Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti–PD-1 antibodies recognizing the membrane-proximal region are PD-1 agonists that can down-regulate inflammatory diseases

The PD-1 receptor triggers a negative immunoregulatory mechanism that prevents overactivation of immune cells and subsequent inflammatory diseases. Because of its biological significance, PD-1 has been a drug target for modulating immune responses. Immunoenhancing anti–PD-1 blocking antibodies have become a widely used cancer treatment; however, little is known about the required characteristics for anti–PD-1 antibodies to be capable of stimulating immunosuppressive activity. Here, we show that PD-1 agonists exist in the group of anti–PD-1 antibodies recognizing the membrane-proximal extracellular region in sharp contrast to the binding of the membrane-distal region by blocking antibodies. This trend was consistent in an analysis of 81 anti-human PD-1 monoclonal antibodies. Because PD-1 agonist antibodies trigger immunosuppressive signaling by cross-linking PD-1 molecules, Fc engineering to enhance FcγRIIB binding of PD-1 agonist antibodies notably improved human T cell inhibition. A PD-1 agonist antibody suppressed inflammation in murine disease models, indicating its clinical potential for treatment of various inflammatory disorders, including autoimmune diseases. Description Agonistic anti-PD-1 antibodies that bind the membrane-proximal extracellular region of PD-1 suppress T cell activity in vivo. Inhibitory anti–PD-1 sticks its landing The PD-1 glycoprotein on T cells is widely appreciated as an immune checkpoint molecule recognized by blocking monoclonal antibodies (mAbs) that can boost the function of antitumor T cells in the setting of cancer. In sharp contrast, a distinct set of anti–PD-1 mAbs has the opposite functional effect, switching on inhibitory signaling in T cells. Using a panel of 81 anti-human PD-1 mAbs with binding sites mapping to eight separate segments on PD-1’s surface, Suzuki et al. found that the inhibitory mAbs all bound to the membrane proximal portion of PD-1. Protein engineering that improved binding of this group of mAbs to Fc receptors increased their inhibitory effects. These discoveries provide a blueprint for designing potent inhibitory anti-PD-1 antibodies for treatment of human autoimmune diseases. —IRW
Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset

We investigate the problem of identifying the major hypothesis that is addressed in a scientific paper. To this end, we present a dataset from the domain of invasion biology that organizes a set of 954 papers into a network of fine-grained domain-specific categories of hypotheses. We carry out experiments on classifying abstracts according to these categories and present a pilot study on annotating hypothesis statements within the text. We find that hypothesis statements in our dataset are complex, varied and more or less explicit, and, importantly, spread over the whole abstract. Experiments with BERT-based classifiers show that these models are able to classify complex hypothesis statements to some extent, without being trained on sentence-level text span annotations.
Sixty gigahertz indoor radio wave propagation prediction method based on full scattering model

In radio system deployment, the main focus is on assuring sufficient coverage, which can be estimated with path loss models for specific scenarios. When more detailed performance metrics such as peak throughput are studied, the environment has to be modeled accurately in order to estimate multipath behavior. By means of laser scanning we can acquire very accurate data of indoor environments, but the format of the scanning data, a point cloud, cannot be used directly in available deterministic propagation prediction tools. Therefore, we propose to use a single‐lobe directive model, which calculates the electromagnetic field scattering from a small surface and is applicable to the point cloud, and describe the overall field as fully diffuse backscattering from the point cloud. The focus of this paper is to validate the point cloud‐based full diffuse propagation prediction method at 60 GHz. The performance is evaluated by comparing characteristics of measured and predicted power delay profiles in a small office room and an ultrasonic inspection room in a hospital. Also directional characteristics are investigated. It is shown that by considering single‐bounce scattering only, the mean delay can be estimated with an average error of 2.6% and the RMS delay spread with an average error of 8.2%. The errors when calculating the azimuth and elevation spreads are 2.6° and 0.6°, respectively. Furthermore, the results demonstrate the applicability of a single parameter set to characterize the propagation channel in all transmit and receive antenna locations in the tested scenarios.
PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature

We present a publicly available corpus with detailed annotations describing the core elements of clinical trials: Participants, Intervention, Control, and Outcomes. The corpus consists of 1011 abstracts of breast cancer randomized controlled trials extracted from the PubMed database. The corpus improves previous corpora by providing detailed annotations for outcomes to identify numeric texts that report the number of participants that experience specific outcomes. The corpus will be helpful for the development of systems for automatic extraction of data from randomized controlled trial literature to support evidence-based medicine. Additionally, we demonstrate the feasibility of the corpus by using two strong baselines for named entity recognition task. Most of the entities achieve F1 scores greater than 0.80 demonstrating the quality of the dataset.
Point cloud projection based light-to-medium G-PCC-1 hole distortion repair method for colored point cloud

