bluuebunny/crossref_metadata_2025 huggingface dataset:
Geometrical correspondence of the Miura transformation induced from affine Kac–Moody algebras
Authors: Qu Changzheng , Wu Zhiwei
Published: September 2023

The Miura transformation plays a crucial role in the study of integrable systems. There have been various extensions of the Miura transformation, which have been used to relate different kinds of integrable equations and to classify the bi‐Hamiltonian structures. In this paper, we are mainly concerned with the geometric aspects of the Miura transformation. The generalized Miura transformations from the mKdV‐type hierarchies to the KdV‐type hierarchies are constructed under both algebraic and geometric settings. It is shown that the Miura transformations not only relate integrable curve flows in different geometries but also induce the transition between different moving frames. Moreover, the Miura transformation gives the factorization of generating operators of constraint Gelfand–Dickey hierarchy. Other geometric formulations are also investigated.
Phytocystatin 6 is a context‐dependent, tight‐binding inhibitor of Arabidopsis thaliana legumain isoform β
Authors: Santos Naiá P. , Soh Wai Tuck , Demir Fatih , Tenhaken Raimund , Briza Peter , Huesgen Pitter F. , Brandstetter Hans , Dall Elfriede
Published: September 2023

Plant legumains are crucial for processing seed storage proteins and are critical regulators of plant programmed cell death. Although research on legumains boosted recently, little is known about their activity regulation. In our study, we used pull‐down experiments to identify AtCYT6 as a natural inhibitor of legumain isoform β (AtLEGβ) in Arabidopsis thaliana. Biochemical analysis revealed that AtCYT6 inhibits both AtLEGβ and papain‐like cysteine proteases through two separate cystatin domains. The N‐terminal domain inhibits papain‐like proteases, while the C‐terminal domain inhibits AtLEGβ. Furthermore, we showed that AtCYT6 interacts with legumain in a substrate‐like manner, facilitated by a conserved asparagine residue in its reactive center loop. Complex formation was additionally stabilized by charged exosite interactions, contributing to pH‐dependent inhibition. Processing of AtCYT6 by AtLEGβ suggests a context‐specific regulatory mechanism with implications for plant physiology, development, and programmed cell death. These findings enhance our understanding of AtLEGβ regulation and its broader physiological significance.
Combination phacoemulsification and pars plana vitrectomy for retinal reattachment surgery in the Siberian Husky breed
Authors: Chen Tiffany M. , Sapienza John S. , Nadelstein Brad , Palmer‐Greenberg Samantha , Kim Kay
Published: September 2023

Objective

To report the success rate and complications of combined phacoemulsification and pars plana vitrectomy (PPV) for treatment of cataracts and retinal detachment in the Siberian Husky breed.

Animals Studied

Client‐owned Siberian Husky dogs that underwent combined phacoemulsification and PPV at two veterinary referral centers.

Procedure

Retrospective study of 16 Siberian Husky dogs that underwent combined phacoemulsification and PPV with a minimum 3‐month postoperative follow‐up. Signalment and preoperative ophthalmic examination findings, intraoperative findings, and postoperative visual status and complications were recorded. Functional success was defined as the maintenance or restoration of vision.

Results

Seventeen eyes of 16 dogs were evaluated. Immediate postoperative anatomic success was achieved in all 17 eyes (100%), with functional success through the last known follow‐up examination achieved in 88.2% of operated eyes (15/17). The most common postoperative complication was silicone oil migration into the anterior chamber (AC‐SiO migration), occurring in 47.1% of eyes (8/17), followed by corneal endothelial decompensation and glaucoma each occurring in 17.6% of eyes (3/17).

