similar approaches can be applied to Drosophila and C. elegans. 
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Access-based consumption, behaviour change and future mobility: insights from visions of car sharing in Greater London

The way in which people choose to travel has changed throughout history and adaptations have taken place in order to provide the most convenient, efficient and cost-effective method(s) of transport possible. This research explores two trends—technological and socio-economic change—by discussing the effects of their application in the renewed drive to promote car clubs in Greater London through the introduction of new technologies and innovative ways in which a car can be used and hired, thus helping to generate new insights for car sharing. A mixed methods approach was used, combining secondary data analysis obtained from a car club member survey of 5898 people with in-depth, semi-structured interviews. Our findings show that there is an opportunity to utilise car clubs as a tool for facilitating a step change away from private vehicle ownership in the city. In addition, the results suggest that car club operators are seeking to deliver a mode of transport that is able to compete with private car ownership. In terms of policy implications, such findings would suggest that compromise is necessary, and an operator/authority partnership would offer the most effective way of delivering car clubs in a manner that benefits all Londoners

Sequential Keystroke Behavioral Biometrics for Mobile User Identification via Multi-view Deep Learning

With the rapid growth in smartphone usage, more organizations begin to focus on providing better services for mobile users. User identification can help these organizations to identify their customers and then cater services that have been customized for them. Currently, the use of cookies is the most common form to identify users. However, cookies are not easily transportable (e.g., when a user uses a different login account, cookies do not follow the user). This limitation motivates the need to use behavior biometric for user identification. In this paper, we propose DEEPSERVICE, a new technique that can identify mobile users based on user's keystroke information captured by a special keyboard or web browser. Our evaluation results indicate that DEEPSERVICE is highly accurate in identifying mobile users (over 93% accuracy). The technique is also efficient and only takes less than 1 ms to perform identification.Comment: 2017 Joint European Conference on Machine Learning and Knowledge Discovery in Database

Improved Decoding of Staircase Codes: The Soft-aided Bit-marking (SABM) Algorithm

Staircase codes (SCCs) are typically decoded using iterative bounded-distance decoding (BDD) and hard decisions. In this paper, a novel decoding algorithm is proposed, which partially uses soft information from the channel. The proposed algorithm is based on marking certain number of highly reliable and highly unreliable bits. These marked bits are used to improve the miscorrection-detection capability of the SCC decoder and the error-correcting capability of BDD. For SCCs with $2$-error-correcting Bose-Chaudhuri-Hocquenghem component codes, our algorithm improves upon standard SCC decoding by up to $0.30$~dB at a bit-error rate (BER) of $10^{-7}$. The proposed algorithm is shown to achieve almost half of the gain achievable by an idealized decoder with this structure. A complexity analysis based on the number of additional calls to the component BDD decoder shows that the relative complexity increase is only around $4\%$ at a BER of $10^{-4}$. This additional complexity is shown to decrease as the channel quality improves. Our algorithm is also extended (with minor modifications) to product codes. The simulation results show that in this case, the algorithm offers gains of up to $0.44$~dB at a BER of $10^{-8}$.Comment: 10 pages, 12 figure

Portrayal of Nuclear Energy on Twitter

Background: With more than 300 million active users, Twitter has become a platform to consume and share political rhetoric and popular science opinion. With long-held concerns of nuclear disasters, radioactive waste, and economic sustainability, nuclear energy is a particularly polarizing topic on Twitter. Purpose: This study used directed content analysis to examine how nuclear energy is portrayed on Twitter, a popular social media microblogging website. Methods: Using the search terms “nuclear energy” and “nuclear power,” researchers sampled tweets in the “Top” category, skipping 25 non-relevant tweets, for a total of 400 relevant tweets. A codebook was developed, pilot tested, and used to analyze themes. Results: Of the sample, 27% positively portrayed nuclear energy and 27% portrayed it negatively. Nearly half of the tweets (47%) had a balanced portrayal of nuclear energy. Nuclear energy was discussed in a political sense in 37% of tweets with 73% of the tweets containing factually correct information and the remaining 27% containing misinformation. Conclusion: Twitter users hold a wide spectrum of perspectives on the use, benefits, and effects of nuclear energy as a widespread energy source. The topic of nuclear energy is often weaponized for numerous causes such as environmentalism or political debates. Researchers reinforced their observation that nuclear energy and nuclear energy are one of many topics discussed on Twitter daily

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.Comment: KDD 201

Evolution of Interlayer Coupling in Twisted MoS2 Bilayers

Van der Waals (vdW) coupling is emerging as a powerful method to engineer and tailor physical properties of atomically thin two-dimensional (2D) materials. In graphene/graphene and graphene/boron-nitride structures it leads to interesting physical phenomena ranging from new van Hove singularities1-4 and Fermi velocity renormalization5, 6 to unconventional quantum Hall effects7 and Hofstadter's butterfly pattern8-12. 2D transition metal dichalcogenides (TMDCs), another system of predominantly vdW-coupled atomically thin layers13, 14, can also exhibit interesting but different coupling phenomena because TMDCs can be direct or indirect bandgap semiconductors15, 16. Here, we present the first study on the evolution of interlayer coupling with twist angles in as-grown MoS2 bilayers. We find that an indirect bandgap emerges in bilayers with any stacking configuration, but the bandgap size varies appreciably with the twist angle: it shows the largest redshift for AA- and AB-stacked bilayers, and a significantly smaller but constant redshift for all other twist angles. The vibration frequency of the out-of-plane phonon in MoS2 shows similar twist angle dependence. Our observations, together with ab initio calculations, reveal that this evolution of interlayer coupling originates from the repulsive steric effects, which leads to different interlayer separations between the two MoS2 layers in different stacking configurations