With the wide application of Colored Point Clouds(CPC), the amount of data is increasing, and efficient compression is required in practical applications. The most advanced compression technology for static CPC is the G-PCC proposed by MPEG. However, quantification errors from G-PCC-1 (Octree) can cause grid hole distortion, which can have a serious impact on the quality of user's visual perception. For this reason, this paper proposes a Point Cloud Projection based light-to-medium G-PCC-1 Hole Distortion Repair method (denoted as P-GHDR) for CPC. The distorted CPC is projected from 3D space to 2D plane, and the G-PCC-1 distorted is repaired by combining multi-view color and geometric projection maps. Finally, the repaired CPC is reconstructed by reverse projection. Experiments show that the proposed method can effectively improve the geometric and visual objective metrics of G-PCC-1 coded CPC, and can significantly improve the quality of CPC reconstructed with light-to-medium G-PCC-1 codes.
Parametric evaluation of observed objects from images based on perspective geometry methods and convolutional neural networks

We proposed an approach for estimating the shape and geometric parameters of the observed objects from a perspective image based on typed elements, perspective geometry methods and convolutional neural networks. The proposed method uses the assumption that the object under study is rigid. A method is proposed for restoring a 3-D model of an observed object from one perspective image using reference objects and typed elements. Semantic segmentation of typed elements allows to set the photometric parameters of the coordinate system attached to the points on the image. According to the calculated photometric parameters and segmentation of the observed object in the image, its parameters and a 3-D model are estimated. The developed method is applicable for calculating 3-D models from a single perspective image in the vicinity of a road (both road and railway) infrastructure, where there are a large number of typed elements.
Head radiographic detection of feature aggregation networks with coordinated attention

Dental oral disease is one of the most prevalent diseases worldwide, as of a medical analysis in The Lancet 2022[1]. The most common oral diseases worldwide are dental caries (cavities), periodontal disease, tooth loss, and overdevelopment of the jaw caused by excessive unilateral chewing. Dental radiography plays a very important role in clinical diagnosis, treatment and surgery. Automatic segmentation of medical lesions is a prerequisite for efficient clinical analysis. Therefore, accurate positioning of anatomical landmarks is a crucial technique for clinical diagnosis and treatment planning. In this paper, we propose a novel deep network to detect anatomical landmarks. Our proposed network consists of a multi-scale feature aggregation module for channel attention and a deep network for feature refinement. To demonstrate the superiority of our network, training comparisons with several popular networks are performed on the same dataset. The end result is that our network outperforms several popular networks today in both mean radial error (MRE) and successful detection rate (SDR).
Future challenges and opportunities in language testing and assessment: Basic questions and principles at the forefront

In this invited Viewpoint on the occasion of the 40th anniversary of the journal Language Testing, I argue that at the core of future challenges and opportunities for the field—both in scholarly and operational respects—remain basic questions and principles in language testing and assessment. Despite the high levels of sophistication of issues looked into, and methodological and operational solutions found, outstanding concerns still amount to: what are we testing, how are we testing, and why are we testing? Guided by these questions, I call for more thorough and adequate language use domain definitions (and a suitable broadening of research and testing methodologies to determine these), more comprehensive operationalizations of these domain definitions (especially in the context of technology in language testing), and deeper considerations of test purposes/uses and of their connections with domain definitions. To achieve this, I maintain that the field needs to continue investing in the topics of validation, ethics, and language assessment literacy, and engaging with broader fields of enquiry such as (applied) linguistics. I also encourage a more synthetic look at the existing knowledge base in order to build on this, and further diversification of voices in language testing and assessment research and practice.
Strong Narrow Fe II Emission Lines in the Quasar SDSS J102839.11+450009.4

We report identification of SDSS J102839.11+450009.4 as a peculiarly interesting quasar. It shows very prominent narrow emission lines (NELs) in Fe II that are remarkably different from the broad Fe II multiplets commonly observed in quasars and Seyfert 1 galaxies. The Fe II NEL spectrum is dominated in the UV by transitions to the low-lying ground levels with even parity, and by forbidden transitions in the optical, suggesting its formation in a warm collisionally ionized low density medium. Comparison with photoionization models suggests that fluorescence of Fe II by a soft continuum is probably also important in explaining the intensity ratios within the Fe II NEL multiplets and that of Fe II to S II λ4047 NELs. The Fe II NELs have similar widths to other NELs, including [O III] and [O II], but are blueshifted by ~170 km s−1 with respect to the redshift of the latter, which we take to be the systemic redshift of the quasar. In addition to the unusual Fe II NELs, the SDSS spectrum also reveals narrow absorption lines (NALs) in Hβ, Hγ, Hδ, He I λλ3889, 3189, 2946, 2830, and Mg II λλ2796, 2803. We identify two Mg II NAL systems blueshifted by 1200 and 670 km s−1, respectively. The redshift of the lower velocity Mg II NAL system is consistent with that of the Balmer and He I NALs. The observed He I NALs require a hydrogen column density of >1020 cm−2 in an H II region. Very few quasars show absorption in He I lines, and even fewer in Balmer lines. The unique spectral properties make this quasar a promising probe of the formation of Fe II emission lines and outflows, and the possible relation between the two.
Creating a "Faraday Ghost" inside the Rotation Measure Synthesis Technique, through a Wide Observational "Gap" in Wavelength Coverage

