The HERMES (High Energy Rapid Modular Ensemble of Satellites) Pathfinder mission serves as an in-orbit demonstration of a constellation of nanosatellites whose primary scientific purpose is to discover intense high-energy transients, such as gamma-ray bursts, across a broad energy range (few keV to few MeV) with unparalleled temporal precision and exact localisation. By 2024, the first constellation of six nanosatellites is expected to be launched. To fully exploit satellite data and allow faint astronomical events to emerge, a precise estimation of satellite background count rates is required to determine whether the event is statistically valid or not. The dynamics of the background are related to the satellite's orbital information, which varies in the order of minutes, potentially hiding long transient events. This work introduces two main contributions I have brought ahead; first a novel background estimator is presented that could potentially be fitted to any type of X/Gamma-ray satellite space telescope, capable of capturing long-term dynamics and accurate enough to detect faint transients. This estimator is built using a Neural Network and tested on data from the Fermi Gamma-ray Space Telescope's Gamma Burst Monitor (GBM). As a second objective, it is employed a trigger algorithm, called FOCuS (Functional Online CUSUM), to extract events from the background using the background estimator. The resulting framework, DeepGRB, can identify astronomical events that are both present and absent from the Fermi-GBM catalog. The analysis of the discovered events reveals the strengths and weaknesses of the framework.

Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are invariably affected by the plethora of redundant and erroneous edges present within the constructed graphs. In this paper, we propose treating these noisy edges as adversarial attack and use a spectral adversarial robustness evaluation method to diminish the impact of noisy edges on the performance of graph algorithms. Our method identifies those points that are less vulnerable to noisy edges and leverages only these robust points to perform graph-based algorithms. Our experiments with spectral clustering, one of the most representative and widely utilized graph algorithms, reveal that our methodology not only substantially elevates the precision of the algorithm but also greatly accelerates its computational efficiency by leveraging only a select number of robust data points.

We propose a novel algorithm for the support estimation of partially known Gaussian graphical models that incorporates prior information about the underlying graph. In contrast to classical approaches that provide a point estimate based on a maximum likelihood or a maximum a posteriori criterion using (simple) priors on the precision matrix, we consider a prior on the graph and rely on annealed Langevin diffusion to generate samples from the posterior distribution. Since the Langevin sampler requires access to the score function of the underlying graph prior, we use graph neural networks to effectively estimate the score from a graph dataset (either available beforehand or generated from a known distribution). Numerical experiments demonstrate the benefits of our approach.

While Large Language Models (LLMs) have proven to be exceptional on a variety of tasks after alignment, they may still produce responses that contradict the context or world knowledge confidently, a phenomenon known as ``hallucination''. In this paper, we demonstrate that reducing the inconsistency between the external knowledge encapsulated in the training data and the intrinsic knowledge inherited in the pretraining corpus could mitigate hallucination in alignment. Specifically, we introduce a novel knowledge consistent alignment (KCA) approach, which involves automatically formulating examinations based on external knowledge for accessing the comprehension of LLMs. For data encompassing knowledge inconsistency, KCA implements several simple yet efficient strategies for processing. We illustrate the superior performance of the proposed KCA approach in mitigating hallucinations across six benchmarks using LLMs of different backbones and scales. Furthermore, we confirm the correlation between knowledge inconsistency and hallucination, signifying the effectiveness of reducing knowledge inconsistency in alleviating hallucinations. Our code, model weights, and data are public at \url{https://github.com/fanqiwan/KCA}.

Recent advancements in biological research leverage the integration of molecules, proteins, and natural language to enhance drug discovery. However, current models exhibit several limitations, such as the generation of invalid molecular SMILES, underutilization of contextual information, and equal treatment of structured and unstructured knowledge. To address these issues, we propose $\mathbf{BioT5}$, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. $\mathbf{BioT5}$ utilizes SELFIES for $100%$ robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, $\mathbf{BioT5}$ distinguishes between structured and unstructured knowledge, leading to more effective utilization of information. After fine-tuning, BioT5 shows superior performance across a wide range of tasks, demonstrating its strong capability of capturing underlying relations and properties of bio-entities. Our code is available at $\href{https://github.com/QizhiPei/BioT5}{Github}$.

