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Calibrating the optical properties within the detection medium of a neutrino telescope is crucial for determining its angular resolution and energy scale. For the next generation of neutrino telescopes planned to be constructed in deep water, such as the TRopIcal DEep-sea Neutrino Telescope (TRIDENT), there are additional challenges due to the dynamic nature and potential non-uniformity of the water medium. This necessitates a real-time optical calibration system distributed throughout the large detector array. This study introduces a custom-designed CMOS camera system equipped with rapid image processing algorithms, providing a real-time optical calibration method for TRIDENT and other similar projects worldwide. In September 2021, the TRIDENT Pathfinder experiment (TRIDENT Explorer, T-REX for short) successfully deployed this camera system in the West Pacific Ocean at a depth of 3420 meters. Within 30 minutes, about 3000 images of the T-REX light source were captured, allowing for the in-situ measurement of seawater attenuation and absorption lengths under three wavelengths. This deep-sea experiment for the first time showcased a technical demonstration of a functioning camera calibration system in a dynamic neutrino telescope site, solidifying a substantial part of the calibration strategies for the future TRIDENT project.

The multi-beam measurement system plays a crucial role in ocean mapping and underwater terrain detection. By simultaneously transmitting multiple beams, the system can accurately receive sound waves reflected from the seabed, providing more precise and comprehensive water depth information while effectively revealing the complexity and characteristics of underwater terrain. Building upon the background and application provided by Question B of the 2023 National Mathematical Contest in Modeling for College Students, this paper investigates the relationship between ocean floor width measurement and factors such as beam position, angle, and slope. Utilizing geometric relations, trigonometric similarity, and sine theorem, a mathematical model is established to determine adjacent strip coverage width and overlap ratio. Furthermore, an optimal strategy is determined using a greedy algorithm, while binary search backtracking is employed to derive the interval of the next adjacent survey line with the required overlap ratio in order to obtain an optimal terrain detection strategy., Comment: 8 pages and 5 images, which have been included in the Conference: 2024 3th International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA 2023)

This paper explores the optimization of Ground Delay Programs (GDP), a prevalent Traffic Management Initiative used in Air Traffic Management (ATM) to reconcile capacity and demand discrepancies at airports. Employing Reinforcement Learning (RL) to manage the inherent uncertainties in the national airspace system-such as weather variability, fluctuating flight demands, and airport arrival rates-we developed two RL models: Behavioral Cloning (BC) and Conservative Q-Learning (CQL). These models are designed to enhance GDP efficiency by utilizing a sophisticated reward function that integrates ground and airborne delays and terminal area congestion. We constructed a simulated single-airport environment, SAGDP_ENV, which incorporates real operational data along with predicted uncertainties to facilitate realistic decision-making scenarios. Utilizing the whole year 2019 data from Newark Liberty International Airport (EWR), our models aimed to preemptively set airport program rates. Despite thorough modeling and simulation, initial outcomes indicated that the models struggled to learn effectively, attributed potentially to oversimplified environmental assumptions. This paper discusses the challenges encountered, evaluates the models' performance against actual operational data, and outlines future directions to refine RL applications in ATM.

Next generation neutrino telescopes are highly anticipated to boost the development of neutrino astronomy. A multi-cubic-kilometer neutrino telescope, TRopIcal DEep-sea Neutrino Telescope (TRIDENT), was proposed to be built in the South China Sea. The detector aims to achieve ~ 0.1 degree angular resolution for track-like events at energy above 100 TeV by using hybrid digital optical modules, opening new opportunities for neutrino astronomy. In order to measure the water optical properties and marine environment of the proposed TRIDENT site, a pathfinder experiment was conducted, in which a 100-meter-long string consisting of three optical modules was deployed at a depth of 3420 m to perform in-situ measurements. The central module emits light by housing LEDs, whereas the other two modules detect light with two independent and complementary systems: the PMT and the camera systems. By counting the number of detected photons and analyzing the photon arrival time distribution, the PMT system can measure the absorption and scattering lengths of sea water, which serve as the basic inputs for designing the neutrino telescope. In this paper, we present the design concept, calibration and performance of the PMT system in the pathfinder experiment.