Several recently published Faraday rotation measures (RM) derived using the novel RM synthesis technique are likely in error. If a set of polarimetric observations contains a large gap in the wavelength coverage, the rotation measure determination is sometimes ambiguous; this is also true even when two long wavelength ranges are observed but are separated by a wide gap. Essentially, there are 180° ambiguities in the observed Position Angle of the electric polarisation vector between the two wavelength ranges; these ambiguities are not resolved because the extent of wavelengths2 covered, within each of the two ranges, is too small to uniquely determine the RM in isolation. We find that unphysical “Faraday ghosts” can be mathematically constructed with a np ambiguity (±180° times an integer) at predictable polarization position angles when using only two wavelength ranges separated by a gap, as a function of the width of the gap (Equation (4)). Our computations suggest an empirical correlation between an observational gap between two wavelength ranges and the appearance of “Faraday ghosts”.
Feedback control of the Rayleigh–Bénard convection by means of mode reduction

An optimal feedback control is synthesized for the Rayleigh–Bénard convection by means of empirical reduction of modes. The Boussinesq equation is reduced to a minimal set of ordinary differential equations by using the Karhunen–Loève Galerkin procedure. The state feedback control synthesis, that drives the intensity of convection to a preset trajectory by adjusting heat flux at the bottom of the system, is constructed using this low‐dimensional dynamic model by first performing an extended Kalman filter estimate of the velocity and temperature fields and then developing the optimal feedback law by means of the linear regulator theory. The present technique allows for the practical implementation of modern control concepts to the natural convection and is found to yield satisfactory results. Copyright © 2002 John Wiley & Sons, Ltd.
SPAMeR: Speculative Push for Anticipated Message Requests in Multi-Core Systems

With increasing core counts and multiple levels of cache memories, scaling multi-threaded and task-level parallel workloads is continuously becoming a challenge. A key challenge to scaling the number of communicating tasks (or threads) is the rate at which existing communication mechanisms scale (in terms of latency and bandwidth). Architectures with hardware accelerated queuing operations have the potential to reduce the latency and improve scalability of moving data between processing elements, reducing synchronization penalties, and thereby improving the performance of task-level parallel workloads. While hardware queues reduce synchronization penalties, they cannot fully hide load-to-use latency, i.e., perfect pipelines often are not realized. There is the potential, however, for better overlap. If the inter-processor communication latency is equal to or less than the time spent processing a message at the consumer, any and all latency may be overlapped while the consumer is processing. We exploit this property to speedup parallel applications above and beyond existing hardware queues. In this paper, we present SPAMeR, a speculation mechanism built on top of a state-of-the-art hardware-driven message queue architecture. SPAMeR has the capability to speculatively push messages in anticipation of consumer message requests. Unlike pre-fetch approaches which predict what addresses to fetch next, with a queue we know exactly what data is needed next but not when it is needed; SPAMeR adds algorithms that attempt to predict this. We evaluate the effectiveness of SPAMeR with a set of diverse task-parallel benchmarks utilizing the gem5 full system simulator, and observe a 1.33 × average speedup.
Acuerdo: Fast Atomic Broadcast over RDMA

Atomic broadcast protocols ensure that messages are delivered to a group of machines in some total order, even when some of these machines can fail. These protocols are key to making distributed services fault-tolerant, as their total order guarantee allows keeping multiple service replicas in sync. But, unfortunately, atomic broadcast protocols are also notoriously expensive. We present a new protocol, called Acuerdo, that improves atomic broadcast performance by using remote direct memory addressing (RDMA). Acuerdo is built from the ground up to perform communication using one-side RDMA writes, which do not use the CPU of the remote machine, and is explicitly designed to minimize waiting on the critical path. Our experimental results demonstrate that Acuerdo provides raw throughput comparable to or exceeding other RDMA atomic broadcast protocols, while improving latency by almost 2x.
The characteristics of the particle position along an optical axis in particle holography

