Large Language Models (LLMs) need to be in accordance with human values-being helpful, harmless, and honest (HHH)-is important for safe deployment. Existing works use Supervised Fine-Tuning (SFT) and Mixture-of-Experts (MoE) to align LLMs. However, these works face challenges in multi-objective settings, such as SFT leading to interference between conflicting objectives, while MoEs suffer from miscalibrated routing. We term this failure mode Axis Collapse, marked by (1) disjoint feature spaces causing catastrophic forgetting, and (2) unreliable inference from misrouted experts. To resolve this, we propose AlignX, a two-stage framework. Stage 1 uses prompt-injected fine-tuning to extract axis-specific task features, mitigating catastrophic forgetting. Stage 2 deploys a MoCaE module that calibrates expert routing using fractal and natural geometry, improving inference reliability. AlignX achieves significant gains on Alpaca (Helpfulness), BeaverTails (Harmlessness), and TruthfulQA (Honesty), with +171.5% win rate, +110.1% in truthfulness-informativeness, and 4.3% fewer safety violations. It also reduces latency and memory usage by over 35% compared to prior MoEs. Results across four
Hear from six pediatricians in measles hot spot Utah, as the US faces infection levels not seen in over three decades and prepares to lose its status as a country that eliminated the dangerous disease
Memory is not merely the storage of data; it is the scaffolding of reality. When biological memory fades, the world does not simply turn black; it regresses into an unrecognizable chaos. Echoes of the Prior is an interactive installation that attempts to visualize this subjective phenomenology of forgetting. By inducing controlled synaptic decay within a Feed-Forward 3D Reconstruction model, we create an artistic analogy for the erosion of the brain's predictive priors. We position the Neural Network not as a tool for engineering, but as a cognitive proxy - a silicon brain whose structural degeneration evokes the disorienting, poetic, and terrifying experience of losing one's grip on the world. Ultimately, we offer this framework as a catalyst, inviting the wider community to explore the uncharted potential of neuromorphic aesthetics in visualizing the fragility of intelligence. Interactive demo see https://decart-4d.github.io/.
Applying a principle of "radical mundanity", this paper examines explanations for the lack of strong evidence for the presence of technology-using extraterrestrial civilizations (ETCs) in the Galaxy - the Fermi paradox. With this principle, the prospect that the Galaxy contains a modest number of civilizations is preferred, where none have achieved technology levels sufficient to accomplish large-scale astro-engineering or lack the desire to do so. This consideration also leads to the expectation that no ETC will colonize a large fraction of the Galaxy, even using robotic probes, and that there are no long-duration high-power beacons. However, there is a reasonable chance that we may make contact on a short, by historical standards, timescale. This event would be momentous, but could still leave us slightly disappointed. Such a Universe would be less terrifying than either of the two possibilities in the quote generally attributed to Arthur C. Clarke on whether we are alone or not. Also, if there is a modest number of ETCs in the Galaxy, that would suggest that there is a large number of planets with some form of life.
Video generation models (VGMs) have demonstrated the capability to synthesize high-quality output. It is important to understand their potential to produce unsafe content, such as violent or terrifying videos. In this work, we provide a comprehensive understanding of unsafe video generation. First, to confirm the possibility that these models could indeed generate unsafe videos, we choose unsafe content generation prompts collected from 4chan and Lexica, and three open-source SOTA VGMs to generate unsafe videos. After filtering out duplicates and poorly generated content, we created an initial set of 2112 unsafe videos from an original pool of 5607 videos. Through clustering and thematic coding analysis of these generated videos, we identify 5 unsafe video categories: Distorted/Weird, Terrifying, Pornographic, Violent/Bloody, and Political. With IRB approval, we then recruit online participants to help label the generated videos. Based on the annotations submitted by 403 participants, we identified 937 unsafe videos from the initial video set. With the labeled information and the corresponding prompts, we created the first dataset of unsafe videos generated by VGMs. We then study p
Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop data-driven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the
Undoubtedly breast cancer identifies itself as one of the most widespread and terrifying cancers across the globe. Millions of women are getting affected each year from it. Breast cancer remains the major one for being the reason of largest number of demise of women. In the recent time of research, Medical Image Computing and Processing has been playing a significant role for detecting and classifying breast cancers from ultrasound images and mammograms, along with the celestial touch of deep neural networks. In this research, we focused mostly on our rigorous implementations and iterative result analysis of different cutting-edge modified versions of EfficientNet architectures namely EfficientNet-V1 (b0-b7) and EfficientNet-V2 (b0-b3) with ultrasound image, named as CEIMVEN. We utilized transfer learning approach here for using the pre-trained models of EfficientNet versions. We activated the hyper-parameter tuning procedures, added fully connected layers, discarded the unprecedented outliers and recorded the accuracy results from our custom modified EfficientNet architectures. Our deep learning model training approach was related to both identifying the cancer affected areas with
Fine-grained emotion classification (FEC) is a challenging task. Specifically, FEC needs to handle subtle nuance between labels, which can be complex and confusing. Most existing models only address text classification problem in the euclidean space, which we believe may not be the optimal solution as labels of close semantic (e.g., afraid and terrified) may not be differentiated in such space, which harms the performance. In this paper, we propose HypEmo, a novel framework that can integrate hyperbolic embeddings to improve the FEC task. First, we learn label embeddings in the hyperbolic space to better capture their hierarchical structure, and then our model projects contextualized representations to the hyperbolic space to compute the distance between samples and labels. Experimental results show that incorporating such distance to weight cross entropy loss substantially improves the performance with significantly higher efficiency. We evaluate our proposed model on two benchmark datasets and found 4.8% relative improvement compared to the previous state of the art with 43.2% fewer parameters and 76.9% less training time. Code is available at https: //github.com/dinobby/HypEmo.
