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In this work, we draw connections between the classical Shannon interpolation of bandlimited deterministic signals and the literature on estimating continuous-time random processes from their samples (known in various communities under different names, such as Wiener-Kolmogorov filtering, Gaussian process regression, and kriging). This leads to the realization that Shannon interpolation can be interpreted as implicitly expecting that the unknown signal has uniform spectral density within its bandwidth. However, in many practical applications, we often expect more energy at some frequencies than at others. This leads us to propose novel Shannon-like interpolators that are optimal with respect to appropriately-constructed weighted Hilbert spaces, where weighting enables us to accommodate prior information about nonuniform spectral density. Although our new interpolants are derived for data obtained with any sampling rate, we observe that they are particularly useful for interpolating sub-Nyquist data. In addition to theory, we also discuss aspects of practical implementation and show illustrative examples to demonstrate the potential benefits of the approach.

While visuomotor policy learning has advanced robotic manipulation, precisely executing contact-rich tasks remains challenging due to the limitations of vision in reasoning about physical interactions. To address this, recent work has sought to integrate tactile sensing into policy learning. However, many existing approaches rely on optical tactile sensors that are either restricted to recognition tasks or require complex dimensionality reduction steps for policy learning. In this work, we explore learning policies with magnetic skin sensors, which are inherently low-dimensional, highly sensitive, and inexpensive to integrate with robotic platforms. To leverage these sensors effectively, we present the Visuo-Skin (ViSk) framework, a simple approach that uses a transformer-based policy and treats skin sensor data as additional tokens alongside visual information. Evaluated on four complex real-world tasks involving credit card swiping, plug insertion, USB insertion, and bookshelf retrieval, ViSk significantly outperforms both vision-only and optical tactile sensing based policies. Further analysis reveals that combining tactile and visual modalities enhances policy performance and spatial generalization, achieving an average improvement of 27.5% across tasks. https://visuoskin.github.io/

Understanding the complex interactions within the microbiome is crucial for developing effective diagnostic and therapeutic strategies. Traditional machine learning models often lack interpretability, which is essential for clinical and biological insights. This paper explores the application of symbolic regression (SR) to microbiome relative abundance data, with a focus on colorectal cancer (CRC). SR, known for its high interpretability, is compared against traditional machine learning models, e.g., random forest, gradient boosting decision trees. These models are evaluated based on performance metrics such as F1 score and accuracy. We utilize 71 studies encompassing, from various cohorts, over 10,000 samples across 749 species features. Our results indicate that SR not only competes reasonably well in terms of predictive performance, but also excels in model interpretability. SR provides explicit mathematical expressions that offer insights into the biological relationships within the microbiome, a crucial advantage for clinical and biological interpretation. Our experiments also show that SR can help understand complex models like XGBoost via knowledge distillation. To aid in reproducibility and further research, we have made the code openly available at https://github.com/swag2198/microbiome-symbolic-regression ., Comment: 5 pages, 2 figures

The ergodicity postulate, a foundational pillar of Gibbsian statistical mechanics predicts that a periodically driven (Floquet) system in the absence of any conservation law heats to a featureless `infinite temperature' state. Here, we find--for a clean and interacting generic spin chain subject to a {\it strong} driving field--that this can be prevented by the emergence of {\it approximate but stable} conservation-laws not present in the undriven system. We identify their origin: they do not necessarily owe their stability to familiar protections by symmetry, topology, disorder, or even high energy costs. We show numerically, {\it in the thermodynamic limit,} that when required by these emergent conservation-laws, the entanglement-entropy density of an infinite subsystem remains zero over our entire simulation time of several decades in natural units. We further provide a recipe for designing such conservation laws with high accuracy. Finally, we present an ensemble description, which we call the strongly driven ensemble incorporating these constraints. This provides a way to control many-body chaos through stable Floquet-engineering. Strong signatures of these conservation-laws should be experimentally accessible since they manifest in all length and time scales. Variants of the spin model we have used, have already been realized using Rydberg-dressed atoms.

