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Magnon based spintronic devices require the modulation of magnon spin transport for their operations. We provide a proof-of-principle of two unconventional non-local magnon transport devices in which we modulate the diffusive magnon transport of incoherent magnons in the van der Waals antiferromagnet Chromium thiophosphate, CrPS4. The non-local signals generated electrically by spin injection via the spin Hall effect (SHE) and thermally via the spin Seebeck effect (SSE) are altered by a modulator electrode. The current through the modulator increases or decreases the magnon chemical potential via the SHE and changes the magnon temperature through Joule heating. We achieve up to 25%/mA and 16%/mA modulation efficiencies for the electrically and thermally generated magnon spin transport, respectively, for CrPS4 in the collinear state at in plane fields >7T at a temperature of 25K.

We investigate the injection, transport and detection of magnon spins in the van der Waals antiferromagnet chromium thiophosphate (CrPS4). We electrically and thermally inject magnon spins by platinum contacts and examine the non-local resistance as a function of in-plane magnetic field up to 12 Tesla. We observe a large non-local resistance from both the electrically and thermally excited magnon modes above the spin-flip field where CrPS4 is in the collinear state. At 25 K for an in-plane field of ranging 5 - 12 T, we extract the magnon relaxation length ranging $200-800$ nm and a typical magnon conductivity of 1e4 Sm-1, which is one order of magnitude smaller than in yttrium iron garnet (YIG) films at room temperature. Moreover, we find that the magnon conductivity is almost zero for CrPS4 in the non-collinear state. Our results open up the way to understanding the role of the antiferromagnetic magnon modes on spin injection into antiferromagnets and implementation of two-dimensional magnets for scalable magnonic circuits., Comment: 7 pages, 5 figures

Recent advancements in sensors have led to high resolution and high data throughput at the pixel level. Simultaneously, the adoption of increasingly large (deep) neural networks (NNs) has lead to significant progress in computer vision. Currently, visual intelligence comes at increasingly high computational complexity, energy, and latency. We study a data-driven system that combines dynamic sensing at the pixel level with computer vision analytics at the video level and propose a feedback control loop to minimize data movement between the sensor front-end and computational back-end without compromising detection and tracking precision. Our contributions are threefold: (1) We introduce anticipatory attention and show that it leads to high precision prediction with sparse activation of pixels; (2) Leveraging the feedback control, we show that the dimensionality of learned feature vectors can be significantly reduced with increased sparsity; and (3) We emulate analog design choices (such as varying RGB or Bayer pixel format and analog noise) and study their impact on the key metrics of the data-driven system. Comparative analysis with traditional pixel and deep learning models shows significant performance enhancements. Our system achieves a 10X reduction in bandwidth and a 15-30X improvement in Energy-Delay Product (EDP) when activating only 30% of pixels, with a minor reduction in object detection and tracking precision. Based on analog emulation, our system can achieve a throughput of 205 megapixels/sec (MP/s) with a power consumption of only 110 mW per MP, i.e., a theoretical improvement of ~30X in EDP., Comment: Accepted to the Conference on Dynamic Data-Driven Applications Systems (DDDAS2024)

The Integrated Waste Management program under the Office of Nuclear Energy is planning for the future transportation, storage, and eventual disposal of spent nuclear fuel and high-level radioactive waste from nuclear power plant and waste custodian sites across the United States. To better enable informed decision-making regarding the back end of the nuclear fuel cycle, the IWM program has been sponsoring the development and application of system analysis tools capable of analyzing various options for managing SNF and HLW. With these tools, integrated waste management system architecture analyses are being conducted to support the future deployment of a comprehensive nuclear waste management system that considers all major back-end aspects of the nuclear fuel cycle i.e., transportation, storage, and disposal. System analyses and assessments typically use these modeling and simulation tools to investigate implications of changes in various assumptions and parameters such as acceptance rates, receipt logic, facility capacities and capabilities, use of standardized canisters, start and stop dates of facilities, etc., Comment: Presented at the 2024 Waste Management Symposia. arXiv admin note: text overlap with arXiv:2306.12319