Conclusions

Combined phacoemulsification and PPV is a viable option in Siberian Husky dogs with cataracts and preoperative retinal detachment. Visual success was achieved in 88.2% of eyes, with the most common postoperative complication being AC‐SiO migration.
Testing a tine weeder with artificial weeds in the controlled conditions of a soil bin
Authors: Parks Jordan W. , Gallandt Eric R.
Published: September 2023

Farmers adjust physical weed control (PWC) tools to optimize performance based on soil conditions, as well as the size and species of weeds and crop. These many variables make PWC field research challenging. Research in controlled conditions would reduce variability and allow more rapid testing of PWC tools. To this end, we constructed a soil bin with a mobile tool carriage inside of a heated glasshouse. The soil bin was 2 m wide by 12 m long by 1 m high. The mobile tool carriage can operate PWC tools at 0.4–19.0 km h−1. Research methods were developed using a tine weeding tool and artificial weeds (AWs). The tine weeder offered uniform soil disturbance and, based on observation, has both uprooting and burial modes of action. Tine speed, angle and depth were evaluated in replicated soil bin and field experiments. Efficacy using AWs in soil bin was similar to efficacy based on surrogate and a real weed in one field experiment, but much greater than values measured in a second field experiment. Despite this inconsistency, we think that the simplified conditions of the soil bin system may be useful to test and prioritize tool settings or adjustments for field studies.
The Influence of Social Media on the Transparency and Accountability of the South African Public Sector
Authors: Masiya Tyanai , Lubinga Stellah
Published: September 2023

There is a growing debate on the potential role of social media in improving government transparency and accountability and, ultimately, policy-making. This article analyses the contribution of social media, specifically Twitter, in increasing government transparency and accountability in South Africa by transferring information between the government and the public. To achieve this aim, we conducted an in-depth analysis of Twitter content, focusing on a curated selection of randomly selected top hashtags that became prominent in the South African context. These hashtags represent notable trends such as #FeesMustFall, #ZumaMustFall, #NotInMyName, #AmINext, and the #ThaboBester saga. By examining the discourses surrounding these hashtags, we aimed to uncover insights into how social media interactions intersected with government actions and reactions. Our results indicate that these hashtags served as potent catalysts for public engagement, driving discussions that critiqued government decisions and compelled timely reactions from the government.
Working with Passion: Digital Media Specialist as Precarious Worker in the Digital Age
Authors: Annisa Firly , Suwarto Dyna Herlina
Published: September 2023

This paper addresses issues the global concern of uncertainties of digital creative workers, emphasizing Indonesian case. This paper argues creative digital workers in digital media industry deal with uncertainty in "atypical," "contingent," "temporary," "informal," and, in particular, "precarious" settings. This research will provide arguments about new forms of labour exploitation in the era of “user-generated content” and “prosumption”. This paper's primary theoretical contribution is to clarify the precarious nature of labor for digital labor in communication and media organizations, with a focus on creative workers in Yogyakarta and Jakarta. Additionally, this study will demonstrate how staff members in various communication and media companies routinely work under deadline pressure, work remotely, have short-term contracts, and participate in hustle cult practices. This study used qualitative and in-depth interview studies to look ten people at the lives of digital content officers in a social media specialist (as new media professionals) who produce digital content in social media, managing content for influencers, internet celebrities, and corporations. This research took place between January and June 2022, and it raised fundamental concerns regarding how these industries develop the rhetoric of flexibility, autonomy, and informality.
Post's Correspondence Problem for hyperbolic and virtually nilpotent groups
Authors: Ciobanu Laura , Levine Alex , Logan Alan D.
Published: September 2023

Post's Correspondence Problem (the PCP) is a classical decision problem in theoretical computer science that asks whether for pairs of free monoid morphisms there exists any non‐trivial such that . PCP for a group takes pairs of group homomorphisms instead, and similarly asks whether there exists an such that holds for non‐elementary reasons. The restrictions imposed on in order to get non‐elementary solutions lead to several interpretations of the problem; we mainly consider the natural restriction asking that and prove that the resulting interpretation of the PCP is undecidable for arbitrary hyperbolic , but decidable when is virtually nilpotent, en route also studying this problem for finite extensions. We also consider a different interpretation of the PCP due to Myasnikov, Nikolaev and Ushakov, proving decidability for torsion‐free nilpotent groups.
Mothers as Others in Collegiate Athletic Departments: The Impact of a Gendered Organization on Women Coaches
Authors: Siegele Jessica , Taylor Elizabeth , Saxe Kelsie , Smith Allison
Published: September 2023