Recent works have shown theoretically and empirically that redundant data dimensions are a source of adversarial vulnerability. However, the inverse doesn't seem to hold in practice; employing dimension-reduction techniques doesn't exhibit robustness as expected. In this work, we consider classification tasks and characterize the data distribution as a low-dimensional manifold, with high/low variance features defining the on/off manifold direction. We argue that clean training experiences poor convergence in the off-manifold direction caused by the ill-conditioning in widely used first-order optimizers like gradient descent. The poor convergence then acts as a source of adversarial vulnerability when the dataset is inseparable in the on-manifold direction. We provide theoretical results for logistic regression and a 2-layer linear network on the considered data distribution. Furthermore, we advocate using second-order methods that are immune to ill-conditioning and lead to better robustness. We perform experiments and exhibit tremendous robustness improvements in clean training through long training and the employment of second-order methods, corroborating our framework. Additionally, we find the inclusion of batch-norm layers hinders such robustness gains. We attribute this to differing implicit biases between traditional and batch-normalized neural networks.

Imitation learning has proven to be a powerful tool for training complex visuomotor policies. However, current methods often require hundreds to thousands of expert demonstrations to handle high-dimensional visual observations. A key reason for this poor data efficiency is that visual representations are predominantly either pretrained on out-of-domain data or trained directly through a behavior cloning objective. In this work, we present DynaMo, a new in-domain, self-supervised method for learning visual representations. Given a set of expert demonstrations, we jointly learn a latent inverse dynamics model and a forward dynamics model over a sequence of image embeddings, predicting the next frame in latent space, without augmentations, contrastive sampling, or access to ground truth actions. Importantly, DynaMo does not require any out-of-domain data such as Internet datasets or cross-embodied datasets. On a suite of six simulated and real environments, we show that representations learned with DynaMo significantly improve downstream imitation learning performance over prior self-supervised learning objectives, and pretrained representations. Gains from using DynaMo hold across policy classes such as Behavior Transformer, Diffusion Policy, MLP, and nearest neighbors. Finally, we ablate over key components of DynaMo and measure its impact on downstream policy performance. Robot videos are best viewed at https://dynamo-ssl.github.io

Current theories of decision making suggest that the neural circuits in mammalian brains (including humans) computationally combine representations of the past (memory), present (perception), and future (agentic goals) to take actions that achieve the needs of the agent. How information is represented within those neural circuits changes what computations are available to that system which changes how agents interact with their world to take those actions. We argue that the computational neuroscience of decision making provides a new microeconomic framework (neuroeconomics) that offers new opportunities to construct policies that interact with those decision-making systems to improve outcomes. After laying out the computational processes underlying decision making in mammalian brains, we present four applications of this logic with policy consequences: (1) contingency management as a treatment for addiction, (2) precommitment and the sensitivity to sunk costs, (3) media consequences for changes in housing prices after a disaster, and (4) how social interactions underlie the success (and failure) of microfinance institutions.

Graph Neural Network (GNN) models on streaming graphs entail algorithmic challenges to continuously capture its dynamic state, as well as systems challenges to optimize latency, memory, and throughput during both inference and training. We present D3-GNN, the first distributed, hybrid-parallel, streaming GNN system designed to handle real-time graph updates under online query setting. Our system addresses data management, algorithmic, and systems challenges, enabling continuous capturing of the dynamic state of the graph and updating node representations with fault-tolerance and optimal latency, load-balance, and throughput. D3-GNN utilizes streaming GNN aggregators and an unrolled, distributed computation graph architecture to handle cascading graph updates. To counteract data skew and neighborhood explosion issues, we introduce inter-layer and intra-layer windowed forward pass solutions. Experiments on large-scale graph streams demonstrate that D3-GNN achieves high efficiency and scalability. Compared to DGL, D3-GNN achieves a significant throughput improvement of about 76x for streaming workloads. The windowed enhancement further reduces running times by around 10x and message volumes by up to 15x at higher parallelism., Comment: 14 pages, 7 figures, published at VLDB'24

Non-planar aromatic molecules are interesting systems for organic electronics and optoelectronics applications due to their high stability and electronic properties. By using scanning tunneling microscopy and spectroscopy, we investigated thianthrene-based molecules adsorbed on Au(111), which are non-planar in the gas phase and the bulk solid state. Varying the molecular coverage leads to the formation of two different kinds of self-assembled structures: close-packed islands and quasi one-dimensional chains. We found that the molecules are non-planar within the close-packed islands, while the configuration is planar in the molecular chain and for single adsorbed molecules. Using vertical tip manipulation to isolate a molecule from the island, we demonstrate the conversion of a non-planar molecule to its planar configuration. We discuss the two different geometries and their electronic properties with the support of density functional theory calculations.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTSPEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

