Publications

Infrequent stimulation of a subject generates event-related potential (ERP) signals masked by background EEG activity. It is generally assumed that this background activity is normal white noise. Another assumption made is that the underlying evoked signals are deterministic and they do not vary from trial to trial. The signal, thus, is modeled as y/sub i//sup (j)/=x/sub i/+/spl sigma//sub i//sup (j)/, where y/sub i//sup (j)/ is the jth trial and z/sub i/ is a white noise. The signal x/sub i/ is recovered by averaging the observations y/sub i//sup (j)/. In reality, the background activity is "colored" and not always Gaussian. The time samples of the background activity are generally correlated. In addition, the signal x/sub i/ varies across observations. The purpose of this study is to extract single trial ERPs from the EEG. The authors are interested in the trial to trial variation of the ERPs and their clinical applicability. They have investigated the wavelet soft-thresholding method to remove the background noise and separate out the single trial response. The non-white background activity, after wavelet transformation, may concentrate in certain resolution levels. The authors determined these levels by testing the Gaussianity of the wavelet coefficients, using both the /spl chi//sup 2/ and Kolmogorov-Smirnov goodness of fit tests. In the resolution levels at which the null hypothesis was not rejected, the noise level was estimated. The de-noising threshold was then calculated using a level dependent rule T/sub j,N/=/spl radic/(2 log(N))/spl middot/MAD(C/sub j,k/)/0.6745, where C/sub j,k/ are the wavelet coefficients and MAD(C/sub j,k/)=Median(|C/sub j,k|/) is an estimator of the noise level. The resolution levels at which the null hypothesis was rejected were not thresholded.

AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage that testing alone cannot provide. Distributed systems are a prime example: properties such as consistency between reads and writes must hold under every possible interleaving of events. Mechanized formal verification can guarantee such correctness, but typically demands months to years of expert effort. As evidence, even SOTA coding agents (Codex with GPT-5.4 and Claude Code with Opus 4.6) succeed on only 2/7 distributed key-value-store specifications. In this paper, we present the first effective approach to addressing this gap, Inductive Deductive Synthesis (IDS), which jointly and incrementally synthesizes implementation and proof, and learns from failed attempts to systematically try promising strategies. Built as an agentic LLM system, IDS achieves 7/7 in about 6.8 hours and $106 per spec on average, roughly 200x faster than expert effort and 17% cheaper than SOTA agents. IDS further incorporates performance feedback into the same loop, yielding implementations up to 3x faster than published verified systems.

Using scanning tunneling microscopy, we show the phase transition between new structures of NO on Rh(111) in equilibrium with the gas phase near 300 K, in the Torr pressure range. Two phases with (2 x 2) and (3 x 3) periodicity transform into each other as the pressure and temperature change around the equilibrium P-T line. By measuring P and T at coexistence, we determined the heat of adsorption in the (3 x 3) structure. From the phase boundary dynamics, the activation energy barrier between phases were estimated. The results demonstrate that unique information can be obtained from high-pressure and high-temperature studies.

Postpolymerization functionalization of polymers can create new applications for existing materials, while retaining their most favorable, intrinsic properties. Polyvinyl chloride (PVC) is a widely used, commodity polymer that is particularly challenging to modify. We report a copper-catalyzed protocol that replaces a small fraction of the C-Cl bonds in PVC with C-B bonds to boronic esters. The reaction occurs with an inexpensive catalyst comprising copper(II) chloride and an NHC ligand derived from a common ionic liquid that is distinct from the N-heterocyclic carbene (NHC) used for the borylation of small alkyl halides. The resulting materials adhere strongly to common surfaces, such as glass and metals, even more strongly than do commercial glues.

Abstract Invited for the cover of this issue are Elad Gross, F. Dean Toste, and co‐workers at The Hebrew University and UC Berkeley. The image depicts the flexible anchoring geometry of addressable carbene molecules on Au surface, which upon exposure to reducing conditions changed their orientation from a standing into a flat‐lying position. Read the full text of the article at 10.1002/chem.201903434 .

A fundamental chemoselectivity challenge that remains intrinsically unsolved in aldol-type reactions is the suppression of self-aldol reactions with enolizable aldehydes in reactions such as cross-aldol processes. Contrasting with the usual practice of using large excesses of one component to compete with the undesired self-aldehyde condensation reactions, we have developed an enzyme-like polymer catalyst consisting of a hyperbranched polyethyleneimine derivative and proline that can eliminate the self-aldol reactions by suppressing an irreversible aldol condensation pathway. Control experiments and preliminary mechanistic studies suggest that the polymer catalyst provides an optimum environment for the aldol reaction to proceed selectively in water, and the catalytic conditions provided by the polymer are difficult to duplicate with typical small molecule analogues. This polymer catalyst system or its modified version has potential applications in developing a new or more efficient synthesis, as demonstrated in a dynamic catalytic process for the preparation of alpha,beta-unsaturated ketones using cross ketone/aldehyde reactions without the need for excess substrates.

Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint. In this paper we repurpose powerful learning machinery, originally developed for object classification, to discover image cues relevant for recovering the 3D shape of potentially unfamiliar objects. We cast the problem as one of local prediction of surface normals and global detection of 3D reflection symmetry planes, which open the door for extrapolating occluded surfaces from visible ones. We demonstrate that our method is able to recover accurate 3D shape information for classes of objects it was not trained on, in both synthetic and real images.

High stiffness and strength carbon fibres are commonly used to reinforce epoxy-resin composites. While wild Antheraea pernyi silk fibres exhibit high toughness originating from their α-helix/random coil conformation structures and their micro-fibre morphology, their insufficient strength and stiffness hinders them from being used in similar structural composites. In this work, we use interply hybridization of silk and carbon fibres to reinforce epoxy-matrix composites. With increased carbon fibre content, the quasi-static tensile/flexural stiffness and strength increases following the rule of mixtures while more silk fibre acts to increase ductility and impact strength. This results in a composite comprising equal volumes of carbon and silk fibres achieving an impact strength of 98 kJ m −2 , which is twice that of purely carbon-fibre reinforced composites (44 kJ m −2 ). This work shows tough natural silk fibres and strong synthetic fibres can be successfully integrated into epoxy-resin composites for tailored mechanical properties.

The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are widely used, but lead to multiple optima and are hard to interpret (appear noise-like) for complex neurons. Image-based methods use maximally-activating image regions which are easier to interpret, but do not provide pixel-level insight into why the neuron responds to them. In this work we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM), which is designed for diagnostic visualization. LDAM provides two affordances in combination: the ability to explore the set of maximally activating pre-images, and the ability to trade-off interpretability and pixel-level accuracy using a GAN-style discriminator as a regularizer. We present case studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs. Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.

Mechancial surface treatments are known to be effective at improving the fatigue resistance of metallic alloys at elevated temperatures (~;550-600 aC), even though the near-surface compressive residual stress fields have been annealed out. We have investigated the thermal stability of near-surface microstructures induced by deep rolling and laser-shock peening in an austentic stainless steel (AISI 304) and a titanium alloy (Ti-6Al-4V) using in situ hot-stage transmission electron microscopy. It is found that the improvements in fatigue resistance at elevated temperature are related to the high-temperature stability of the work-hardened near-surface microstructure in each case.