Science Chess isn't fair--so rearrange the pieces A new study suggests the standard chessboard setup needs restructuring. Breakthroughs, discoveries, and DIY tips sent every weekday. The arrangement of the 32 pieces on a standard chess board has remained the same for centuries, but a forthcoming study suggests an overhaul is in order. Based on recent statistical calculations, the fairest and most balanced setup for both players can be found among the 960 possible starting positions popularized by former world champion Bobby Fischer over 30 years ago. The standard rules of chess grant most people a lifetime of dynamic and challenging matches, but that's not always the case for the world's best players.

The storm, which is set to make landfall in Jamaica Tuesday, has stunned meteorologists with its intensity and the speed at which it built. Meteorologists who have spent the past few days monitoring the rapid development of Hurricane Melissa in the Atlantic Ocean are sounding the alarm about the storm, which is set to make landfall in Jamaica today as a Category 5 hurricane. The sustained--and growing--intensity of the storm is remarkable, experts say, and has the makings of a historic hurricane. "When I look at the cloud pattern, I will tell you as a meteorologist and professional--and a person--it is beautiful, but it is terrifying," says Sean Sublette, a meteorologist based in Virginia. "I know what is underneath those clouds."

This might unnecessarily alienate the deep learning crowd, given that it has so often been emphasized that all types of modules are suitable for embedding in deep learning architectures, and bits and pieces of computer vision architectures have in fact been used (e.g.

Multimodal representation learning seeks to relate and decompose information inherent in multiple modalities. By disentangling modality-specific information from information that is shared across modalities, we can improve interpretability and robustness and enable downstream tasks such as the generation of counterfactual outcomes. Separating the two types of information is challenging since they are often deeply entangled in many real-world applications. We present a comprehensive analysis of the optimality of each disentangled representation, particularly focusing on the scenario not covered in prior work where the so-called Minimum Necessary Information (MNI) point is not attainable. SSL successfully learns shared and modality-specific features on multiple synthetic and real-world datasets and consistently outperforms baselines on various downstream tasks, including prediction tasks for vision-language data, as well as molecule-phenotype retrieval tasks for biological data. Humans understand and interact with the world using multiple senses, each providing unique and complementary information essential for forming a comprehensive mental representation of the environment. Large multimodal representation learning models such as CLIP (Radford et al., 2021), trained through self-supervised learning, maximally capture the mutual information shared across multiple modalities, exploiting the assumption of multi-view redundancy (Tosh et al., 2021; Sridharan & Kakade, 2008). This property indicates that shared information between modalities is exactly what is relevant for downstream tasks. However, the modality gap, rooted in the inherent differences in representational nature and information content across modalities (Liang et al., 2022b; Ramasinghe et al., 2024; Huh et al., 2024), leads to the misalignment between modalities and restricts the application of these methods in many real-world multimodal scenarios.

When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial tool. Luckily, there exist many methods for computing such CIs and new promising approaches are continuously being proposed. Typically, these methods combine various resampling procedures, most popular among them cross-validation and bootstrapping, with different variance estimation techniques. Unfortunately, however, there is currently no consensus on when any of these combinations may be most reliably employed and how they generally compare. In this work, we conduct the first large-scale study comparing CIs for the generalization error - empirically evaluating 13 different methods on a total of 18 tabular regression and classification problems, using four different inducers and a total of eight loss functions. We give an overview of the methodological foundations and inherent challenges of constructing CIs for the generalization error and provide a concise review of all 13 methods in a unified framework. Finally, the CI methods are evaluated in terms of their relative coverage frequency, width, and runtime. Based on these findings, we are able to identify a subset of methods that we would recommend. We also publish the datasets as a benchmarking suite on OpenML and our code on GitHub to serve as a basis for further studies.

MRI-targeted biopsy has shown significant advantages over conventional random sextant biopsy, detecting more clinically significant cancers and improving risk stratification. However, needle targeting accuracy, especially in transperineal MRI-guided biopsies, presents a challenge due to needle deflection. This can negatively impact patient outcomes, leading to repeated sampling and inaccurate diagnoses if cancerous tissue isn't properly collected. To address this, we developed a novel differential drive prototype designed to improve needle control and targeting precision. This system, featuring a 2-degree-of-freedom (2-DOF) MRI-compatible cooperative needle driver, distances the robot from the MRI imaging area, minimizing image artifacts and distortions. By using two motors for simultaneous needle insertion and rotation without relative movement, the design reduces MRI interference. In this work, we introduced two mechanical differential drive designs: the ball screw/spline and lead screw/bushing types, and explored both hollow-type and side-pulley differentials. Validation through low-resolution rapid-prototyping demonstrated the feasibility of differential drives in prostate biopsies, with the custom hollow-type hybrid ultrasonic motor (USM) achieving a rotary speed of 75 rpm. The side-pulley differential further increased the speed to 168 rpm, ideal for needle rotation applications. Accuracy assessments showed minimal errors in both insertion and rotation motions, indicating that this proof-of-concept design holds great promise for further development. Ultimately, the differential drive offers a promising solution to the critical issue of needle targeting accuracy in MRI-guided prostate biopsies.