The United States DOE Office of Integrated Waste Management program is planning for the transportation, storage, and eventual disposal of spent nuclear fuel and high-level radioactive waste from nuclear power plant sites across the United States. The Next Generation System Analysis Model is an agent-based simulation toolkit that is used for system level simulation and analysis of the SNF inventory in the United States. This tool was developed as part of a collaborative effort between Argonne National Laboratory and Oak Ridge National Laboratory. The analyst using NGSAM has the ability to define several factors like the number of storage facilities, capacity at each facility, transportation schedules, shipment rates, and other conditions. The primary purpose of NGSAM is to provide the system analyst with the tools to model the integrated waste management system and gain insights on waste management alternatives, impact of storage choices, generating cost estimates, and developing an integrated approach with emphasis on flexibility., Comment: Presented at the '2024 Waste Management Symposia'

Quantum communication promises unprecedented communication capabilities enabled by the transmission of quantum states of light. However, current implementations face severe limitations in communication distance due to photon loss. Silicon carbide (SiC) defects have emerged as a promising quantum device platform, offering strong optical transitions, long spin coherence lifetimes and the opportunity for integration with semiconductor devices. Some defects with optical transitions in the telecom range have been identified, allowing to interface with fiber networks without the need for wavelength conversion. These unique properties make SiC an attractive platform for the implementation of quantum nodes for quantum communication networks. We provide an overview of the most prominent defects in SiC and their implementation in spin-photon interfaces. Furthermore, we model a memory-enhanced quantum communication protocol in order to extract the parameters required to surpass a direct point-to-point link performance. Based on these insights, we summarize the key steps required towards the deployment of SiC devices in large-scale quantum communication networks., Comment: 20 pages, 8 figures

Language models (LMs) exhibit and amplify many types of undesirable biases learned from the training data, including gender bias. However, we lack tools for effectively and efficiently changing this behavior without hurting general language modeling performance. In this paper, we study three methods for identifying causal relations between LM components and particular output: causal mediation analysis, automated circuit discovery and our novel, efficient method called DiffMask+ based on differential masking. We apply the methods to GPT-2 small and the problem of gender bias, and use the discovered sets of components to perform parameter-efficient fine-tuning for bias mitigation. Our results show significant overlap in the identified components (despite huge differences in the computational requirements of the methods) as well as success in mitigating gender bias, with less damage to general language modeling compared to full model fine-tuning. However, our work also underscores the difficulty of defining and measuring bias, and the sensitivity of causal discovery procedures to dataset choice. We hope our work can contribute to more attention for dataset development, and lead to more effective mitigation strategies for other types of bias., Comment: Accepted at BlackboxNLP 2023

We present the submission of the ILLC at the University of Amsterdam to the BabyLM challenge (Warstadt et al., 2023), in the strict-small track. Our final model, ChapGTP, is a masked language model that was trained for 200 epochs, aided by a novel data augmentation technique called Automatic Task Formation. We discuss in detail the performance of this model on the three evaluation suites: BLiMP, (Super)GLUE, and MSGS. Furthermore, we present a wide range of methods that were ultimately not included in the model, but may serve as inspiration for training LMs in low-resource settings., Comment: Part of the BabyLM challenge at CoNLL

The recently reported Bilinear Magnetoeletric Resistance (BMR) in novel materials with rich spin textures, such as bismuth selenide (Bi$_2$Se$_3$) and tungsten ditelluride (WTe$_2$), opens new possibilities for probing the spin textures via magneto-transport measurements. By its nature, the BMR effect is directly linked to the crystal symmetry of the materials and its spin texture. Therefore, understanding the crystallographic dependency of the effect is crucial. Here we report the observation of crystallographic-dependent BMR in thin WTe$_2$ layers and explore how it is linked to its spin textures. The linear response measured in first harmonic signals and the BMR measured in second harmonic signals are both studied under a wide range of magnitudes and directions of magnetic field, applied current and at different temperatures. We discover a three-fold symmetry contribution of the BMR when current is applied along the a-axis of the WTe$_2$ thin layer at 10 K, which is absent for when current is applied along the b-axis.

