Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot Exoplanet Atmospheres Resolved with Transit Spectroscopy (HEARTS)

High-resolution optical spectroscopy is a powerful tool to characterise exoplanetary atmospheres from the ground. The sodium D lines, with their large cross sections, are especially suited to studying the upper layers of atmospheres in this context. We report on the results from Hot Exoplanet Atmosphere Resolved with Transit Spectroscopy survey (HEARTS), a spectroscopic survey of exoplanet atmospheres, performing a comparative study of hot gas giants to determine the effects of stellar irradiation. In this second installation of the series, we highlight the detection of neutral sodium on the ultra-hot giant WASP-76b. We observed three transits of the planet using the High-Accuracy Radial-velocity Planet Searcher (HARPS) high-resolution spectrograph at the European Southern Observatory (ESO) 3.6 m telescope and collected 175 spectra of WASP-76. We repeatedly detect the absorption signature of neutral sodium in the planet atmosphere (0.371 ± 0.034%; 10.75σ in a 0.75 Å passband). The sodium lines have a Gaussian profile with full width at half maximum (FWHM) of 27.6 ± 2.8 km s−1. This is significantly broader than the line spread function of HARPS (2.7 km s−1). We surmise that the observed broadening could trace the super-rotation in the upper atmosphere of this ultra-hot gas giant.
Hot Exoplanet Atmospheres Resolved with Transit Spectroscopy (HEARTS)

WASP-127b is one of the puffiest exoplanets found to date, with a mass of only 3.4 Neptune masses, but a radius larger than that of Jupiter. It is located at the border of the Neptune desert, which describes the lack of highly irradiated Neptune-sized planets, and which remains poorly understood. Its large scale height and bright host star make the transiting WASP-127b a valuable target to characterise in transmission spectroscopy. We used combined EulerCam and TESS light curves to recalculate the system parameters. Additionally, we present an in-depth search for sodium in four transit observations of WASP-127b, obtained as part of the Hot Exoplanet Atmosphere Resolved with Transit Spectroscopy (HEARTS) survey with the High Accuracy Radial velocity Planet Searcher (HARPS) spectrograph. Two nights from this dataset were analysed independently by another team. The team claimed a detection of sodium that is incompatible with previous studies of data from both ground and space. We show that this strong sodium detection is due to contamination from telluric sodium emissions and the low signal-to-noise ratio in the core of the deep stellar sodium lines. When these effects are properly accounted for, the previous sodium signal is reduced to an absorption of 0.46 ± 0.20% (2.3σ), which is compatible with analyses of WASP-127b transits carried out with other instruments. We can fit a Gaussian to the D2 line, but the D1 line was not detected. This indicates an unusual line ratio if sodium exists in the atmosphere. Follow-up of WASP-127 at high resolution and with high sensitivity is required to firmly establish the presence of sodium and analyse its line shape.
Mitigating Action Hysteresis in Traffic Signal Control with Traffic Predictive Reinforcement Learning

Traffic signal control plays a pivotal role in the management of urban traffic flow. With the rapid advancement of reinforcement learning, the development of signal control methods has seen a significant boost. However, a major challenge in implementing these methods is ensuring that signal lights do not change abruptly, as this can lead to traffic accidents. To mitigate this risk, a time-delay is introduced in the implementation of control actions, but usually has a negative impact on the overall efficacy of the control policy. To address this challenge, this paper presents a novel Traffic Signal Control Framework (PRLight), which leverages an On-policy Traffic Control Model (OTCM) and an Online Traffic Prediction Model (OTPM) to achieve efficient and real-time control of traffic signals. The framework collects multi-source traffic information from a local-view graph in real-time and employs a novel fast attention mechanism to extract relevant traffic features. To be specific, OTCM utilizes the predicted traffic state as input, eliminating the need for communication with other agents and maximizing computational efficiency while ensuring that the most relevant information is used for signal control. The proposed framework was evaluated on both simulated and real-world road networks and compared to various state-of-the-art methods, demonstrating its effectiveness in preventing traffic congestion and accidents.
Context-aware Event Forecasting via Graph Disentanglement

Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. In the separation stage, we leverage the context as a prior guidance to disentangle the event graph into multiple sub-graphs, followed by a context-specific module to model the relational-temporal patterns within each context. In the collaboration stage, we design a cross-context module to retain the collaborative associations among multiple contexts. Since there is no available dataset for this novel task, we construct three large- scale datasets based on GDELT. Experimental results demonstrate hat our model outperforms a list of SOTA methods. The dataset and code are released via https://github.com/yecchen/SeCoGD.
Local-Access Generators for Basic Random Graph Models by Amartya