Piezoelectric ultrasonic motors perform the advantages of compact design, faster reaction time, and simpler setup compared to other motion units such as pneumatic and hydraulic motors, especially its non-ferromagnetic property makes it a perfect match in MRI-compatible robotics systems compared to traditional DC motors. Hollow shaft motors address the advantages of being lightweight and comparable to solid shafts of the same diameter, low rotational inertia, high tolerance to rotational imbalance due to low weight, and tolerance to high temperature due to low specific mass. This article presents a prototype of a hollow cylindrical ultrasonic motor (HCM) to perform direct drive, eliminate mechanical non-linearity, and reduce the size and complexity of the actuator or end effector assembly. Two equivalent HCMs are presented in this work, and under 50g prepressure on the rotor, it performed 383.3333rpm rotation speed and 57.3504mNm torque output when applying 282$V_{pp}$ driving voltage.

Precise surgical procedures may benefit from intra-operative image guidance using magnetic resonance imaging (MRI). However, the MRI's strong magnetic fields, fast switching gradients, and constrained space pose the need for an MR-guided robotic system to assist the surgeon. Piezoelectric actuators can be used in an MRI environment by utilizing the inverse piezoelectric effect for different application purposes. Piezoelectric ultrasonic motor (USM) is one type of MRI-compatible actuator that can actuate these robots with fast response times, compactness, and simple configuration. Although the piezoelectric motors are mostly made of nonferromagnetic material, the generation of eddy currents due to the MRI's gradient fields can lead to magnetic field distortions causing image artifacts. Motor vibrations due to interactions between the MRI's magnetic fields and those generated by the eddy currents can further degrade image quality by causing image artifacts. In this work, a plastic piezoelectric ultrasonic (USM) motor with more degree of MRI compatibility was developed and induced with preliminary optimization. Multiple parameters, namely teeth number, notch size, edge bevel or straight, and surface finish level parameters were used versus the prepressure for the experiment, and the results suggested that using 48 teeth, thin teeth notch with 0.39mm, beveled edge and a surface finish using grit number of approximate 1000 sandpaper performed a better output both in rotary speed and torque. Under this combination, the highest speed reached up to 436.6665rpm when the prepressure was low, and the highest torque reached up to 0.0348Nm when the prepressure was approximately 500g.

Intra-operative image guidance using magnetic resonance imaging (MRI) can significantly enhance the precision of surgical procedures, such as deep brain tumor ablation. However, the powerful magnetic fields and limited space within an MRI scanner require the use of robotic devices to aid surgeons. Piezoelectric motors are commonly utilized to drive these robots, with piezoelectric ultrasonic motors being particularly notable. These motors consist of a piezoelectric ring stator that is bonded to a rotor through frictional coupling. When the stator is excited at specific frequencies, it generates distinctive mode shapes with surface waves that exhibit both in-plane and out-of-plane displacement, leading to the rotation of the rotor. In this study, we continue our previous work and refine the motor design and performance, we combine finite element modeling (FEM) with stroboscopic and time-averaged digital holography to validate a further plastic-based ultrasonic motor with better rotary performance.

The task of depth completion aims at recovering a dense depth map from a sparse depth map using additional inputs such as camera images as guidance (cf. Figure 2). The task is especially important in the context of autonomous vehicles (AVs), where sparse depth maps are produced by lidar sensors but dense depth maps are required by some employed perception algorithms. For example, the Velodyne HDL-64E lidar sensor used by the popular KITTI [6] dataset fills up only 6% of the depth values of a corresponding color image, when projected onto the image. In addition to infilling and interpolating the depth values of the remaining 94% pixels, a proper depth completion solution needs to be able to deal with errors caused by the different mounting positions of the camera and lidar sensor, moving objects and the spinning movement of the lidar sensor itself. Figure 2 illustrates the inputs (color image, sparse depth) and the output (dense depth) of the depth completion task. Notice how there are occlusions (image regions with missing points) and overlaps (image regions with points from different depths) in the image, since the lidar and the camera have slightly different viewpoints. Most state-of-the-art depth completion approaches rely on a U-Net [21] style backbone followed by a CSPN-based [2] refinement network [8, 14, 19].