Collider tests of fundamental symmetries and neutrino properties

The CP parity of the Higgs boson and the details of the electroweak symmetry breaking are the two crucial ingredients to understand the matter-antimatter asymmetry in our universe. Electroweak baryogenesis is an intriguing solution to the puzzle of this unexplained observed asymmetry because of its testability at present and near future collider experiments. The possibilities of testing CP phase in the Two-Higgs-Doublets Models (2HDMs) and the generation of a strong first-order electroweak phase transition (SFOEWPT) in the real singlet model at the future high luminosity LHC are studied. In addition to the specific extensions to the Standard Model (SM), I also study the sensitivity of the future 100 TeV collider in probing the Wilson coefficients of the dimension 6 operators related to the Higgs sector using the effective field theory (EFT) approach. The observed non-zero neutrino mass is another phenomenon that the SM fails to explain. The Left-Right Symmetric Model (LRSM) proposes a natural explanation of the smallness of the neutrino mass and its connection to the high scale spontaneous parity violation. I study the prospects of searching the heavy right-handed neutrinos and measuring the mixing between the light and heavy neutrinos in the framework of the minimal LRSM at the future 100 TeV hadron collider

An End-to-End Multi-Task Learning to Link Framework for Emotion-Cause Pair Extraction

Emotion-cause pair extraction (ECPE), as an emergent natural language processing task, aims at jointly investigating emotions and their underlying causes in documents. It extends the previous emotion cause extraction (ECE) task, yet without requiring a set of pre-given emotion clauses as in ECE. Existing approaches to ECPE generally adopt a two-stage method, i.e., (1) emotion and cause detection, and then (2) pairing the detected emotions and causes. Such pipeline method, while intuitive, suffers from two critical issues, including error propagation across stages that may hinder the effectiveness, and high computational cost that would limit the practical application of the method. To tackle these issues, we propose a multi-task learning model that can extract emotions, causes and emotion-cause pairs simultaneously in an end-to-end manner. Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i.e., the links are directional. Emotion extraction and cause extraction are incorporated into the model as auxiliary tasks, which further boost the pair extraction. Experiments are conducted on an ECPE benchmarking dataset. The results show that our proposed model outperforms a range of state-of-the-art approaches.Comment: 7 pages, 3 figures, 5 table

MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning algorithms in single-agent settings. However, in multi-agent reinforcement learning (MARL), these techniques face challenges because each agent only receives partial observation from an environment influenced by others, resulting in correlated observations in the agent dimension. So it is necessary to consider agent-level information in representation learning for MARL. In this paper, we propose an effective framework called \textbf{M}ulti-\textbf{A}gent \textbf{M}asked \textbf{A}ttentive \textbf{C}ontrastive \textbf{L}earning (MA2CL), which encourages learning representation to be both temporal and agent-level predictive by reconstructing the masked agent observation in latent space. Specifically, we use an attention reconstruction model for recovering and the model is trained via contrastive learning. MA2CL allows better utilization of contextual information at the agent level, facilitating the training of MARL agents for cooperation tasks. Extensive experiments demonstrate that our method significantly improves the performance and sample efficiency of different MARL algorithms and outperforms other methods in various vision-based and state-based scenarios. Our code can be found in \url{https://github.com/ustchlsong/MA2CL

Investigating Impulse Buying Behavior in Live Streaming Commerce: The Role of Social Presence

Live streaming is changing the paradigm of people’s entertainment and consumption. It has been adopted by many small individual sellers to improve their market performance, leading to the emergence of live streaming commerce. Although existing literature has paid attention to consumer purchase behavior in live streaming commerce, little knowledge on impulse buying can be available. Drawing on social presence theory and cognitive-affective framework, this paper attempts to develop a theoretical model to investigate how social presence affects consumers’ urge to buy impulsively through the mediating mechanism of cognitive state (i.e., product risk) and affective state (i.e., affective intensity). This paper is expected to advance knowledge on consumers’ impulse buying in live streaming commerce

Distributed multi-user MIMO transmission using real-time sigma-delta-over-fiber for next generation fronthaul interface

To achieve the massive device connectivity and high data rate demanded by 5G, wireless transmission with wider signal bandwidths and higher-order multiple-input multiple-output (MIMO) is inevitable. This work demonstrates a possible function split option for the next generation fronthaul interface (NGFI). The proof-of-concept downlink architecture consists of real-time sigma-delta modulated signal over fiber (SDoF) links in combination with distributed multi-user (MU) MIMO transmission. The setup is fully implemented using off-the-shelf and in-house developed components. A single SDoF link achieves an error vector magnitude (EVM) of 3.14% for a 163.84 MHz-bandwidth 256-QAM OFDM signal (958.64 Mbps) with a carrier frequency around 3.5 GHz transmitted over 100 m OM4 multi-mode fiber at 850 nm using a commercial QSFP module. The centralized architecture of the proposed setup introduces no frequency asynchronism among remote radio units. For most cases, the 2 x 2 MU-MIMO transmission has little performance degradation compared to SISO, 0.8 dB EVM degradation for 40.96 MHz-bandwidth signals and 1.4 dB for 163.84 MHz-bandwidth on average, implying that the wireless spectral efficiency almost doubles by exploiting spatial multiplexing. A 1.4 Gbps data rate (720 Mbps per user, 163.84 MHz-bandwidth, 64-QAM) is reached with an average EVM of 6.66%. The performance shows that this approach is feasible for the high-capacity hot-spot scenario

The propagation of air fingers into an elastic branching network

We study experimentally the propagation of an air finger through the Y-bifurcation of an elastic, liquid-filled Hele-Shaw channel, as a benchtop model of airway reopening. With channel compliance provided by an elastic upper boundary, we can impose collapsed channel configurations into which we inject air with constant volume-flux. We typically observe steady finger propagation in the main channel, which is lost ahead of the Y-bifurcation but subsequently recovered in the daughter channels. At low levels of initial collapse, steady finger shapes and bubble pressure in the daughter channels map onto those in the main channel, despite small differences in initial collapse in different parts of the Y-channel. However, at higher levels of initial collapse where the elastic sheet almost touches the bottom boundary of the channel, experimentally indistinguishable fingers in the main channel can lead to multiple states of reopening of the daughter channels. The downstream distance at which steady propagation is recovered in the daughter channels also varies considerably with injection flow rate and initial collapse because of a transition in the mechanics regulating finger propagation. We find that the characteristic time and length-scales of this recovery are largest in the regime where viscous and surface tension forces dominate at low flow rate and/or low initial collapse, and that they decrease towards a constant plateau reached in the limit where elastic and surface tension forces balance at high flow rate and/or high initial collapse. Our findings suggest that practical networks are unlikely to comprise long enough channels for steady state propagation to remain established.Comment: 36 pages, 13 finger

Real-time head nod and shake detection for continuous human affect recognition

Human affect recognition is the field of study associated with using automatic techniques to identify human emotion or human affective state. A person’s affective states is often communicated non-verbally through body language. A large part of human body language communication is the use of head gestures. Almost all cultures use subtle head movements to convey meaning. Two of the most common and distinct head gestures are the head nod and the head shake gestures. In this paper we present a robust system to automatically detect head nod and shakes. We employ the Microsoft Kinect and utilise discrete Hidden Markov Models (HMMs) as the backbone to a to a machine learning based classifier within the system. The system achieves 86% accuracy on test datasets and results are provided