Constraints on absolute neutrino Majorana mass from current data

We present new constraints on the neutrino Majorana masses from the current data of neutrinoless double beta decay and neutrino flavour mixing. With the latest results of $0\nu\beta\beta$ progresses from various isotopes, including the recent calculations of the nuclear matrix elements, we find that the strongest constraint of the effective Majorana neutrino mass is from the $^{136}\rm{Xe}$ data of the EXO-200 and KamLAND-Zen collaborations. Further more, by combining the $0\nu\beta\beta$ experimental data with the neutrino mixing parameters from new analyses, we get the mass upper limits of neutrino mass eigenstates and flavour eigenstates and suggest several relations among these neutrino masses.Comment: 6 latex pages, 2 figures. Final version for publication in "The Universe

Ultra-high energy cosmic neutrinos from gamma-ray bursts

Based on recent proposal to associate IceCube TeV and PeV neutrino events with gamma-ray bursts~(GRBs) by considering the Lorentz violation of neutrinos, we provide a new estimate on the GRB neutrino flux and such result is much bigger than previous results by the IceCube Collaboration. Among these 24 neutrino ``shower" events above 60~TeV, 12 events are associated with GRBs. Such result is comparable with the prediction from GRB fireball models. Analysis of track events provide consistent result with the shower events to associate high energy cosmic neutrinos with GRBs under the same Lorentz violation features of neutrinos. We also make a background estimation and reveal GRBs as a significant source for the ultra-high energy IceCube neutrino events. Our work supports the Lorentz violation and $CPT$-violation of neutrinos, indicating new physics beyond relativity.Comment: 8 latex pages, 3 figures, final version for journal publicatio

User Review-Based Change File Localization for Mobile Applications

In the current mobile app development, novel and emerging DevOps practices (e.g., Continuous Delivery, Integration, and user feedback analysis) and tools are becoming more widespread. For instance, the integration of user feedback (provided in the form of user reviews) in the software release cycle represents a valuable asset for the maintenance and evolution of mobile apps. To fully make use of these assets, it is highly desirable for developers to establish semantic links between the user reviews and the software artefacts to be changed (e.g., source code and documentation), and thus to localize the potential files to change for addressing the user feedback. In this paper, we propose RISING (Review Integration via claSsification, clusterIng, and linkiNG), an automated approach to support the continuous integration of user feedback via classification, clustering, and linking of user reviews. RISING leverages domain-specific constraint information and semi-supervised learning to group user reviews into multiple fine-grained clusters concerning similar users' requests. Then, by combining the textual information from both commit messages and source code, it automatically localizes potential change files to accommodate the users' requests. Our empirical studies demonstrate that the proposed approach outperforms the state-of-the-art baseline work in terms of clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table

Chasing Chopsticks Street: A Sequel to Foshan’s Forgotten Qilous

Since China’s economic reform in the late 1970s, large extents of its cities’ vernacular urban fabrics have been replaced by state-of-the-art high-rises in a building frenzy that was equally unprecedented in its destructive endeavours. Devasted were not only physical relics passed down from ancestors, but also the intangible heritage – memories, values, and rituals embodied in the architecture – that connected a people to its cultural roots. As local distinctiveness was sacrificially offered to a ubiquity of anonymous skyscrapers, the alarming loss of collective remembrance and identity awakens a fervor to reintegrate the past – in its meagre remains – into the modern-day city. Yet, contemporary practice, from the preservation of iconic buildings to the commercial-romanticization of entire historic districts, often bestow upon urban relics a seeming immutability that in effect jeopardizes their communicative potency to convey the age-honoured tales and customs orchestrated by the architectural artefacts. This thesis explores alternative intervention strategies for vernacular buildings in the historic inner-city, to withdraw them from modern obsolescence and to perpetuate their role as active hosts of living traditions in the contemporary city. The investigation focuses on a cluster of dilapidating shophouses, locally called “qilous,” on Chopsticks Street in Foshan, China, one of the few remaining swatches of the city’s historic core. The study searches for insights from the Chinese attitude towards the past, the shifting and the resilient patterns, examining contemporary and historical texts to understand the underlying political, economic, and philosophical influences. It dives into the macro-histories and micro-stories of the qilou’s maritime typological genealogy and intimately local significance, observed and documented through a process of drawing by hand. It finally proposes design interventions for three qilous on Chopsticks Street, presented through a series of hand-drawn narratives, inspired by cinematographic approaches, accompanied by short poems. The stories, at once visual, architectural, and metaphorical, seek to unveil the buildings’ accreted past and embodied significance in a contemporary language that can be perceived and interpreted by people today, and continue to cue cultural rituals for present and posterity

SSR: SAM is a Strong Regularizer for domain adaptive semantic segmentation

We introduced SSR, which utilizes SAM (segment-anything) as a strong regularizer during training, to greatly enhance the robustness of the image encoder for handling various domains. Specifically, given the fact that SAM is pre-trained with a large number of images over the internet, which cover a diverse variety of domains, the feature encoding extracted by the SAM is obviously less dependent on specific domains when compared to the traditional ImageNet pre-trained image encoder. Meanwhile, the ImageNet pre-trained image encoder is still a mature choice of backbone for the semantic segmentation task, especially when the SAM is category-irrelevant. As a result, our SSR provides a simple yet highly effective design. It uses the ImageNet pre-trained image encoder as the backbone, and the intermediate feature of each stage (ie there are 4 stages in MiT-B5) is regularized by SAM during training. After extensive experimentation on GTA5$\rightarrow$Cityscapes, our SSR significantly improved performance over the baseline without introducing any extra inference overhead

GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence

Conditional story generation is significant in human-machine interaction, particularly in producing stories with complex plots. While Large language models (LLMs) perform well on multiple NLP tasks, including story generation, it is challenging to generate stories with both complex and creative plots. Existing methods often rely on detailed prompts to guide LLMs to meet target conditions, which inadvertently restrict the creative potential of the generated stories. We argue that leveraging information from exemplary human-written stories facilitates generating more diverse plotlines. Delving deeper into story details helps build complex and credible plots. In this paper, we propose a retrieval-au\textbf{G}mented sto\textbf{R}y generation framework with a f\textbf{O}rest of e\textbf{V}id\textbf{E}nce (GROVE) to enhance stories' complexity. We build a retrieval repository for target conditions to produce few-shot examples to prompt LLMs. Additionally, we design an ``asking-why'' prompting scheme that extracts a forest of evidence, providing compensation for the ambiguities that may occur in the generated story. This iterative process uncovers underlying story backgrounds. Finally, we select the most fitting chains of evidence from the evidence forest and integrate them into the generated story, thereby enhancing the narrative's complexity and credibility. Experimental results and numerous examples verify the effectiveness of our method.Comment: Findings of EMNLP 202