Consider a computation on a massive random graph: Does one need to generate the whole random graph up front, prior to performing the computation? Or, is it possible to provide an oracle to answer queries to the random graph "on-the-fly" in a much more efficient manner overall? That is, to provide a local access generator which incrementally constructs the random graph locally, at the queried portions, in a manner consistent with the random graph model and all previous choices. Local access generators can be useful when studying the local behavior of specific random graph models. Our goal is to design local access generators whose required resource overhead for answering each query is significantly more efficient than generating the whole random graph. Our results focus on undirected graphs with independent edge probabilities, that is, each edge is chosen as an independent Bernoulli random variable. We provide a general implementation for generators in this model. Then, we use this construction to obtain the first efficient local implementations for the Erdos-Renyi G(n, p) model, and the Stochastic Block model. As in previous local-access implementations for random graphs, we support VERTEXPAIR, NEXT-NEIGHBOR queries, and ALL-NEIGHBORS queries. In addition, we introduce a new RANDOM-NEIGHBOR query. We also give the first local-access generation procedure for ALL-NEIGHBORS queries in the (sparse and directed) Kleinberg's Small-World model. Note that, in the sparse case, an ALL-NEIGHBORS query can be used to simulate the other types of queries efficiently. All of our generators require no pre-processing time, and answer each query using O(poly(log n)) time, random bits, and additional space. Thesis Supervisor: Ronitt Rubinfeld Title: Professor
Synthesizing Robust Adversarial Examples

Neural networks are susceptible to adversarial examples: small, carefully-crafted perturbations can cause networks to misclassify inputs in arbitrarily chosen ways. However, some studies have showed that adversarial examples crafted following the usual methods are not tolerant to small transformations: for example, zooming in on an adversarial image can cause it to be classified correctly again. This raises the question of whether adversarial examples are a concern in practice, because many real-world systems capture images from multiple scales and perspectives. This paper shows that adversarial examples can be made robust to distributions of transformations. Our approach produces single images that are simultaneously adversarial under all transformations in a chosen distribution, showing that we cannot rely on transformations such as rescaling, translation, and rotation to protect against adversarial examples.
Handwritten Signature Verification Using Hidden Markov Models

In this paper we present the work developed on offline signature verification using Hidden Markov Models (HMM)[1]. HMM is a well-known technique used by other biometric features, for instance, in speaker recognition and dynamic or on-line signature verification. Our goal here is to extend Left-to-Right (LR)-HMM to the field of static or off-line signature processing using results provided by image connectivity analysis. The chain encoding of perimeter points for each blob obtained by this analysis is an ordered set of points in the space, clockwise around the perimeter of the blob. We discuss two different ways of generating the models depending on the way the blobs obtained from the connectivity analysis are ordered. Finally, two LR-HMM models are trained using the parameters obtained by the mentioned techniques. Verification results of the two techniques are compared and some improvements are proposed.
One Instrument to Rule Them All: The Bias and Coverage of Just-ID IV *

We argue that in microeconometric applications, just-identi(cid:28)ed instrumental variables (IV) estimators are virtually unbiased, and the usual inference strategies are likely adequate. Con(cid:28)dence interval undercoverage exceeds 5% only for endogeneity beyond that seen even when IV and OLS estimates di(cid:27)er by an order of magnitude. Three widely-cited applications are used to explain why endogeneity is likely low enough for IV estimates to be reliable. IV identi(cid:28)cation typically implies a (cid:28)rst-stage sign restriction; most analysts probably screen their estimates accordingly. We show that screening on the estimated (cid:28)rst stage sign halves median bias of conventional IV without reducing coverage.
Anomalous temperature coefficient of resistance in graphene nanowalls/polymer films and applications in infrared photodetectors

