Miracle-dz/UniFAR-FTUs huggingface dataset:
Communication-Efficient Generalized Neuron Matching for Federated Learning

Federated Learning (FL) is a popular distributed machine learning paradigm that allows multiple participants to collaboratively train a model without sharing the raw data. In each round of FL, all participants train local models in parallel, and a server aggregates the local models to create the global model. Neuron matching is a promising aggregation method that utilizes permutational invariance to improve the quality of the global model and reduces communication costs. However, existing neural matching approaches have two strict limitations: 1) They are only applicable to models with simple sequential networks and cannot support advanced models such as ResNet. 2) Their approaches significantly increase the model size after matching, which causes massive memory and communication costs as the training proceeds. In this paper, we propose a novel neuron matching algorithm called federated generalized matched averaging (FedGMA) to mitigate these limitations. Our experiments show that the proposed method is applicable to complex network structures such as ResNet and InceptionNet, and achieves accuracy comparable to algorithms that directly average the entire model. It also reduces communication costs and demonstrates robustness in data heterogeneity scenarios.
BitColor: Accelerating Large-Scale Graph Coloring on FPGA with Parallel Bit-Wise Engines

The graph coloring algorithm plays a crucial role in many applications such as social network analysis. However, since the minimal graph coloring problem is NP-complete, which is increasingly computationally and memory-intensive as the number of vertices in the graph grows rapidly. Despite numerous FPGA-based works proposed to accelerate large-scale graph processing algorithms, such as Single Source Shortest Path, and various coloring algorithms, such as linear programming algorithms, efficiently implementing the greedy coloring algorithm for large-scale graphs on FPGA still remains highly challenging due to several reasons: ① The coloring algorithm requires color state traversal to determine the final color after traversing neighbor vertices. The time complexity of color traversal is equal to the neighbor vertices traversal, which is inefficient. ② Neighbor vertices traversal requires extensive random memory accesses on vertex color data. ③ Coloring different vertices in parallel is difficult due to potential color update conflicts between adjacent vertices. In this paper, we present BitColor to accelerate large-scale graph coloring on FPGA. Firstly, BitColor uses a bit-wise processing engine to complete fast color state determination in one cycle instead of multiple traversal cycles. Secondly, to mitigate heavy random accesses, BitColor adopts a customized high-degree vertex cache (HVC) and a graph prune strategy to reduce off-chip memory access and improve throughput for reading and updating vertex colors. Finally, BitColor employs a data conflict table and a multi-port HVC to enable simultaneous coloring of vertices. The experimental results demonstrate that BitColor achieves 54.9 × performance speedup over CPU and 2.71 × speedup over GPU.
Typing Behavior is About More than Speed: Users' Strategies for Choosing Word Suggestions Despite Slower Typing Rates

Mobile word suggestions can slow down typing, yet are still widely used. To investigate the apparent benefits beyond speed, we analyzed typing behavior of 15,162 users of mobile devices. Controlling for natural typing speed (a confounding factor not considered by prior work), we statistically show that slower typists use suggestions more often but are slowed down by doing so. To better understand how these typists leverage suggestions -- if not to improve their speed -- we extract eight usage strategies, including completion, correction, and next-word prediction. We find that word characteristics, such as length or frequency, along with the strategy, are predictive of whether a user will select a suggestion. We show how to operationalize our findings by building and evaluating a predictive model of suggestion selection. Such a model could be used to augment existing suggestion algorithms to consider people's strategic use of word predictions beyond speed and keystroke savings.
SuperGlue-Based Deep Learning Method for Image Matching from Multiple Viewpoints

With the ability to identify the same physical content from two or more images, image matching has always been a crucial and fundamental task in the fields of computer vision. Effective methods dealing with image matching are of great significance for 3D vision problems like Structure from Motion (SfM). In this work, a deep learning method is proposed based on SuperGlue to match images from multiple viewpoints with excellent performance. We utilize the SuperPoint network as the CNN backbone to extract keypoints and feature descriptors of an image pair. The descriptors are subsequently processed by SuperGlue using graph neural networks to identify then match the feature points. The matching results are finally optimized by the USAC_MAGSAC algorithm, which is a combination of MAGSAC++ implementation and degeneracy tests. The performance of our method is evaluated by the Kaggle competition Image Matching Challenge 2022, which is quantified by the mean Average Accuracy 0.84824/0.83940 on the public/private test set. This result places our team in the 15th position overall out of 643 teams and qualifies us for the silver medal in this competition. This work could have the potential to facilitate the development of generalized techniques of 3D vision tasks.
Re-aligning Across-page Requests for Flash-based Solid-state Drives