The fractal and phase transitional properties of each type of pions (i.e. $\pi^{\pm,0}$) through one-dimensional $\eta-$space, at an energy of $\sqrt{s}=13~$TeV, have been studied with the help of the Scaled Factorial Moment (SFM) framework. To generate simulated data sets for $pp$ collisions under the minimum bias (MB) condition at $\sqrt{s}=13~$TeV, we have employed the Monte Carlo-based event simulator PYTHIA. Various parameters such as the Levy index $(\mu)$, degree of multifractality $(r)$, anomalous fractal dimension $(d_q)$, multifractal specific heat $(c)$ and critical exponent $(\nu)$ have been calculated. To study the Bose Einstein(BE) effect due to identical particles (here pions) we have also derived these parameters for mixed pion pairs (i.e. $\{\pi^{+},\pi^{-}\}$, $\{\pi^{+},\pi^{0}\}$ and $\{\pi^{-},\pi^{0}\}$) and we find that the effects of identical particles weakened for the mixture with respect to the individual distributions. The quest for the quark-hadron phase transition has also been conducted within the framework of the Ginzburg-Landau (GL) theory of second-order phase transition. Analysis revealed that for PYTHIA-generated MB events, there is a clear indication of the quark-hadron phase transition according to the GL theory. Furthermore, the values of the multifractal specific heat ($c$) for each $\pi^{+}, \pi^{-}, \pi^{0}$ and the mixture pair data sets of pions generated by PYTHIA model at MB condition, indicate a transition from multifractality to monofractality in $pp$ collisions at $\sqrt{s}=13~$TeV., Comment: 19 pages, 10 Figures ( Total 18 Figures with sub-figures)

Magnetic solitons such as skyrmions and bimerons show great promise for both fundamental research and spintronic applications. Stabilizing and controlling topological spin textures in atomically thin van der Waals (vdW) materials has gained tremendous attention due to high tunability, enhanced functionality, and miniaturization. Using rigorous first-principles calculations and atomistic spin simulations, we predict the existence of multiple magnetic solitons in an all-magnetic vdW heterostructure Fe$_3$GeTe$_2$/Cr$_2$Ge$_2$Te$_6$ (FGT/CGT). N\'eel-type skyrmions are formed at both sides of FGT/CGT with opposite chirality. Skyrmions at the FGT layer and bimerons at the CGT layer persist in the heterostructure at zero field, while the latter undergoes bimeron-skyrmion transformation if a field is applied. We further demonstrate that, although skyrmions and bimerons are topologically equivalent, they exhibit very different localization behavior. Skyrmions are isotropically localized, while bimerons exhibit anisotropic nonlocality, leading to finite-size effects. We analyze how this affects the stability of bimerons in confined materials. Our work represents an important step forward in the fundamental properties of topological spin textures and paves the way toward skyrmionic devices based on atomically thin vdW heterostructures., Comment: 12 pages, 7 figures

The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature., Comment: Published at CIKM 2024

Materials with antiferromagnetic order have recently emerged as promising candidates in spintronics based on their beneficial characteristics such as vanishing stray fields and ultra-fast dynamics. At the same time more complex localized non-coplanar magnetic states as for instance skyrmions are in the focus of applications due to their intriguing properties such as the topological Hall effect. Recently a conceptual shift has occurred to envision the use of such magnetic defects not only in one-dimensional race track devices but to exploit their unique properties in two-dimensional networks. Here we use local strain in a collinear antiferromagnet to induce non-coplanar domain wall junctions, which connect in a very specific way to form extended networks. We combine spin-polarized scanning tunneling microscopy with density functional theory to characterize the different building blocks of the network, and unravel the origin of the handedness of triple-junctions and the size of the arising topological orbital moments., Comment: Marie Sk{\l}odowska-Curie Actions, H2020-MSCA-ITN-2020; Project acronym SPEAR; Grant Agreement No. 955671

We design a set-up for creating maximally entangled two-qudit links between distant nodes which are weakly coupled with interacting spin-s bulk (processor). We exhibit that such quantum links of arbitrary spin quantum number can be formed when the system is prepared at a very low temperature. We find that the Heisenberg and the bilinear-biquadratic (BBQ) spin-s models are the potential candidates to achieve the maximal entanglement in equilibrium. By eliminating the equilibrium requirement, we show that a completely polarized state in the bulk and a suitable qudit state in the link can evolve over time to produce a highly entangled state, as per the BBQ Hamiltonian with nearest- and next-nearest neighbor interactions. When the number of sites in the bulk grows, so does the maximum entanglement produced in dynamics. Further, both the static and the dynamical protocols presented here remain efficient even if the spin quantum numbers of the bulk and the connection are unequal., Comment: 10 pages, 12 figures

