Text-to-Image (T2I) Synthesis has made tremendous strides in enhancing synthesized image quality, but current datasets evaluate model performance only on descriptive, instruction-based prompts. Real-world news image captions take a more pragmatic approach, providing high-level situational and Named-Entity (NE) information and limited physical object descriptions, making them abstractive. To evaluate the ability of T2I models to capture intended subjects from news captions, we introduce the Abstractive News Captions with High-level cOntext Representation (ANCHOR) dataset, containing 70K+ samples sourced from 5 different news media organizations. With Large Language Models (LLM) achieving success in language and commonsense reasoning tasks, we explore the ability of different LLMs to identify and understand key subjects from abstractive captions. Our proposed method Subject-Aware Finetuning (SAFE), selects and enhances the representation of key subjects in synthesized images by leveraging LLM-generated subject weights. It also adapts to the domain distribution of news images and captions through custom Domain Fine-tuning, outperforming current T2I baselines on ANCHOR. By launching the ANCHOR dataset, we hope to motivate research in furthering the Natural Language Understanding (NLU) capabilities of T2I models.

Large language models (LLMs) show excellent performance in difficult tasks, but they often require massive memories and computational resources. How to reduce the parameter scale of LLMs has become research hotspots. In this study, we make an important observation that the multi-head self-attention (MHA) sub-layer of Transformer exhibits noticeable low-rank structure, while the feed-forward network (FFN) sub-layer does not. With this regard, we design a mixed compression model, which organically combines Low-Rank matrix approximation And structured Pruning (LoRAP). For the MHA sub-layer, we propose an input activation weighted singular value decomposition method to strengthen the low-rank characteristic. Furthermore, we discover that the weight matrices in MHA sub-layer have different low-rank degrees. Thus, a novel parameter allocation scheme according to the discrepancy of low-rank degrees is devised. For the FFN sub-layer, we propose a gradient-free structured channel pruning method. During the pruning, we get an interesting finding that the least important 1% of parameter actually play a vital role in model performance. Extensive evaluations on zero-shot perplexity and zero-shot task classification indicate that our proposal is superior to previous structured compression rivals under multiple compression ratios.

This study advanced tele-operations in Advanced Air Mobility (AAM) through the creation of a Vehicle Digital Twin (VDT) system for eVTOL aircraft, tailored to enhance remote control safety and efficiency, especially for Beyond Visual Line of Sight (BVLOS) operations. By synergizing digital twin technology with immersive Virtual Reality (VR) interfaces, we notably elevate situational awareness and control precision for remote operators. Our VDT framework integrates immersive tele-operation with a high-fidelity aerodynamic database, essential for authentically simulating flight dynamics and control tactics. At the heart of our methodology lies an eVTOL's high-fidelity digital replica, placed within a simulated reality that accurately reflects physical laws, enabling operators to manage the aircraft via a master-slave dynamic, substantially outperforming traditional 2D interfaces. The architecture of the designed system ensures seamless interaction between the operator, the digital twin, and the actual aircraft, facilitating exact, instantaneous feedback. Experimental assessments, involving propulsion data gathering, simulation database fidelity verification, and tele-operation testing, verify the system's capability in precise control command transmission and maintaining the digital-physical eVTOL synchronization. Our findings underscore the VDT system's potential in augmenting AAM efficiency and safety, paving the way for broader digital twin application in autonomous aerial vehicles.

Designing tests to evaluate if a given autonomous system satisfies complex specifications is challenging due to the complexity of these systems. This work proposes a flow-based approach for reactive test synthesis from temporal logic specifications, enabling the synthesis of test environments consisting of static and reactive obstacles and dynamic test agents. The temporal logic specifications describe desired test behavior, including system requirements as well as a test objective that is not revealed to the system. The synthesized test strategy places restrictions on system actions in reaction to the system state. The tests are minimally restrictive and accomplish the test objective while ensuring realizability of the system's objective without aiding it (semi-cooperative setting). Automata theory and flow networks are leveraged to formulate a mixed-integer linear program (MILP) to synthesize the test strategy. For a dynamic test agent, the agent strategy is synthesized for a GR(1) specification constructed from the solution of the MILP. If the specification is unrealizable by the dynamics of the test agent, a counterexample-guided approach is used to resolve the MILP until a strategy is found. This flow-based, reactive test synthesis is conducted offline and is agnostic to the system controller. Finally, the resulting test strategy is demonstrated in simulation and experimentally on a pair of quadrupedal robots for a variety of specifications.