Design/methodology/approach We compared the awareness of 813 people in Wuhan city from January to March 2023 (Wuhan 2023) and 2,973 people in East and South China from February to May 2020 (China 2020) using responses to questionnaires conducted at Japanese local subsidiaries during each period. Purpose As the coronavirus pandemic becomes less terrifying than before, there is a trend in countries around the world to abolish strict behavioral restrictions imposed by governments. How should overseas subsidiaries change the way they manage human resources in response to these system changes? To find an answer to this question, this paper examines what changes occurred in the mindset of employees working at local subsidiaries after the government's strict behavioral restrictions were introduced and lifted during the COVID-19 pandemic. Findings The results showed that the analytical model based on conservation of resources (COR) theory can be applied to both China 2020 and Wuhan 2023. However, the relationship between anxiety, fatigue, compliance, turnover intention, and psychological and social resources of employees working at local subsidiaries changed after the initiation and remova
In the area of information technology, cybersecurity is critical. Information security is one of todays highest priorities. Cyber attacks, which are on the rise and include Ransomware, are the first thing that springs to mind when we think about cybersecurity. To counteract cybercrime, several governments and companies employ a range of strategies. Despite several cybersecurity measures, ransomware continues to terrify people.
Worldwide Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is disrupting in the economy and anxiety of people. The public anxiety has increased the psychological burden on government and healthcare professionals, resulting in a government worker suicide in Japan. The terrified people are asking the government for border measures. However, are border measures possible for this virus? By analyzing 48 almost complete virus genome sequences, we found out that the viruses that invaded Taiwan, the United States, and Japan were introduced independently. We identified thirteen parsimony-informative sites and three groups (CTC, TCC, and TCT). Viruses found outside China did not form a monophyletic clade, opposite to previous study. These results suggest the difficulty of implementing effective border measures against this virus.
Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI has to perform \textit{fine-grained} emotion recognition, distinguishing between many more varied emotions. Here, we focus on improving fine-grained emotion recognition by introducing external knowledge into a pre-trained self-attention model. We propose Knowledge-Embedded Attention (KEA) to use knowledge from emotion lexicons to augment the contextual representations from pre-trained ELECTRA and BERT models. Our results and error analyses outperform previous models on several datasets, and is better able to differentiate closely-confusable emotions, such as afraid and terrified.
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Scientists at Nanyang Technological University in Singapore have discovered a surprisingly simple way to create exotic light structures called optical skyrmions using a 200-year-old optical effect known as the Poisson spot。 Instead of relying on expensive, highly engineered materials, they simply shine a laser at a tiny circular disc, producing sta
A new theoretical study offers a possible explanation for how the Universe can grow more complex without violating the second law of thermodynamics。 Using a quantum gravity framework called Gravity from Entropy, mathematician Ginestra Bianconi found that the Universe’s total entropy may rise as space expands, even while entropy within each unit of
Four nearby white dwarf stars have been discovered hiding in plain sight beside brighter red dwarf companions。 Hubble's ultraviolet observations finally revealed the long-hidden stellar remnants, including one just 25 light-years away that took nearly three decades to confirm。 The findings match long-standing predictions and suggest our corner of t
China’s Chang’e-6 samples have uncovered a surprising difference between the Moon’s two hemispheres。 Solar wind particles penetrated deeper into the far-side soil because Earth’s magnetosphere slows the particles that reach the near side。 Noble gases locked inside the lunar regolith preserved evidence of this uneven bombardment
Scientists say new technologies have reopened the debate over whether Mars could someday be terraformed, turning a once impossible idea into a serious research topic。 Before anyone tries to reshape the Red Planet, though, researchers say we must understand the risks, including what might be lost if Mars already harbors its own forms of life
JWST has captured unusually detailed images of gas feeding the supermassive black hole at the center of NGC 4696。 A vast filament appears to funnel material into an 800-light-year-wide spinning disk, where gas races around at up to 600 kilometers per second。 The findings suggest black holes may recycle their own fuel by heating gas with jets and la