Understanding the relaxation and recombination processes of excited states in two-dimensional (2D)/three-dimensional (3D) semiconductor heterojunctions is essential for developing efficient optical and (opto)electronic devices which integrate new 2D materials with more conventional 3D ones. In this work, we unveil the carrier dynamics and charge transfer in a monolayer of WSe$_2$ on a GaAs substrate. We use time-resolved differential reflectivity to study the charge relaxation processes involved in the junction and how they change when compared to an electrically decoupled heterostructure, WSe$_2$/hBN/GaAs. We observe that the monolayer in direct contact with the GaAs substrate presents longer optically-excited carrier lifetimes (3.5 ns) when compared with the hBN-isolated region (1 ns), consistent with a strong reduction of radiative decay and a fast charge transfer of a single polarity. Through low-temperature measurements, we find evidence of a type-II band alignment for this heterostructure with an exciton dissociation that accumulates electrons in the GaAs and holes in the WSe$_2$. The type-II band alignment and fast photo-excited carrier dissociation shown here indicate that WSe$_2$/GaAs is a promising junction for new photovoltaic and other optoelectronic devices, making use of the best properties of new (2D) and conventional (3D) semiconductors., Comment: 6 pages, 4 Figures

The United States Department of Energy has long term goals to develop solutions for managing the nations spent nuclear fuel and high-level waste inventory. The Integrated Waste Management program is employing system-level engineering and analysis principles to inform potential future waste management system architectures. Managing the SNF requires the use of system-level analysis software that considers waste generation, on-site (centralized storage), transportation infrastructure, and long-term disposal. The Next Generation System Analysis Model is an agent-based model that was developed to simulate the transportation and storage of SNF and HLW. NGSAM has the capability to detail the interaction and movement of individual components and groups, such as rail cars and casks. The SNF inventory from commercial nuclear reactors is currently in temporary storage at multiple locations spread across the US. Shipping of SNF from these locations relies on one of three transportation modes: rail, heavy-haul truck, or barge. Rail is the most preferred due to the size of the canisters and casks the SNF would be shipped in. Under some scenarios, a rail route might not be available to a reactor site or improving the rail infrastructure at shutdown sites might be too cost-prohibitive for utilities to opt for a direct rail transfer. Under such scenarios, using a barge or heavy haul truck to de-inventory the site and transfer the SNF to a nearby intermodal transfer site with adequate rail infrastructure where the payload could be transferred to a rail car might prove to be an attractive option. This work presents the various intermodal transportation options to move SNF from reactor sites to rail cars. Next, the operational steps in each of these modes to move the SNF from a reactor site and transfer it to a rail car is explored. The ideology, assumptions, and future steps are presented., Comment: American Nuclear Society 2022 International High-Level Radioactive Waste Management Conference (https://www.ans.org/pubs/proceedings/article-52728/)

How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \textit{Pythia} to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at \url{https://github.com/EleutherAI/pythia}., Comment: Code at https://github.com/EleutherAI/pythia

Prostate cancer is one of the leading causes of cancer-related death in men worldwide. Like many cancers, diagnosis involves expert integration of heterogeneous patient information such as imaging, clinical risk factors, and more. For this reason, there have been many recent efforts toward deep multimodal fusion of image and non-image data for clinical decision tasks. Many of these studies propose methods to fuse learned features from each patient modality, providing significant downstream improvements with techniques like cross-modal attention gating, Kronecker product fusion, orthogonality regularization, and more. While these enhanced fusion operations can improve upon feature concatenation, they often come with an extremely high learning capacity, meaning they are likely to overfit when applied even to small or low-dimensional datasets. Rather than designing a highly expressive fusion operation, we propose three simple methods for improved multimodal fusion with small datasets that aid optimization by generating auxiliary sources of supervision during training: extra supervision, clinical prediction, and dense fusion. We validate the proposed approaches on prostate cancer diagnosis from paired histopathology imaging and tabular clinical features. The proposed methods are straightforward to implement and can be applied to any classification task with paired image and non-image data., Comment: IEEE ISBI 2023 (see http://2023.biomedicalimaging.org/en/)

Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to democratize access to interpretability analyses of sequence generation models. Inseq enables intuitive and optimized extraction of models' internal information and feature importance scores for popular decoder-only and encoder-decoder Transformers architectures. We showcase its potential by adopting it to highlight gender biases in machine translation models and locate factual knowledge inside GPT-2. Thanks to its extensible interface supporting cutting-edge techniques such as contrastive feature attribution, Inseq can drive future advances in explainable natural language generation, centralizing good practices and enabling fair and reproducible model evaluations., Comment: ACL 2023 Demo Track. Library: https://github.com/inseq-team/inseq, Docs: https://inseq.readthedocs.io, v0.4

Semiconductor transition metal dichalcogenides (TMDs) have equivalent dynamics for their two spin/valley species. This arises from their energy-degenerated spin states, connected via time-reversal symmetry. When an out-of-plane magnetic field is applied, time-reversal symmetry is broken and the energies of the spin-polarized bands shift, resulting in different bandgaps and dynamics in the K$_+$ and K$_-$ valleys. Here, we use time-resolved Kerr rotation to study the magnetic field dependence of the spin dynamics in monolayer MoSe$_2$. We show that the magnetic field can control the light-induced spin accumulation of the two valley states, with a small effect on the recombination lifetimes. We unveil that the magnetic field-dependent spin accumulation is in agreement with hole spin dynamics at the longer timescales, indicating that the electron spins have faster relaxation rates. We propose a rate equation model that suggests that lifting the energy-degeneracy of the valleys induces an ultrafast spin-flip toward the stabilization of the valley with the higher valence band energy. Our results provide an experimental insight into the ultrafast charge and spin dynamics in TMDs and a way to control it, which will be useful for the development of new spintronic and valleytronic applications., Comment: 6 pages, 4 figures

HollandPTC is an independent outpatient center for proton therapy, scientific research, and education. Patients with different types of cancer are treated with Intensity Modulated Proton Therapy (IMPT). In addition, the HollandPTC R&D consortium conducts scientific research into the added value and improvements of proton therapy. To this end, HollandPTC created clinical and pre-clinical research facilities including a versatile R&D proton beamline for various types of biologically and technologically oriented research. In this work, we present the characterization of the R&D proton beam line of HollandPTC. Its pencil beam mimics the one used for clinical IMPT, with energy ranging from 70 up to 240 MeV, which has been characterized in terms of shape, size, and intensity. For experiments that need a uniform field in depth and lateral directions, a dual ring passive scattering system has been designed, built, and characterized. With this system, field sizes between 2x2 cm2 and 20x20 cm2 with 98% uniformity are produced with dose rates ranging from 0.5 Gy/min up to 9 Gy/min. The unique and customized support of the dual ring system allows switching between a pencil beam and a large field in a very simple and fast way, making the HollandPTC R&D proton beam able to support a wide range of different applications.

We demonstrate the potential of van der Waals magnets for spintronic applications by reporting long-distance magnon spin transport in the electrically insulating antiferromagnet chromium thiophosphate (CrPS$_4$) with perpendicular magnetic anisotropy. We inject and detect magnon spins non-locally by Pt contacts and monitor the non-local resistance as a function of an in-plane magnetic field up to 7 Tesla. We observe a non-local resistance over distances up to at least a micron below the Neel temperature (T$_{\rm N}$ = 38 Kelvin) close to magnetic field strengths that saturate the sublattice magnetizations., Comment: 5 pages, 5 figures