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Home owners told no need for ‘panicking’ amid Sydney property slump
Sydney's falling property prices sign of more to come, real estate agents say Sat 6 Jun 2026 at 8:09am In short: Home values have fallen across Sydney, with buyer confidence low amid interest rate hikes and proposed property tax changes. Real estate agents say buyers have reduced their budgets by up to 15 per cent, and homes are taking longer to sell. Prices are expected to continue to decline, but a market crash remains unlikely.
TLA-Prover: Verifiable TLA+ Specification Synthesis via Preference-Optimized Low-Rank Adaptation
arXiv:2606.06133v1 Announce Type: new Abstract: TLA+ is a formal specification language for verifying distributed systems and safety-critical protocols. Large language models (LLMs) frequently produce TLA+ specifications that fail the TLC model checker for semantic reasons. Across 25 LLMs, the best public baseline is 26.6% syntactic parse and 8.6% semantic model-check.
Livvy Dunne gets emotional after Paul Skenes' incredible gesture to baseball kids, Spurs fan beaten & MEAT
Researchers say there's no better way to get your Tuesday morning started in June than settling in for another edition of Screencaps where we check in with the 1A or 1B best pitcher of this generation, Paul Skenes, to see what he's up to off the field. *Ohtani has to be in the best pitcher discussion. Yes, I'm aware that Shohei is 31.I don't care if Paul and Livvy Dunne pulled off this visit to a local Pittsburgh baseball field as part of some brand campaign or just for some great press,...
Dynamic Query Modification for Binary Locality Sensitive Hashing
arXiv:2605.23807v2 Announce Type: replace Abstract: Our context of interest is how binary locality sensitive hash (LSH) functions can be used to solve the approximate near neighbour (ANN) problem, which seeks to find the k closest elements of some dataset X to some further point q presented as a query. Binary locality sensitive function families H are sets of functions each which accept a point and return a binary value. A function is locality sensitive if and only if the output of the...
'Healthy hair every day' with Home Bargains set reduced from £45 to £8
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Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
arXiv:2410.02628v5 Announce Type: replace Abstract: Learning conditional distributions $\pi^*(\cdot|x)$ is a central problem in machine learning, which is typically approached via supervised methods with paired data $(x,y) \sim \pi^*$. However, acquiring paired data samples is often challenging, especially in problems such as domain translation. This necessitates the development of $\textit{semi-supervised}$ models that utilize both limited paired data and additional unpaired i.i.d. samples...
Effect of introducing viscoelastic polyurethane on the dispersion and vibration isolation efficiency of chiral phononic crystals
arXiv:2606.01272v1 Announce Type: new Abstract: Phononic crystals, a sequence of masses and (damped) springs, are being used more and more in practical applications, exploiting Bragg bandgaps to attenuate vibration transmission in a wide frequency range. In particular, chiral phononic crystals have demonstrated their ability to achieve low frequency bandgaps while maintaining a high static stiffness, and thus load bearing capacities. However, tuning of the bandgap frequencies is non-trivial...
Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
arXiv:2509.06856v2 Announce Type: replace-cross Abstract: We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve and Iterative-Sketching methods for the first time. By iteratively constructing and solving sketched least-squares (LS) subproblems with increasing sketch sizes to achieve better precisions, SLSE-FRS gradually...
Violating the 3rd law of black hole mechanics in vacuum gravity
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Rationality Measurement and Theory for Reinforcement Learning Agents
Announce Type: replace Abstract: This paper proposes a suite of rationality measures and associated theory for reinforcement learning agents, a property increasingly critical yet rarely explored. We define an action in deployment to be perfectly rational if it maximises the hidden true value function in the steepest direction. The expected value discrepancy of a policy's actions against their rational counterparts, culminating over the trajectory in deployment, is defined to be expected...