Work–life conflict and the underrepresentation of women in college coaching have been widely examined topics in sport research. However, more limited attention has been devoted to exploring the influence of parental status on the careers of coaches. The purpose of the study was to understand the experiences of women who voluntarily left the coaching profession because of its perceived incompatibility with motherhood. Utilizing Acker’s Theory of Gendered Organizations framework, the current study interviewed six former National Collegiate Athletic Association women coaches whose collegiate coaching careers ended prematurely due to the difficulty in balancing parental and professional responsibilities. Using interpretative phenomenological analysis, three high-order themes were constructed: (a) no space for women; (b) getting out, wanting to be in; and (c) impact of gendered society. Findings indicate that women coaches with children experience unique barriers and challenges, which can ultimately lead to women exiting the college coaching profession. Findings dispel the myth that women “don’t want to coach” and implicate the compounding stress of gender roles in the family and broader society.
The Influence of Social Support Provider on Adolescent Athlete Burnout and Engagement
Authors: Mellano Kathleen T. , Pacewicz Christine
Published: September 2023

The purpose of the current research was to examine the interaction of teammate and coach support on athlete burnout and engagement, and determine how type of teammate and coach support links with athlete burnout and engagement. Adolescent athletes (N = 176) completed measures of support, burnout, and engagement. Total teammate and coach support accounted for 14.9%–26.0% and 22.4%–36.7% explained variance across dimensions of burnout and engagement, respectively. No meaningful significant interaction was found. Teammate esteem support predicted reduced accomplishment, devaluation, confidence, and vigor (β = −0.31, −0.36, 0.28, and 0.28), while emotional and tangible support predicted dedication (β = 0.29 and 0.20) and enthusiasm (β = 0.30 and 0.22). Coach esteem support predicted reduced accomplishment (β = −0.59) and devaluation (β = −0.27). Findings suggest that support from one key agent can enhance youth athlete well-being, but types of support provided by agents differ in salience.
Sequentially coupled thermal-hydraulic-mechanical simulation for geomechanical assessments of caprock integrity in SAGD
Authors: Zhang Bo , Chalaturnyk Rick , Boisvert Jeff
Published: September 2023

Oil sand reservoirs and caprock undergo deformations triggered by pore pressure increases and thermal induced stresses during the steam-assisted-gravity-drainage (SAGD) processes. Geomechanical assessments are mandated by energy regulators to evaluate the caprock integrity and ensure the safe SAGD operations. Commercial reservoir simulation packages started to incorporate geomechanical effects when predicting flow response; however, these geomechanical modules are not able to correctly model the plastic deformations caused by thermal-hydraulic-mechanical (THM) interactions, which has a first order effect on predicting steam chamber propagation and evaluating caprock integrity. An integrated coupled THM modeling methodology is proposed here to improve the modeling of reservoir deformations and caprock integrity in a heterogeneous oil sand reservoir with interbedded shale barriers. The pressure and temperature front are found to propagate at different speed and that dominate the elastic and plastic deformations caused by changes of shear and mean effective stress. Therefore, four stages are divided in the SAGD process that can be interpretations of changes in stress paths including buildup of pore pressure, generation and dissipation of thermal induced stresses. The response surfaces of minimum factor of safety are introduced and computed to provide a conservative estimate for caprock integrity during SAGD of a heterogeneous reservoir with multiple layers of caprocks in Athabasca oil sands.
The validity of Birkhoff’s theorem in modified teleparallel equivalent theories of gravity
Authors: Cheng Ji-Yu
Published: September 2023

Birkhoff’s theorem is a vitally important theorem in studying local systems in theories of gravity. In this paper we provide a straightforward proof of the validity of Birkhoff’s theorem in modified teleparallel equivalent theories of gravity, which states that if the matter distribution is spherically symmetric, then the spacetime is static in a region in which t remains timelike and $$(r,\theta ,\phi )$$

