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Post-Newtonian theory is considered a reliable effective expansion of General Relativity in the weak-field and slow-motion limit. We argue that such a belief is misplaced. In generic many-body relativistic dynamics, the absence of globally conserved charges in the region of interest and non-integrability can drive strong sensitivity to angular-momentum exchange across inhomogeneous curvature, invalidating naive power counting in an effective theory expansion. Building on general lessons from effective field theory, we derive an explicit breakdown criterion that delineates when post-Newtonian truncations become unreliable despite small local potentials and velocities. This supplies a controlled systematic for weak-field mass inference, relevant to the dark matter puzzle in astrophysics and cosmology.
The MEVIR 2 framework innovates and improves how we understand trust decisions in our polarized information landscape. Unlike classical models assuming ideal rationality, MEVIR 2 recognizes that human trust emerges from three interacting foundations: how we process evidence procedurally, our character as epistemic agents virtue theory, and our moral intuitions shaped by both evolutionary cooperation MAC model and cultural values Extended Moral Foundations Theory. This explains why different people find different authorities, facts, and tradeoffs compelling. MEVIR 2's key innovation introduces "Truth Tribes" TTs-stable communities sharing aligned procedural, virtue, and moral epistemic profiles. These arent mere ideological groups but emergent clusters with internally coherent "trust lattices" that remain mutually unintelligible across tribal boundaries. The framework incorporates distinctions between Truth Bearers and Truth Makers, showing disagreements often stem from fundamentally different views about what aspects of reality can make propositions true. Case studies on vaccination mandates and climate policy demonstrate how different moral configurations lead people to select dif
We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf{B}$ig $\textbf{A}$daptive $\textbf{S}$treamable TTS with $\textbf{E}$mergent abilities. BASE TTS is the largest TTS model to-date, trained on 100K hours of public domain speech data, achieving a new state-of-the-art in speech naturalness. It deploys a 1-billion-parameter autoregressive Transformer that converts raw texts into discrete codes ("speechcodes") followed by a convolution-based decoder which converts these speechcodes into waveforms in an incremental, streamable manner. Further, our speechcodes are built using a novel speech tokenization technique that features speaker ID disentanglement and compression with byte-pair encoding. Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences. We design and share a specialized dataset to measure these emergent abilities for text-to-speech. We showcase state-of-the-art naturalness of BASE TTS by evaluating against baselines that include publicly avai
We present a scalable method to produce high quality emphasis for text-to-speech (TTS) that does not require recordings or annotations. Many TTS models include a phoneme duration model. A simple but effective method to achieve emphasized speech consists in increasing the predicted duration of the emphasised word. We show that this is significantly better than spectrogram modification techniques improving naturalness by $7.3\%$ and correct testers' identification of the emphasized word in a sentence by $40\%$ on a reference female en-US voice. We show that this technique significantly closes the gap to methods that require explicit recordings. The method proved to be scalable and preferred in all four languages tested (English, Spanish, Italian, German), for different voices and multiple speaking styles.
Accurately segmenting fluid in 3D optical coherence tomography (OCT) images is critical for detecting eye diseases but remains challenging. Traditional autoencoder-based methods struggle with resolution loss and information recovery. While transformer-based models improve segmentation, they arent optimized for 3D OCT volumes, which vary by vendor and extraction technique. To address this, we propose SwinVFTR, a transformer architecture for precise fluid segmentation in 3D OCT images. SwinVFTR employs channel-wise volumetric sampling and a shifted window transformer block to improve fluid localization. Moreover, a novel volumetric attention block enhances spatial and depth-wise attention. Trained using multi-class dice loss, SwinVFTR outperforms existing models on Spectralis, Cirrus, and Topcon OCT datasets, achieving mean dice scores of 0.72, 0.59, and 0.68, respectively, along with superior performance in mean intersection-over-union (IOU) and structural similarity (SSIM) metrics.