In flash-based solid-state drives (SSDs), certain small unaligned I/O requests span two logical pages though their size is not larger than the basic write/read unit of SSDs (i.e. an SSD page), and we term them as across-page requests. Servicing such across-page requests triggers two separated I/O operations on different SSD pages, and thus impacts the I/O performance and the endurance of SSDs. For mitigating negative effects caused by across-page requests, this paper proposes a novel flash translation layer (FTL) scheme for SSDs to separately re-align such requests via remapping them onto a single SSD page. Consequently, both read and write requests on the across-page data can be completed with one page-level I/O operation. Through a series of experiments based on the selected disk traces of real-world applications, we demonstrate that the proposed realigning method at FTL of SSD devices, can noticeably reduce the I/O latency by between 4.6% and 11.6%, and the erase number (i.e. the indicator of SSD endurance) by between 6.4% and 19.11%, compared to state-of-the-art methods.
An approximation method for a non-preemptive multiserver queue with quasi-Poisson arrivals

We consider a non-preemptive multiserver queue with multiple priority classes. We assume distinct exponentially distributed service times and separate quasi-Poisson arrival processes with a predefined maximum number of requests that can be present in the system for each class. We present an approximation method to obtain the steady-state probabilities for the number of requests of each class in our system. In our method, the priority levels (classes) are solved “nearly separately”, linked only by certain conditional probabilities determined approximately from the solution of other priority levels. Several numerical examples illustrate the accuracy of our approximate solution. The proposed approach significantly reduces the complexity of the problem while featuring generally good accuracy.
Conflux: Exploiting Persistent Memory and RDMA Bandwidth via Adaptive I/O Mode Selection

Persistent Memory (PM) and Remote Direct Memory Access (RDMA) technologies have significantly improved the storage and network performance in data centers and spawned a slew of distributed file system (DFS) designs. Existing DFSs often consider remote storage a performance constraint, assuming it delivers lower bandwidth and higher latency than local storage devices. However, the advances in RDMA technology provide an opportunity to bridge the performance gap between local and remote access, enabling DFSs to leverage both local and remote PM bandwidth and achieve higher overall throughput. We propose Conflux, a new DFS architecture that leverages the aggregated bandwidth of PM and RDMA networks. Conflux dynamically steers I/O requests to local and remote PM to fully utilize PM and RDMA bandwidth under heavy workloads. To adaptively decide the I/O run-time path, we propose SEED, a learning-based policy engine predicting Conflux I/O latency and making decisions in a real-time system. Furthermore, Conflux adopts a fine-grained concurrency control approach to improve its scalability. Experimental results show that Conflux achieves up to 4.7 × throughput compared to existing DFSs on multi-threaded workloads.
Imperfect Surrogate Users: Understanding Performance Implications of Augmentative and Alternative Communication Systems through Bounded Rationality, Human Error, and Interruption Modeling

Nonspeaking individuals with motor disabilities frequently rely on augmentative and alternative communication (AAC) systems that allow users to communicate through a text entry interface coupled with a speech synthesizer. Such systems are notoriously difficult to evaluate with end-users. However, recent research has proposed envelope analysis as a method to estimate text entry rates and keystroke savings by simulating the interaction of an expert surrogate user entering sentences on a conceptual word-predictive text entry system. While only a part of the evaluation process of an AAC system, this method enables AAC designers to benefit from quantitative insights early on in the design process. This paper extends prior work by (1) demonstrating how to incorporate natural language generation, such as sentence generation, in such analyses; (2) presenting a model of an imperfect surrogate user that incorporates bounded rationality, human error, and interruptions to provide a more realistic simulation of text entry behavior; and (3) demonstrating how to estimate model parameters by observing users' actual typing behavior. We validate the model with data collected from eight participants using an AAC system on a touchscreen.
Communication-Avoiding Optimizations for Large-Scale Unstructured-Mesh Applications with OP2