The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been made to correct this issue through human annotators. However, hiring and managing human annotators is expensive and time-consuming. As an alternative, recent studies are exploring the use of large language models (LLMs) for data annotation. In this study, we present a case study that extends the application of LLM-based data annotation to enhance the quality of existing datasets through a cleansing strategy. Specifically, we leverage approaches such as chain-of-thought (CoT) and majority voting to imitate human annotation and classify unrelated documents from the Multi-News dataset, which is widely used for the multi-document summarization task. Through our proposed cleansing method, we introduce an enhanced Multi-News+. By employing LLMs for data cleansing, we demonstrate an efficient and effective approach to improving dataset quality without relying on expensive human annotation efforts.

The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial attacks is one of the major issues. In adversarial machine learning, malicious users try to fool the deep learning model by inserting adversarial perturbation inputs into the model during its training or testing phase. Subsequently, it reduces the model confidence score and results in incorrect classifications. The novel key contribution of the research is to empirically test the black-box adversarial transferability phenomena in cyber attack detection systems. It indicates that the adversarial perturbation input generated through the surrogate model has a similar impact on the target model in producing the incorrect classification. To empirically validate this phenomenon, surrogate and target models are used. The adversarial perturbation inputs are generated based on the surrogate-model for which the hacker has complete information. Based on these adversarial perturbation inputs, both surrogate and target models are evaluated during the inference phase. We have done extensive experimentation over the CICDDoS-2019 dataset, and the results are classified in terms of various performance metrics like accuracy, precision, recall, and f1-score. The findings indicate that any deep learning model is highly susceptible to adversarial attacks, even if the attacker does not have access to the internal details of the target model. The results also indicate that white-box adversarial attacks have a severe impact compared to black-box adversarial attacks. There is a need to investigate and explore adversarial defence techniques to increase the robustness of the deep learning models against adversarial attacks.

Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data. However, current simulation-based amortized inference methods are simulation-hungry and inflexible: They require the specification of a fixed parametric prior, simulator, and inference tasks ahead of time. Here, we present a new amortized inference method -- the Simformer -- which overcomes these limitations. By training a probabilistic diffusion model with transformer architectures, the Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks and is substantially more flexible: It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data, including both posterior and likelihood. We showcase the performance and flexibility of the Simformer on simulators from ecology, epidemiology, and neuroscience, and demonstrate that it opens up new possibilities and application domains for amortized Bayesian inference on simulation-based models.

This year, the U.S. military carried out strikes against Iranian forces and allied militias in Syria and Iraq in response to a drone attack in Jordan that killed three American soldiers. And in 2020, the United States killed a top Iranian commander, Maj. Prime Minister Rishi Sunak confirmed that Britain had helped defend Israel against the Iranian attack, saying that Britain's air force had shot down "a number of Iranian attack drones" and would now work with allies to de-escalate tensions. "What we now need is for calm heads to prevail," Mr. Sunak told the BBC on Sunday. Rear Adm. Daniel Hagari, Israel's chief military spokesman, said that Israel had intercepted most of the 200 drones, cruise missiles and ballistic missiles launched by Iran with "some assistance" from its allies.

Iran launched a large aerial attack on Israel and the territory it controls starting late on Saturday, firing at least 300 drones and missiles. It is the first such direct attack launched from Iranian territory after decades of shadow warfare between the two countries. The assault was in response to a recent strike on a building in the Iranian Embassy complex in Syria that killed several of Iran's top commanders. Here's a look at what we know about the Iranian attack this weekend and its implications: Air raid sirens sounded in Israel and the West Bank overnight, signaling the start of an attack that had been anticipated for days. In the event, almost all of the missiles and drones were intercepted, the Israeli military said on Sunday.

Washington, DC – The response from US President Joe Biden's administration to Iran's historic missile and drone attack on Israel has been two-fold: Washington has re-upped its pledge to always stand by its "ironclad" ally Israel, while also appealing to the government of Benjamin Netanyahu not to take further action that could drag the region into wider war. The days ahead will show if those two options are compatible, or if the two governments' priorities are on collision course, analysts told Al Jazeera. In the short term, the Iranian attack is a coup for both Israel and its backers in the US: From their perspective, it offers renewed justification for military support to Israel while weakening the world's focus on alleged abuses committed in Gaza in seven months of war, according to Trita Parsi, the executive vice president of the Washington-based Quincy Institute. But defiance from Netanyahu to US calls for restraint could find the Biden administration further hamstrung by its political and ideological commitments to Israel, which could eventually drag Washington into a wider war, he added. "The Israelis have been told by Biden to take this as a win and stop here," Parsi told Al Jazeera.