We address how the interplay between the finite availability and carrying capacity of particles at different parts of a spatially extended system can control the steady state currents and density profiles in the one-dimensional current-carrying lanes connecting the different parts of the system. To study this, we set up a minimal model consisting of two particle reservoirs of the same finite carrying capacity connected by two equally sized anti-parallel asymmetric exclusion processes (TASEP). We focus on the steady-state currents and particle density profiles in the two TASEP lanes. The ensuing phases and the phase diagrams, which can be remarkably complex, are parametrized by the model parameters defining particle exchange between the TASEP lanes and the reservoirs and the filling fraction of the particles that determine the total resources available. These parameters may be tuned to make the densities of the two TASEP lanes globally uniform or piece-wise continuous in the form of a combination of a single localized domain wall and a spatially constant density or a pair of delocalized domain walls. Our model reveals that the two reservoirs can be preferentially populated or depopulated in the steady states., Comment: 18 pages, 4 figures

Using density functional theory (DFT), we calculate the magnetic short-ranged exchange forces between a magnetic tip and an adatom adsorbed on the antiferromagnetic Mn monolayer on the W(110) surface [Mn/W(110)]. These exchange forces can be measured in magnetic exchange force microscopy allowing atomic-scale imaging of spin structures on insulating and conducting surfaces. We consider two types of $3d$ transition-metal atoms with intrinsic magnetic moments: Co and Mn and Ir as an example of a $5d$ transition-metal atom exhibiting an induced magnetic moment on Mn/W(110). The tips are modeled by Fe pyramids and terminated either with an Fe or a Mn apex atom. From our total energy DFT calculations for a parallel and antiparallel alignment between tip and adatom magnetic moments we obtain the exchange energy $E_{\rm ex}(d)$ as a function of tip-adatom distance $d$. The exchange forces, $F_{\rm ex}(d)$, are calculated based on the Hellmann-Feynman theorem. We show that structural relaxations of tip and sample due to their interaction need to be taken into account. Due to the exchange interaction the relaxations depend on the alignment between tip and adatom magnetization -- an effect which will affect the tunneling magnetoresistance that can be measured by a scanning tunneling microscope. A maximum in the exchange energy and force curves is obtained for magnetic adatoms at tip-adatom separations of about 3 to 4~{\AA}. The exchange forces with an Fe terminated tip reach a maximum value of up to 0.2~nN and 0.6~nN for Co and Mn adatoms, respectively, and prefer an antiferromagnetic coupling. Surprisingly, we also find an exchange force of up to 0.2~nN for Ir adatoms. We analyze the exchange interaction between tip and adatom based on the spin-polarized electronic structure of the coupled system......

Berry curvature of Bloch bands arising in lattice systems can induce a Hall response even in the absence of topology due to the so-called Berry-curvature dipole (BCD). Such a response is universal and, in principle, should occur as a thermal-Hall effect in magnon systems under the application of a temperature gradient. However, this effect intrinsically appears as a non-linear (second-order) response to the temperature gradient making experimental detection difficult. Here, we propose an alternate route to access BCD in magnons. By utilizing the process of spin-injection in conjunction with a temperature gradient, we uncover two previously unreported contributions to the BCD-induced Hall response for magnons -- one that is linear in temperature gradient, and the other is non-linear in the magnon-chemical potential gradient arising from spin injection. As an added benefit of our approach, both these responses extract distinct moments of the genuine BCD distribution over the magnon bands, as opposed to the recently reported extended BCD in magnons. We use Boltzmann transport theory to derive the expression for the magnon-Hall response in the presence of a thermal gradient and spin injection. Furthermore, using this expression, we offer predictions for the BCD-induced magnon-Hall effect to be observed in experiments for ferro, anti-ferro and ferri magnetically-ordered models on various lattices, including the honeycomb lattice, the kagome lattice, and the dice lattice., Comment: 19 pages, 9 figures