In September 2021, a site scouting mission known as the TRIDENT pathfinder experiment (TRIDENT EXplorer, T-REX for short) was conducted in the South China Sea with the goal of envisaging a next-generation multi-cubic-kilometer neutrino telescope. One of the main tasks is to measure the \textit{in-situ} optical properties of seawater at depths between $2800~\mathrm{m}$ and $3500~\mathrm{m}$, where the neutrino telescope will be instrumented. To achieve this, we have developed a light emitter module equipped with a clock synchronization system to serve as the light source, which could be operated in pulsing and steady modes. Two light receiver modules housing both photomultiplier tubes (PMTs) and cameras are employed to detect the photons emitted by the light source. This paper presents the instrumentation of the light source in T-REX, including its design, calibration, and performance.

The performance of a large-scale water Cherenkov neutrino telescope relies heavily on the transparency of the surrounding water, quantified by its level of light absorption and scattering. A pathfinder experiment was carried out to measure the optical properties of deep seawater in South China Sea with light-emitting diodes (LEDs) as light sources, photon multiplier tubes (PMTs) and cameras as photon sensors. Here, we present an optical simulation program employing the Geant4 toolkit to understand the absorption and scattering processes in the deep seawater, which helps to extract the underlying optical properties from the experimental data. The simulation results are compared with the experimental data and show good agreements. We also verify the analysis methods that utilize various observables of the PMTs and the cameras with this simulation program, which can be easily adapted by other neutrino telescope pathfinder experiments and future large-scale detectors., Comment: 27 pages, 11 figures

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise.

Binary code similarity detection (BCSD) is widely used in various binary analysis tasks such as vulnerability search, malware detection, clone detection, and patch analysis. Recent studies have shown that the learning-based binary code embedding models perform better than the traditional feature-based approaches. In this paper, we propose a novel transformer-based binary code embedding model named UniASM to learn representations of the binary functions. We design two new training tasks to make the spatial distribution of the generated vectors more uniform, which can be used directly in BCSD without any fine-tuning. In addition, we present a new tokenization approach for binary functions, which increases the token's semantic information and mitigates the out-of-vocabulary (OOV) problem. We conduct an in-depth analysis of the factors affecting model performance through ablation experiments and obtain some new and valuable findings. The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approach on the evaluation dataset. The average scores of Recall@1 on cross-compilers, cross-optimization levels, and cross-obfuscations are 0.77, 0.72, and 0.72. Besides, in the real-world task of known vulnerability search, UniASM outperforms all the current baselines., Comment: This work has been submitted to the IEEE for possible publication

Code search with natural language helps us reuse existing code snippets. Thanks to the Transformer-based pretraining models, the performance of code search has been improved significantly. However, due to the quadratic complexity of multi-head self-attention, there is a limit on the input token length. For efficient training on standard GPUs like V100, existing pretrained code models, including GraphCodeBERT, CodeBERT, RoBERTa (code), take the first 256 tokens by default, which makes them unable to represent the complete information of long code that is greater than 256 tokens. To tackle the long code problem, we propose a new baseline SEA (Split, Encode and Aggregate), which splits long code into code blocks, encodes these blocks into embeddings, and aggregates them to obtain a comprehensive long code representation. With SEA, we could directly use Transformer-based pretraining models to model long code without changing their internal structure and re-pretraining. We also compare SEA with sparse Trasnformer methods. With GraphCodeBERT as the encoder, SEA achieves an overall mean reciprocal ranking score of 0.785, which is 10.1% higher than GraphCodeBERT on the CodeSearchNet benchmark, justifying SEA as a strong baseline for long code search. Our source code and experimental data are available at: https://github.com/fly-dragon211/SEA., Comment: Accepted by LREC-COLING 2024

With a good code search engine, developers can reuse existing code snippets and accelerate software development process. Current code search methods can be divided into two categories: traditional information retrieval (IR) based and deep learning (DL) based approaches. DL-based approaches include the cross-encoder paradigm and the bi-encoder paradigm. However, both approaches have certain limitations. The inference of IR-based and bi-encoder models are fast, however, they are not accurate enough; while cross-encoder models can achieve higher search accuracy but consume more time. In this work, we propose TOSS, a two-stage fusion code search framework that can combine the advantages of different code search methods. TOSS first uses IR-based and bi-encoder models to efficiently recall a small number of top-k code candidates, and then uses fine-grained cross-encoders for finer ranking. Furthermore, we conduct extensive experiments on different code candidate volumes and multiple programming languages to verify the effectiveness of TOSS. We also compare TOSS with six data fusion methods. Experimental results show that TOSS is not only efficient, but also achieves state-of-the-art accuracy with an overall mean reciprocal ranking (MRR) score of 0.763, compared to the best baseline result on the CodeSearchNet benchmark of 0.713. Our source code and experimental data are available at: https://github.com/fly-dragon211/TOSS., Comment: Accepted by WSDM 2023

Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the proposed MATIC model shows advantages comparing with traditional method in screening effective compounds in vivo. Next, we explored the model interpretability and found that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attentions. Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.