In this paper, we investigate data movement-reducing and communication-avoiding optimizations and their practicable implementation for large-scale unstructured-mesh applications. Utilizing the high-level abstraction of the OP2 DSL for the unstructured-mesh class of codes, we reason about techniques for reduced communications across a consecutive sequence of loops – a loop-chain. The careful trade-off with increased redundant computation in place of data movement is analyzed for distributed-memory parallelization. A new communication-avoiding (CA) back-end for OP2 is designed, codifying these techniques such that they can be applied automatically to any OP2 application. The back-end is extended to operate on a cluster of GPUs, integrating GPU-to-GPU communication with CUDA, in combination with MPI. The new CA back-end is applied automatically to two non-trivial applications, including the OP2 version of Rolls-Royce’s production CFD application, Hydra. Performance is investigated on both CPU and GPU clusters on representative problems of 8M and 24M node mesh sizes. Results demonstrate how for select configurations the new CA back-end provides between 30 – 65% runtime reductions for the loop-chains in these applications for the mesh sizes on both an HPE Cray EX system and an NVIDIA V100 GPU cluster. We model and examine the determinants and characteristics of a given unstructured-mesh loop-chain that can lead to performance benefits with CA techniques, providing insights into the general feasibility and profitability of using the optimizations for this class of applications.
Smart Cache Insertion and Promotion Policy for Content Delivery Networks

Improving hit rates can be achieved by enhancing cache replacement algorithms with the identification of zero-reuse objects (ZROs) and inserting them at the end of the cache queue. Note that the promotion policy needs to achieve a similar task as the above insertion policy since the hit object may immediately become a ZRO (called P-ZRO) that is not suitable for placement at the front of the queue. However, existing studies have yet to consider P-ZROs, and current insertion algorithms struggle to simultaneously identify both ZROs and P-ZROs. To address these issues, we propose integrating the insertion and promotion policies. We do this by treating hit objects as special missing objects and employing reinforcement learning to create a unified model for both policies, where the learning function recognizes the relationship between performance changes and the emergence of ZROs and P-ZROs. Our proposed solution is a smart cache insertion and promotion policy (SCIP) that dynamically adjusts the insertion position using a bimodal insertion policy for both missing and hit objects, guided by the model. Extensive experiments demonstrate that SCIP significantly improves overall performance in real-world content delivery network systems and outperforms state-of-the-art insertion policies in terms of miss ratios in the simulator. In addition, deploying SCIP on optimal cache replacement algorithms can further decrease their miss ratios.
User-Aware Rendering: Merging the Strengths of Device- and User-Perspective Rendering in Handheld AR

In handheld AR, users have only a small screen to see the augmented scene, making decisions about scene layout and rendering techniques crucial. Traditional device-perspective rendering (DPR) uses the device camera's full field of view, enabling fast scene exploration, but ignoring what the user sees around the device screen. In contrast, user-perspective rendering (UPR) emulates the feeling of looking through the device like a glass pane, which enhances depth perception, but severely limits the field of view in which virtual objects are displayed, impeding scene exploration and search. We introduce the notion of User-Aware Rendering. By following the principles of UPR, but pretending the device is larger than it actually is, it combines the strengths of UPR and DPR. We present two studies showing that User-Aware AR imitating a 50% larger device successfully achieves both enhanced depth perception and fast scene exploration in typical search and selection tasks.
Landslides: Processes, Prediction, and Land Use

Published by the American Geophysical Union as part of the Water Resources Monograph Series, Volume 18. Landslides are a constant in shaping our landscape. Whether by large episodic, or smaller chronic, mass movements, our mountains, hills, valleys, rivers, and streams bear evidence of change from landslides. Combined with anthropogenic factors, especially the development and settlement of unstable terrain, landslides (as natural processes) have become natural disasters. This book charts our understanding of landslide processes, prediction methods, and related land use issues. How and where do landslides initiate? What are the human and economic consequences? What hazard assessment and prediction methods are available, and how well do they work? How does land use, from timber harvesting and road building to urban and industrial development, affect landslide distribution in time and space? And what is the effect of land use and climate change on landslides? [Book Synopsis]
Seeding Activity of Skin Misfolded Tau as a Novel Biomarker for Tauopathies