As a two-sided marketplace, Airbnb brings together hosts who own listings for rent with prospective guests from around the globe. Results from a guest's search for listings are displayed primarily through two interfaces: (1) as a list of rectangular cards that contain on them the listing image, price, rating, and other details, referred to as list-results (2) as oval pins on a map showing the listing price, called map-results. Both these interfaces, since their inception, have used the same ranking algorithm that orders listings by their booking probabilities and selects the top listings for display. But some of the basic assumptions underlying ranking, built for a world where search results are presented as lists, simply break down for maps. This paper describes how we rebuilt ranking for maps by revising the mathematical foundations of how users interact with search results. Our iterative and experiment-driven approach led us through a path full of twists and turns, ending in a unified theory for the two interfaces. Our journey shows how assumptions taken for granted when designing machine learning algorithms may not apply equally across all user interfaces, and how they can be adapted. The net impact was one of the largest improvements in user experience for Airbnb which we discuss as a series of experimental validations.

We prove asymptotic formulae for small weighted solutions of quadratic congruences of the form $\lambda_1x_1^2+\cdots +\lambda_nx_n^2\equiv \lambda_{n+1}\bmod{p^m}$, where $p$ is a fixed odd prime, $\lambda_1,...,\lambda_{n+1}$ are integer coefficients such that $(\lambda_1\cdots \lambda _{n},p)=1$ and $m\rightarrow \infty$. If $n\ge 6$, $p\ge 5$ and the coefficients are fixed and satisfy $\lambda_1,...,\lambda_n>0$ and $(\lambda_{n+1},p)=1$ (inhomogeneous case), we obtain an asymptotic formula which is valid for integral solutions $(x_1,...,x_n)$ in cubes of side length at least $p^{(1/2+\varepsilon)m}$, centered at the origin. If $n\ge 4$ and $\lambda_{n+1}=0$ (homogeneous case), we prove a result of the same strength for coefficients $\lambda_i$ which are allowed to vary with $m$. These results extend previous results of the first- and the third-named authors and N. Bag., Comment: 20 pages

We investigate experimentally the quantum coherence of an electronic two-level system in a double quantum dot under continuous charge detection. The charge-state of the two-level system is monitored by a capacitively coupled single quantum dot detector that imposes a back-action effect to the system. The measured back-action is well described by an additional decoherence rate, approximately linearly proportional to the detector electron tunneling rate. We provide a model for the decoherence rate arising due to level detuning fluctuations induced by detector charge fluctuations. The theory predicts a factor of two lower decoherence rate than observed in the experiment, suggesting the need for a more elaborate theory accounting for additional sources of decoherence., Comment: 5 pages, 3 figures

Training generalist agents capable of solving diverse tasks is challenging, often requiring large datasets of expert demonstrations. This is particularly problematic in robotics, where each data point requires physical execution of actions in the real world. Thus, there is a pressing need for architectures that can effectively leverage the available training data. In this work, we present BAKU, a simple transformer architecture that enables efficient learning of multi-task robot policies. BAKU builds upon recent advancements in offline imitation learning and meticulously combines observation trunks, action chunking, multi-sensory observations, and action heads to substantially improve upon prior work. Our experiments on 129 simulated tasks across LIBERO, Meta-World suite, and the Deepmind Control suite exhibit an overall 18% absolute improvement over RT-1 and MT-ACT, with a 36% improvement on the harder LIBERO benchmark. On 30 real-world manipulation tasks, given an average of just 17 demonstrations per task, BAKU achieves a 91% success rate. Videos of the robot are best viewed at https://baku-robot.github.io/.

We present a superconducting cavity-coupled double quantum dot (DQD) photodiode that achieves a maximum photon-to-electron conversion efficiency of 25% in the microwave domain. With a higher-quality-factor cavity and improved device design to prevent photon leakages through unwanted pathways, our device measures microwave signals down to 100 aW power level and achieves sensitivity to probe microwave signals with one photon at a time in the cavity. We analyze the photodiode operation using Jaynes-Cummings input-output theory, identifying the key improvements of stronger cavity-DQD coupling needed to achieve near-unity photodetection efficiency. The results presented in this work represent a crucial advancement toward near unity microwave photodetection efficiency with single cavity-photon sensitivity for studies of photon statistics in the microwave range and applications related to quantum information processing., Comment: 8 pages, 4 figures