Negation is a common linguistic skill that allows human to express what we do NOT want. Naturally, one might expect video retrieval to support natural-language queries with negation, e.g., finding shots of kids sitting on the floor and not playing with a dog. However, the state-of-the-art deep learning based video retrieval models lack such ability, as they are typically trained on video description datasets such as MSR-VTT and VATEX that lack negated descriptions. Their retrieved results basically ignore the negator in the sample query, incorrectly returning videos showing kids playing with dog. This paper presents the first study on learning to understand negation in video retrieval and make contributions as follows. By re-purposing two existing datasets (MSR-VTT and VATEX), we propose a new evaluation protocol for video retrieval with negation. We propose a learning based method for training a negation-aware video retrieval model. The key idea is to first construct a soft negative caption for a specific training video by partially negating its original caption, and then compute a bidirectionally constrained loss on the triplet. This auxiliary loss is weightedly added to a standard retrieval loss. Experiments on the re-purposed benchmarks show that re-training the CLIP (Contrastive Language-Image Pre-Training) model by the proposed method clearly improves its ability to handle queries with negation. In addition, the model performance on the original benchmarks is also improved., Comment: Accepted by ACMMM2022

While deep learning based image retrieval is reported to be vulnerable to adversarial attacks, existing works are mainly on image-to-image retrieval with their attacks performed at the front end via query modification. By contrast, we present in this paper the first study about a threat that occurs at the back end of a text-to-image retrieval (T2IR) system. Our study is motivated by the fact that the image collection indexed by the system will be regularly updated due to the arrival of new images from various sources such as web crawlers and advertisers. With malicious images indexed, it is possible for an attacker to indirectly interfere with the retrieval process, letting users see certain images that are completely irrelevant w.r.t. their queries. We put this thought into practice by proposing a novel Trojan-horse attack (THA). In particular, we construct a set of Trojan-horse images by first embedding word-specific adversarial information into a QR code and then putting the code on benign advertising images. A proof-of-concept evaluation, conducted on two popular T2IR datasets (Flickr30k and MS-COCO), shows the effectiveness of the proposed THA in a white-box mode., Comment: Accepted by ICASSP 2023

Dimuon events induced by charm-quark productions from neutrino deep inelastic scattering (DIS) processes have been studied in traditional DIS experiments for decades. The recent progress in neutrino telescopes makes it possible to search such dimuon events at energies far beyond laboratory scale. In this paper, we construct a simulation framework to calculate yields and distributions of dimuon signals in an IceCube-like km3 scale neutrino telescope. Due to experimental limitation in the resolution of double-track lateral distance, only dimuon produced outside the detector volume are considered. Detailed information about simulation results for ten years exposure is demonstrated. Both an earlier work [1] and our work study a similar situation, we therefore use that paper as a baseline to conduct comparisons. We then estimate the impacts of different calculation methods of muon energy losses, and find that, the average energy loss formula used in that work results in more events by contrast with Monte Carlo simulation. Finally, we study the experimental potential of dimuon searches under the hypothesis of single-muon background-only. Our results based on a simplified double-track reconstruction indicate a moderate sensitivity especially with the ORCA configuration. Further developments on both the reconstruction algorithm and possible detector designs are thus required, and are under investigation., Comment: 12 pages, 8 figures, published version

In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval. Different from previous research that considers feature fusion only at one end, let it be video or text, we aim for feature fusion for both ends within a unified framework. We hypothesize that optimizing the convex combination of the features is preferred to modeling their correlations by computationally heavy multi-head self attention. We propose Lightweight Attentional Feature Fusion (LAFF). LAFF performs feature fusion at both early and late stages and at both video and text ends, making it a powerful method for exploiting diverse (off-the-shelf) features. The interpretability of LAFF can be used for feature selection. Extensive experiments on five public benchmark sets (MSR-VTT, MSVD, TGIF, VATEX and TRECVID AVS 2016-2020) justify LAFF as a new baseline for text-to-video retrieval., Comment: Accepted by ECCV2022