In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers. We do this by activating distinct parts of the network for different tasks. We train our model using a novel approach to two-stage training. In Stage I, the model learns speaker-independent word-level prosody representations from speech which it uses for many-to-many fine-grained prosody transfer. In Stage II, we learn to predict these prosody representations using the contextual information available in text, thereby, enabling multi-speaker TTS with contextually appropriate prosody. We compare CC2 to two strong baselines, one in TTS with contextually appropriate prosody, and one in fine-grained prosody transfer. CC2 reduces the gap in naturalness between our baseline and copy-synthesised speech by $22.79\%$. In fine-grained prosody transfer evaluations, it obtains a relative improvement of $33.15\%$ in target speaker similarity.
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data. Our goal is to increase diversity of text conditionings available during training. This helps to reduce overfitting, especially in low-resource settings. Our method relies on substituting text and audio fragments in a way that preserves syntactical correctness. We take additional measures to ensure that synthesized speech does not contain artifacts caused by combining inconsistent audio samples. The perceptual evaluations show that our method improves speech quality over a number of datasets, speakers, and TTS architectures. We also demonstrate that it greatly improves robustness of attention-based TTS models.
A catastrophic asteroid breakup may have triggered a huge wave of impacts across the inner solar system about 800 million years ago。 The debris was launched from near a gravitational gateway controlled by Jupiter, sending fragments toward Earth, the Moon, and Mars。 The bombardment may explain ancient lunar craters and could have contributed to majo
Researchers have created cosmic dust from scratch by recreating space-like conditions inside glass tubes。 The dust contains complex carbon-rich molecules built from elements essential to life and produces infrared signals similar to real material found in space。 By studying these laboratory samples, scientists can explore how organic chemistry unfo
A team of mathematicians used whimsical "silly sprinklers" to solve a physics mystery that has puzzled scientists for decades。 Their experiments showed that the rotation of both normal and reverse sprinklers is driven by the momentum of flowing water, not by the outside water flow or other long-standing theories。 The results finally provide a clear
Researchers discovered a proton-assisted mechanism that greatly improves how triplet energy moves between quantum dots and nearby molecules。 The proton briefly shifts position, helps coordinate electron movement, and then returns to where it started。 This quantum-driven shuttle could offer a powerful new way to tune solar cells, lasers, and catalyt
California and XPRIZE competition tests whether drones can stop wildfires early
A new AI-powered blood test could give people a remarkably early warning of serious heart and circulation problems。 Developed by researchers at the University of Hong Kong, CardiOmicScore analyzes thousands of proteins and metabolites to estimate the risk of six major cardiovascular diseases, including heart attack, stroke, heart failure, and atria
Ultra-fine bubbles may offer a cleaner way to perfect inkjet printing for next-generation electronics。 By simply changing the number of bubbles in each droplet, researchers were able to dramatically reshape the final printed pattern without leaving behind unwanted chemical residues
Scientists have developed a new framework that could finally apply the laws of thermodynamics to real, ever-changing black holes instead of only perfectly stable ones。 The advance may improve our understanding of black hole mergers, evaporation, and the powerful gravitational wave events detected by observatories like LIGO
Researchers solved the mystery of how soft lithium dendrites crack the hard ceramic inside solid-state batteries, triggering short circuits。 The breakthrough could help engineers build safer, longer-lasting batteries for smartphones, electric vehicles, and other electronics
Scientists have created a programmable optical chip that can slow light on demand, giving engineers far greater control over how optical signals propagate through a circuit。 The technology could provide the delays, synchronization, and buffering functions needed to make light-based computing more practical。 A single chip could eventually perform se
A new particle detector called PLATON could replace millions of tiny detector components with a single block of light-producing material。 Using a light-field camera, highly sensitive photon sensors, and AI, it reconstructs particle paths in fast, detailed 3D。 Simulations suggest it could match or surpass today’s best detectors while being far easie