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Formal software testing education is important for building efficient QA professionals. Various aspects of quality assurance approaches are usually covered in courses for training software testing students. Automated Test Tools is one of the core courses in the software testing post-graduate curriculum due to the high demand for automated testers in the workforce. It is important to understand which factors are affecting student performance in the automated testing course to be able to assist the students early on based on their needs. Various metrics that are considered for predicting student performance in this testing course are student engagement, grades on individual deliverables, and prerequisite courses. This study identifies the impact of assessing students based on individual vs. group activities, theoretical vs. practical components, and the effect of having taken prerequisite courses in their final grade. To carry out this research, student data was collected from the automated test tools course of a community college-based postgraduate certificate program in software testing. The dataset contained student records from the years 2021 to 2022 and consisted of information from five different semesters. Various machine learning algorithms were applied to develop an effective model for predicting students performance in the automated software testing tools course, and finally, important features affecting the students performance were identified. The predictive performance model of the automated test tools course that was developed by applying the logistic regression technique, showed the best performance, with an accuracy score of 90%., Comment: 8 pages

Diffusion on surfaces is a fundamental process in surface science, governing nanostructure and film growth, molecular self-assembly, and chemical reactions. Atom motion on non-magnetic surfaces has been studied extensively both theoretically and by real-space imaging techniques. For magnetic surfaces density functional theory (DFT) calculations have predicted strong effects of the magnetic state onto adatom diffusion, but to date no corresponding experimental data exists. Here, we investigate Co and Rh atoms on a hexagonal magnetic layer, using scanning tunneling microscopy (STM) and DFT calculations. Experimentally, we "kick" atoms by local voltage pulses and thereby initiate strictly one-dimensional motion which is dictated by the row-wise antiferromagnetic (AFM) state. Our calculations show that the one-dimensional motion of Co and Rh atoms results from conserving the Co spin direction during movement and avoiding high induced Rh spin moments, respectively. These findings demonstrate that magnetism can be a means to control adatom mobility., Comment: 5 main figures, 3 extended figures

Strongly correlated materials feature multiple electronic orbitals which are crucial to accurately understand their many-body properties, from cuprate materials to twisted bilayer graphene. In such multi-band models, quantum interference can lead to dispersionless bands whose large degeneracy gives rise to itinerant magnetism even with weak interactions. Here, we report on signatures of a ferrimagnetic state realized in a Lieb lattice at half-filling, characterized by antialigned magnetic moments with antiferromagnetic correlations, concomitant with a finite spin polarization. We demonstrate its robustness when increasing repulsive interactions from the non-interacting to the Heisenberg regime, and study its emergence when continuously tuning the lattice unit cell from a square to a Lieb geometry. Our work paves the way towards exploring exotic phases in related multi-orbital models such as quantum spin liquids in kagome lattices and heavy fermion behavior in Kondo models., Comment: 7+8 pages, 4+11 figures

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge, focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors., Comment: arXiv admin note: substantial text overlap with arXiv:2305.17033

Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We show that for the task of code summarization, the performance of these models on individual examples often depends on the amount of (subword) token overlap between the code and the corresponding reference natural language descriptions in the dataset. This token overlap arises because the reference descriptions in standard datasets (corresponding to docstrings in large code bases) are often highly similar to the names of the functions they describe. We also show that this token overlap occurs largely in the function names of the code and compare the relative performance of these models after removing function names versus removing code structure. We also show that using multiple evaluation metrics like BLEU and BERTScore gives us very little additional insight since these metrics are highly correlated with each other.

Superconducting Nanowire Single Photon Detector (SNSPD) emerges as a potential candidate in the multiple fields requiring sensitive and fast photodetection. While nanowires of low temperature superconducting detectors are mature with commercial solutions, other material options with higher transition temperature and faster responses are currently being explored. Towards this goal, we develop a generalized numerical model that incorporates the thermodynamic properties of the superconducting material and identifies the minimum resolvable photon count for a given bias and device parameters. A phase diagram of detection and latching phases with the minimum number of photons as a function of biasing current and biasing temperature for each material system is presented. We show using the developed model that while low temperature superconducting (LTS) nanowires are more sensitive to the incident photon at different wavelengths, the ultimate limit of a single photon can be achieved using high temperature superconducting (HTS) material such as YBa2Cu3O7-{\delta}, albeit at stringent biasing conditions. On the contrary, ultrafast response time with three orders of magnitude smaller response times can be achieved in select HTS materials making it an appealing for several practical applications.