Tauopathies are a group of age-related neurodegenerative diseases with a molecular hallmark of the prion-like propagation and accumulation of pathologically phosphorylated tau protein in the brain. They include Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and Pick’s disease (PiD). Currently, in the peripheral tissues and body fluids there are no reliable diagnostic biomarkers available that are able to directly reflect the capability of propagation and spreading of the misfolded tau aggregates. Here, we revealed significantly increased amounts of phosphorylated tau in the skin of AD patients compared to those in other tauopathies and normal controls. Moreover, the seed-amplification assay (SAA) by the ultrasensitive real-time quaking-induced conversion (RT-QuIC) displayed that the prion-like seeding activity of pathological tau in the skin of cadavers with neuropathologically confirmed tauopathies including AD, PSP, CBD, PiD was dramatically higher than that in normal controls, yielding 75-80% sensitivity and 95-100% specificity, respectively, depending on different tau substrates used. The increased tau-seeding activity was also observed in biopsy skin samples from living AD and PSP patients. Moreover, analysis of the end products of skin-tau SAA confirmed that the increased seeding activity is accompanied with formation of tau aggregates that are of different physicochemical properties determined by the different tau-substrates used. Our study provides proof-of-concept that the skin tau-SAA can differentiate tauopathies from normal controls, suggesting that the seeding activity of the skin misfolded tau can serve as an accurate diagnostic biomarker of tauopathies.
Reply to Dahl: Moral Content is Varied, and Premature Definitions Should Not Constrain It

Reply to Dahl: Moral Content is Varied, and Premature Definitions Should Not Constrain It Roger Giner-Sorolla, Simon Myers & Joshua Rottman To cite this article: Roger Giner-Sorolla, Simon Myers & Joshua Rottman (2023) Reply to Dahl: Moral Content is Varied, and Premature Definitions Should Not Constrain It, Psychological Inquiry, 34:2, 86-90, DOI: 10.1080/1047840X.2023. 2248862 To link to this article: https://doi.org/10.1080/1047840X.2023. 2248862
Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.
Sperm competition influences sperm quality and seminal plasma protein expression among territorial males of the courtship cichlid

Sperm competition drives the plasticity of sperm quality and seminal fluid. However, how environmental and behavioral factors influence sperm competition and its outcome on the plasticity of sperm quality are unknown. In this study, we show males can asses the sperm competition level, and their fertilization-promoting sperm traits changes according to intensity. The promiscuous Tanganyikan cichlids Ophthalmotilapia ventralis perform their fertilization in the female mouth cavity. Sperm competition occurs in the female mouth as sperm is collected from multiple males. Behavioral (i.e., courtship success rates [CSR]) and environmental (density of spawning sites [DS]) factors are related to sperm collection. Seminal plasma glycoprotein 120 (SPP120) aids in sperm immobilization and aggregation, and sperm quality (longevity and velocity) could increase the probability of siring offspring. Based on an assumption of the involvement of DS and CSR on sperm collection, DS and CSR could influence the expression of SPP120 and sperm quality. We examined how sperm quality and SPP120 affect sperm competition-associated CSR and DS. Sperm longevity and velocity were positively correlated with DS. SPP120 expression was also positively correlated with CSR and DS. The plasticity of sperm quality and amount of SPP120 result in high fertilization competency according to sperm competition level.
Translational modulation by ISRIB alleviates synaptic and behavioral phenotypes in Fragile X syndrome

Fragile X syndrome (FXS) is caused by the functional loss of Fragile X messenger ribonucleoprotein (FMRP), a translational regulator that binds the transcripts of many proteins involved in synaptic function and plasticity. Dysregulated protein synthesis is a central effect of FMRP loss and to date direct translational modulation has not been leveraged in the treatment of FXS. Thus, we examined the effect of the small molecule translational modulator, Integrated Stress Response Inhibitor (ISRIB), in treating synaptic and behavioral symptoms of FXS. Using a combination of population-level single synapse analysis, in vivo two-photon microscopy, and behavioral analysis, we revealed a mechanism for the appearance of dense and immature dendritic spines, which are observed in human FXS patients and Fmr1 knockout (KO) mice. Our data suggest that FMRP loss at the synapse dysregulates synaptic protein abundance and that spines are disproportionately stabilized through increased PSD-95 expression but prevented from maturing through reduced synaptic accumulation of glutamate receptors. ISRIB rescues these molecular and synaptic deficits in FXS and improves social recognition in Fmr1 KO mice. These findings highlight the therapeutic potential of targeting core translational mechanisms and the differential impact of FMRP on synaptic and cellular function in FXS and neurodevelopmental disorders more broadly.