Three robots are growing vegetables on the roof of the University of Melbourne's student pavilion. As I watch, a mechanical arm, hovering above the crop like a fairground claw machine, sprays a carefully measured dose of water over the plants. The greens themselves look fairly terrestrial – cos lettuce, basil, coriander and moth-eaten kale – but they are actually prototypes for a groundbreaking research mission to grow fresh food in outer space. The project leader, Prof Sigfredo Fuentes, leans over and picks a tiny caterpillar from a kale leaf. "We had a real plague of cabbage moths last week, but it's OK; the kale's just here to distract them from the other vegetables." Prof Fuentes is part of the wonderfully named Australian Research Council Centre of Excellence in Plants for Space – a seven-year collaboration between five Australian universities – which has partnered with 38 organisations, including Nasa, to crack the code of fresh, nutritious "space food".

Police took a digital rendering of a suspect's face, generated using DNA evidence, and ran it through a facial recognition system in a troubling incident reported for the first time by WIRED this week. The tactic came to light in a trove of hacked police records published by the transparency collective Distributed Denial of Secrets. Meanwhile, information about United States intelligence agencies purchasing Americans' phone location data and internet metadata without a warrant was revealed this week only after US senator Ron Wyden blocked the appointment of a new NSA director until the information was made public. And a California teen who allegedly used the handle Torswats to carry out hundreds of swatting attacks across the US is being extradited to Florida to face felony charges. The infamous spyware developer NSO Group, creator of the Pegasus spyware, has been quietly planning a comeback, which involves investing millions of dollars lobbying in Washington while exploiting the Israel-Hamas war to stoke global security fears and position its products as a necessity.

EXCLUSIVE: JERUSALEM – Israel's Defense Ministry is taking advantage of its country's vibrant high-tech scene to create an artificial intelligence-driven information platform that will help keep track of the increasingly deteriorating humanitarian situation in the Gaza Strip, even as Israeli troops continue to battle the Iranian-backed Islamist terror group Hamas, Fox News Digital has learned. Commissioned by Israel's Defense Minister Yoav Gallant, the NRTM system, which resembles ChatGPT and other AI platforms, relies on open-source information materials such as reports from international aid organizations, including those affiliated with the United Nations, satellite imagery, news stories and social media posts coming out of Gaza to create a real time picture of living conditions for some two million civilians in the Palestinian enclave. "The idea came from the minister, who has said that Israel's war is against Hamas and not the people of Gaza," Hadar Peretz, a senior adviser at the Ministry of Defense, told Fox News Digital. "The minister wanted to make sure that we were collecting as much data as possible in order to make a full assessment of the situation." Peretz said the goal was for this platform to become an additional tool to enable decision-making for Israeli leaders and for the minister to use in his myriad of meetings with world leaders, as well as with the heads of international organizations working to mitigate the chaos in Gaza and improve conditions.

Israel's war on Gaza, now well into its fourth month, has taken a toll on its own economy with many industries pausing business even as a few continue to get new investments. Since October, Israel's government has subsidised the salaries of reportedly 360,000 mobilised reservists deployed to Gaza – many of whom are high-tech industry workers in finance, artificial intelligence, pharmaceuticals and agriculture. In November, the Bank of Israel put the war's "gross effects" on Israel at 198 billion shekels ( 53bn) and pared back its estimates for economic growth to 2 percent per year for 2023 and 2024, down from 2.3 percent and 2.8 percent. In December, Israel's Finance Ministry said that the war will likely cost Israel approximately 13.8bn this year if its high-intensity phase concludes during the first quarter of 2024. In the midst of that, experts are watching to see how business is doing on the ground.

The emergence of novel the dummy data injection attack (DDIA) poses a severe threat to the secure and stable operation of power systems. These attacks are particularly perilous due to the minimal Euclidean spatial separation between the injected malicious data and legitimate data, rendering their precise detection challenging using conventional distance-based methods. Furthermore, existing research predominantly focuses on various machine learning techniques, often analyzing the temporal data sequences post-attack or relying solely on Euclidean spatial characteristics. Unfortunately, this approach tends to overlook the inherent topological correlations within the non-Euclidean spatial attributes of power grid data, consequently leading to diminished accuracy in attack localization. To address this issue, this study takes a comprehensive approach. Initially, it examines the underlying principles of these new DDIAs on power systems. Here, an intricate mathematical model of the DDIA is designed, accounting for incomplete topological knowledge and alternating current (AC) state estimation from an attacker's perspective. Subsequently, by integrating a priori knowledge of grid topology and considering the temporal correlations within measurement data and the topology-dependent attributes of the power grid, this study introduces temporal and spatial attention matrices. These matrices adaptively capture the spatio-temporal correlations within the attacks. Leveraging gated stacked causal convolution and graph wavelet sparse convolution, the study jointly extracts spatio-temporal DDIA features. Finally, the research proposes a DDIA localization method based on spatio-temporal graph neural networks. The accuracy and effectiveness of the DDIA model are rigorously demonstrated through comprehensive analytical cases.