Open-sourced, user-friendly tools form the bedrock of scientific advancement across disciplines. The widespread adoption of data-driven learning has led to remarkable progress in multi-fingered dexterity, bimanual manipulation, and applications ranging from logistics to home robotics. However, existing data collection platforms are often proprietary, costly, or tailored to specific robotic morphologies. We present OPEN TEACH, a new teleoperation system leveraging VR headsets to immerse users in mixed reality for intuitive robot control. Built on the affordable Meta Quest 3, which costs $500, OPEN TEACH enables real-time control of various robots, including multi-fingered hands and bimanual arms, through an easy-to-use app. Using natural hand gestures and movements, users can manipulate robots at up to 90Hz with smooth visual feedback and interface widgets offering closeup environment views. We demonstrate the versatility of OPEN TEACH across 38 tasks on different robots. A comprehensive user study indicates significant improvement in teleoperation capability over the AnyTeleop framework. Further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact-rich manipulation tasks. Currently supporting Franka, xArm, Jaco, and Allegro platforms, OPEN TEACH is fully open-sourced to promote broader adoption. Videos are available at https://open-teach.github.io/.

In this article, we present an experimental study of a Josephson junction -based high-impedance resonator. By taking the resonator to the limit of consisting effectively only of one junction, results in strong non-linear effects already for the second photon while maintaining a high impedance of the resonance mode. Our experiment yields thus resonators with the strong interactions both between individual resonator photons and from the resonator photons to other electric quantum systems. We also present an energy diagram technique which enables to measure, identify and analyse different multi-photon optics processes along their energy conservation lines.

The existence of adversarial attacks on machine learning models imperceptible to a human is still quite a mystery from a theoretical perspective. In this work, we introduce two notions of adversarial attacks: natural or on-manifold attacks, which are perceptible by a human/oracle, and unnatural or off-manifold attacks, which are not. We argue that the existence of the off-manifold attacks is a natural consequence of the dimension gap between the intrinsic and ambient dimensions of the data. For 2-layer ReLU networks, we prove that even though the dimension gap does not affect generalization performance on samples drawn from the observed data space, it makes the clean-trained model more vulnerable to adversarial perturbations in the off-manifold direction of the data space. Our main results provide an explicit relationship between the $\ell_2,\ell_{\infty}$ attack strength of the on/off-manifold attack and the dimension gap., Comment: AISTATS 2024

Given the black box nature of machine learning models, a plethora of explainability methods have been developed to decipher the factors behind individual decisions. In this paper, we introduce a novel problem of black box (probabilistic) explanation certification. We ask the question: Given a black box model with only query access, an explanation for an example and a quality metric (viz. fidelity, stability), can we find the largest hypercube (i.e., $\ell_{\infty}$ ball) centered at the example such that when the explanation is applied to all examples within the hypercube, (with high probability) a quality criterion is met (viz. fidelity greater than some value)? Being able to efficiently find such a \emph{trust region} has multiple benefits: i) insight into model behavior in a \emph{region}, with a \emph{guarantee}; ii) ascertained \emph{stability} of the explanation; iii) \emph{explanation reuse}, which can save time, energy and money by not having to find explanations for every example; and iv) a possible \emph{meta-metric} to compare explanation methods. Our contributions include formalizing this problem, proposing solutions, providing theoretical guarantees for these solutions that are computable, and experimentally showing their efficacy on synthetic and real data., Comment: Accepted to ICML 2024

Global popularity (GP) bias is the phenomenon that popular items are recommended much more frequently than they should be, which goes against the goal of providing personalized recommendations and harms user experience and recommendation accuracy. Many methods have been proposed to reduce GP bias but they fail to notice the fundamental problem of GP, i.e., it considers popularity from a \textit{global} perspective of \textit{all users} and uses a single set of popular items, and thus cannot capture the interests of individual users. As such, we propose a user-aware version of item popularity named \textit{personal popularity} (PP), which identifies different popular items for each user by considering the users that share similar interests. As PP models the preferences of individual users, it naturally helps to produce personalized recommendations and mitigate GP bias. To integrate PP into recommendation, we design a general \textit{personal popularity aware counterfactual} (PPAC) framework, which adapts easily to existing recommendation models. In particular, PPAC recognizes that PP and GP have both direct and indirect effects on recommendations and controls direct effects with counterfactual inference techniques for unbiased recommendations. All codes and datasets are available at \url{https://github.com/Stevenn9981/PPAC}., Comment: Accepted by WWW'24 as a research full paper