Abstract Graphene nanowalls (GNWs) exhibit outstanding optoelectronic properties due to their peculiar structure, which makes them a great potential in infrared (IR) detection. Herein, a novel IR detector that is composed of polydimethylsiloxane (PDMS) and designed based on GNWs is demonstrated. Such detector possesses an anomalous temperature coefficient of resistance of 180% K−1 and a relatively high change rate of current (up to 16%) under IR radiation from the human body. It primarily attributes to the ultra-high IR absorption of the GNWs and large coefficient of thermal expansion of PDMS. In addition, the GNW/PDMS device possesses excellent detection performance in the IR region with a responsivity of ~1.15 mA W−1. The calculated detectivity can reach 1.07×108 cm Hz1/2 W−1, which is one or two orders of magnitude larger than that of the traditional carbon-based IR detectors. The significant performance indicates that the GNW/PDMS-based devices reveal a novel design concept and promising applications for the future new-generation IR photodetectors.
Practice in Synonym Extraction at Large Scale

Synonym extraction is an important task in natural language processing and often used as a submodule in query expansion, question answering and other applications. Automatic synonym extractor is highly preferred for large scale applications. Previous studies in synonym extraction are most limited to small scale datasets. In this paper, we build a large dataset with 3.4 million synonym/non-synonym pairs to capture the challenges in real world scenarios. We proposed (1) a new cost function to accommodate the unbalanced learning problem, and (2) a feature learning based deep neural network to model the complicated relationships in synonym pairs. We compare several different approaches based on SVMs and neural networks, and find out a novel feature learning based neural network outperforms the methods with hand-assigned features. Specifically, the best performance of our model surpasses the SVM baseline with a significant 97\% relative improvement.
Enhance Feature Discrimination for Unsupervised Hashing

We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed method, using Gaussian Mixture Model, embeds feature vector into a low-dimensional vector and, simultaneously, enhances the discriminative property of features before passing them into hashing. Our experiment shows that the proposed method boosts the hashing performance of many state-of-the-art, e.g. Binary Autoencoder (BA) [1], Iterative Quantization (ITQ) [2], in standard evaluation metrics for the three main benchmark datasets.
Behaviour-aware Tourist Profiles Data Generation

We propose a computational model to synthesise individual-level user profiles from scarce population-level data in tourism domain. Namely, our model exploits, as input, summary information about the items (and item attributes) selected by users, and, as output, builds individual-level user profiles that respect the provided input. As a key contribution, we utilise discrete choice behavioural model to conjoin (via chi-square divergence minimisation) the choices made by the synthesised population of users with the choices observed in the real data. To showcase our idea, we release a code and a dataset that includes synthesised profiles of 10,000 users that interacted with circa 200 items.
Optimization of DNA isolation and PCR protocol for RAPD analysis of selected medicinal and aromatic plants of conservation concern from Peninsular India

Genetic analysis of plants relies on high yields of pure DNA samples. Here we present the optimization of DNA isolation and PCR conditions for RAPD analysis of selected medicinal and aromatic plants of conservation concern from Peninsular India containing high levels of polysaccharides, polyphenols and secondary metabolites. The method involves a modified CTAB extraction employing polyvinyl pyrrolidone while grinding, successive long-term Chloroform : lsoamyalcohol extractions, an overnight RNase treatment with all steps carried out at room temperature. The yield of DNA ranged from 1-2 μg/μl per gram of the leaf tissue and the purity (ratio) was between 1.6-1.7 indicating minimal levels of contaminating metabolites. The technique is ideal for isolation of DNA from different plant species and the DNA isolated was used for randomly amplified polymorphic DNA (RAPD) analysis. RAPD protocol was optimized based on the use of higher concentration of MgCl2 (3 mM), lower concentrations of primer (0.5 μM) and Taq polymerase (0.2 units), 50 ng of template DNA and an annealing temperature of 37°C, resulted optimal amplification. Reproducible amplifiable products were observed in all PCR reactions. Thus the results indicate that the optimized protocol for DNA isolation and PCR was amenable to plant species belonging to different genera which is suitable for further work on diversity analysis.
Adaptive Hard Thresholding for Block Matching Three dimension for Denoising Volumetric Data

: BM3D is a recent state of art patch based denoising algorithm . It works on the fact that an image has a locally sparse representation in transform domain.It is composed of two stages i)hard thresholding and ii)weiner filtering This paper provides a mechanism that incorporates an improved version of BM3D which combines the digital image characteristic with added noise pollution levels, and adaptively selects block-matching threshold in grouping stage for an extended BM3D to four dimension so as to denoise volumetric data corrupted by Gaussian and rician noise. Experimental results demonstrate it outperforms not only in terms of objective criteria of PSNR, but also in improving the visual quality.
Myogenetic Oligodeoxynucleotide Induces Myocardial Differentiation of Murine Pluripotent Stem Cells