Particle diagnostics involving three-dimensional distributions are important topics in many engineering fields. The holographic particle velocimetry system is a promising optical tool for measuring three-dimensional particle velocities. One of the inherent limitations of particle holography is the very long depth of field of particle images, which causes considerable difficulty in the determination of particle positions along the optical axis. In this study, diffused illumination holography was adopted as a tool for analysing the characteristics of particles. The prerequisites for a more precise determination of the velocities of particles are a profound understanding of image properties and of the focusing parameter used to determine the particle position along an optical axis. For this purpose, the characteristics of speckles and particle images were investigated with respect to variations in hologram aperture and particle position along the optical axis. Based on our findings, we introduced three auto-focusing parameters corresponding to the sizes of particles, namely, correlation coefficient, sharpness index and depth intensity, to determine the focal plane of a particle along the optical axis. To investigate the suitability of these parameters, the plane image of a dot array screen containing different dot sizes was recorded and the positions of each dot along the optical axis were evaluated. In addition, the effect of particle distance from the holographic film was examined by changing the distance between the screen and the holographic film. All measurement results verified that the evaluated positions throughout the suggested auto-focusing parameters remain within acceptable error ranges. These research results may provide fundamental information for the development of a holographic velocimetry system based on automatic image processing.
A framework for low communication approaches for large scale 3D convolution

Large-scale 3D convolutions computed using parallel Fast Fourier Transforms (FFTs) demand multiple all-to-all communication steps, which cause bottlenecks on computing clusters. Since data transfer speeds to/from memory have not increased proportionally to computational capacity (in terms of FLOPs), 3D FFTs become bounded by communication and are difficult to scale, especially on modern heterogeneous computing platforms consisting of accelerators like GPUs. Existing HPC frameworks focus on optimizing the isolated FFT algorithm or communication patterns, but still require multiple all-to-all communication steps during convolution. In this work, we present a strategy for scalable convolution such that it avoids multiple all-to-all exchanges, and also optimizes necessary communication. We provide proof-of-concept results under assumptions of a use case, the MASSIF Hooke’s law simulation convolution kernel. Our method localizes computation by exploiting properties of the data, and approximates the convolution result by data compression, resulting in increased scalability of 3D convolution. Our preliminary results show scalability of 8 times more than traditional methods in the same compute resources without adversely affecting result accuracy. Our method can be adapted for first-principle scientific simulations and leverages cross-disciplinary knowledge of the application, the data and computing to perform large-scale convolution while avoiding communication bottlenecks. In order to make our approach widely usable and adaptable for emerging challenges, we discuss the use of FFTX, a novel framework which can be used for platform-agnostic specification and optimization for algorithmic approaches similar to ours.
ElastiSim: A Batch-System Simulator for Malleable Workloads

As high-performance computing infrastructures move towards exascale, the role of resource and job management systems is more critical now than ever. Simulating batch systems to improve scheduling algorithms and resource management efficiency is an indispensable option, as running large-scale experiments is expensive and time-consuming. Batch-system simulators are responsible for simulating the computing infrastructure and the types of jobs that constitute the workload. In contrast to rigid jobs, malleable jobs can dynamically reconfigure their resources during runtime. Although studies indicate that malleability can improve system performance, no simulator exists to investigate malleable scheduling policies. In this work, we present ElastiSim, a batch-system simulator supporting the combined scheduling of rigid and malleable jobs. To facilitate the simulation, we propose a malleable workload model and introduce a scheduling protocol that enables the evaluation of topology-, I/O-, and progress-aware scheduling algorithms. We validate the scaling behavior of our workload model by comparing training runtimes of various deep-learning models against the results achieved by ElastiSim. We use real-world cluster trace files to generate workloads and simulate various scheduling algorithms (FCFS, SJF, DRF, SRTF) to analyze their implications on the simulated platform. The results demonstrate that real-world executions show the same scaling behavior as our proposed workload model. We further show that ElastiSim can capture the complex interplay between emerging workloads and modern platforms to support algorithm designers by providing consistently meaningful results. ElastiSim is publicly available as an open-source project on https://github.com/elastisim.
BSCache: A Brisk Semantic Caching Scheme for Cloud-based Performance Monitoring Timeseries Systems

Cloud-based performance monitoring timeseries systems are emerging due to cloud’s flexibility and pay-as-you-go capabilities. For such systems, caching is particularly important considering the limited bandwidth and long access latency of the cloud storage. However, existing cache schemes, such as with external cache systems (e.g. Memcached or Redis), are not specially designed for timeseries data and thus provide suboptimal performance. In this paper, we propose BSCache, a novel lightweight semantic cache mechanism for cloud-based performance monitoring timeseries systems, which is a variant of semantic cache specially designed for timeseries workloads. BSCache supports semantic-aware, metadata-data mixed in-memory management so it can significantly improve timeseries query performance. We have implemented a fully-functional, open-source prototype of BSCache and integrated it into Cortex, a distributed performance monitoring timeseries system widely adopted in industry. BSCache is compared with Memcached which is the default caching system in Cortex. Experimental results show that BSCache can significantly improve query performance with higher cache hit ratios and less CPU overhead under the same cache sizes compared with Memcached in Cortex.