An 18-base myogenetic oligodeoxynucleotide (myoDN), iSN04, acts an anti-nucleolin aptamer and induces myogenic differentiation of skeletal muscle myoblasts. This study investigated the effect of iSN04 on murine embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). In the undifferentiated state, iSN04 inhibited the proliferation of ESCs and iPSCs but did not affect the expression of pluripotent markers. In the differentiating condition, iSN04 treatment of ESCs/iPSCs from day 5 onward dramatically induced the differentiation into Nkx2-5+ beating cardiomyocytes with upregulation of Gata4, Isl1, and Nkx2-5, whereas iSN04 treatment from earlier stages completely inhibited cardiomyogenesis. RNA sequencing revealed that iSN04 treatment from day 5 onward contributes to the generation of cardiac progenitors by modulating the Wnt signaling pathway. Immunostaining showed that iSN04 suppressed the cytoplasmic translocation of nucleolin and restricted it to the nucleoli. These results demonstrate that nucleolin inhibition by iSN04 facilitates the terminal differentiation of cardiac mesoderm into cardiomyocytes, but interferes with the differentiation of early mesoderm into the cardiac lineage. This is the first report on the generation of cardiomyocytes from pluripotent stem cells using a DNA aptamer. Since iSN04 did not induce hypertrophic responses in primary-cultured cardiomyocytes, iSN04 would be useful and safe for the regenerative therapy of heart failure using stem cell-derived cardiomyocytes.
Where to Play : Retrieval of Video Segments using Natural-Language eries

In this paper, we propose a new approach for retrieval of video segments using natural language queries. Unlike most previous approaches such as concept-based methods or rule-based structured models, the proposed method uses image captioning model to construct sentential queries for visual information. In detail, our approach exploits multiple captions generated by visual features in each image with ‘Densecap’. en, the similarities between captions of adjacent images are calculated, which is used to track semantically similar captions over multiple frames. Besides introducing this novel idea of ’tracking by captioning’, the proposed method is one of the rst approaches that uses a language generation model learned by neural networks to construct semantic query describing the relations and properties of visual information. To evaluate the eectiveness of our approach, we have created a new evaluation dataset, which contains about 348 segments of scenes in 20 movie-trailers. rough quantitative and qualitative evaluation, we show that our method is eective for retrieval of video segments using natural language queries.
Research on Artificial Intelligence Technology of Virtual Reality Teaching Method in Digital Media Art Creation

The popularity of virtual reality technology has gradually expanded from the computer field to all aspects of life and has also become a new approach to digital media art. In this paper, we analyze the characteristics of virtual reality technology and the three commonly used methods and explore and practice them in-depth. The experimental results provide a concrete reference and comparison basis for the teaching and research fields. In this paper, 160 students from the same school were selected for the teaching experiment. They were divided into four groups and taught courses on different artificial intelligence-based methods of creating virtual reality digital media art for one semester. They were artificial intelligence-based image recognition technology, artificial intelligence-based speech recognition technology, artificial intelligence-based natural language processing technology and traditional teaching methods. At the end of the instruction, we obtained subjective and objective test scores for four students using the same tests. All scores indicated that the effectiveness was much higher than traditional methods, regardless of which virtual reality technology was used. In addition, we found that students were far more sensitive and enlightened to visuals and images than to language.
Learning Adversary-Resistant Deep Neural Networks

Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design. This attack consists of generating particular synthetic examples referred to as adversarial samples. These samples are constructed by slightly manipulating real data-points in order to "fool" the original DNN model, forcing it to mis-classify previously correctly classified samples with high confidence. Addressing this flaw in the model is essential if DNNs are to be used in critical applications such as those in cyber security. Previous work has provided various learning algorithms to enhance the robustness of DNN models, and they all fall into the tactic of "security through obscurity". This means security can be guaranteed only if one can obscure the learning algorithms from adversaries. Once the learning technique is disclosed, DNNs protected by these defense mechanisms are still susceptible to adversarial samples. In this work, we investigate this issue shared across previous research work and propose a generic approach to escalate a DNN's resistance to adversarial samples. More specifically, our approach integrates a data transformation module with a DNN, making it robust even if we reveal the underlying learning algorithm. To demonstrate the generality of our proposed approach and its potential for handling cyber security applications, we evaluate our method and several other existing solutions on datasets publicly available. Our results indicate that our approach typically provides superior classification performance and resistance in comparison with state-of-art solutions.