(
r
,
θ
,
ϕ
)

stay spacelike. This version of Birkhoff’s theorem is rather trivial. The theorem also reveals the fact that the gravitational field of a spherically symmetric distribution of matter and of the spherically symmetric vacuum spacetime are both static in modified teleparallel equivalent theories of gravity.
BASS: Safe Deep Tissue Optical Sensing for Wearable Embedded Systems
Authors: Vali Kourosh , Vafi Ata , Kasap Begum , Ghiasi Soheil
Published: September 2023

In wearable optical sensing applications whose target tissue is not superficial, such as deep tissue oximetry, the task of embedded system design has to strike a balance between two competing factors. On one hand, the sensing task is assisted by increasing the radiated energy into the body, which in turn, improves the signal-to-noise ratio (SNR) of the deep tissue at the sensor. On the other hand, patient safety consideration imposes a constraint on the amount of radiated energy into the body. In this paper, we study the trade-offs between the two factors by exploring the design space of the light source activation pulse.

Furthermore, we propose BASS, an algorithm that leverages the activation pulse design space exploration, which further optimizes deep tissue SNR via spectral averaging, while ensuring the radiated energy into the body meets a safe upper bound. The effectiveness of the proposed technique is demonstrated via analytical derivations, simulations, and
in vivo
measurements in both pregnant sheep models and human subjects.
STADIA: Photonic Stochastic Gradient Descent for Neural Network Accelerators
Authors: Xia Chengpeng , Chen Yawen , Zhang Haibo , Wu Jigang
Published: September 2023

Deep Neural Networks (DNNs) have demonstrated great success in many fields such as image recognition and text analysis. However, the ever-increasing sizes of both DNN models and training datasets make deep leaning extremely computation- and memory-intensive. Recently, photonic computing has emerged as a promising technology for accelerating DNNs. While the design of photonic accelerators for DNN inference and forward propagation of DNN training has been widely investigated, the architectural acceleration for equally important backpropagation of DNN training has not been well studied. In this paper, we propose a novel silicon photonic-based backpropagation accelerator for high performance DNN training. Specifically, a general-purpose photonic gradient descent unit named STADIA is designed to implement the multiplication, accumulation, and subtraction operations required for computing gradients using mature optical devices including Mach-Zehnder Interferometer (MZI) and Mircoring Resonator (MRR), which can significantly reduce the training latency and improve the energy efficiency of backpropagation. To demonstrate efficient parallel computing, we propose a STADIA-based backpropagation acceleration architecture and design a dataflow by using wavelength-division multiplexing (WDM). We analyze the precision of STADIA by quantifying the precision limitations imposed by losses and noises. Furthermore, we evaluate STADIA with different element sizes by analyzing the power, area and time delay for photonic accelerators based on DNN models such as AlexNet, VGG19 and ResNet. Simulation results show that the proposed architecture STADIA can achieve significant improvement by 9.7× in time efficiency and 147.2× in energy efficiency, compared with the most advanced optical-memristor based backpropagation accelerator.
AxOTreeS
: A

Tree

S

earch Approach to Synthesizing FPGA-based

A

ppro

x

imate

O

perators
Authors: Sahoo Siva Satyendra , Ullah Salim , Kumar Akash
Published: September 2023

Approximate computing (AxC) provides the scope for achieving disproportionate gains in a system’s power, performance, and area (PPA) metrics by leveraging an application’s inherent error-resilient behavior (BEHAV). Trading computational accuracy for performance gains makes AxC an attractive proposition for implementing computationally complex AI/ML-based applications on resource-constrained embedded systems. The growing diversity of application domains using AI/ML has also led to the increasing usage of FPGA-based embedded systems. However, implementing AxC for FPGAs has primarily been limited to the post-processing of ASIC-optimized approximate operators (AxOs). This approach usually involves selecting from a set of AxOs that have been optimized for a gate-based implementation in an ASIC. While such an approach does allow leveraging existing knowledge of ASIC-based AxO design, it limits the scope for considering the challenges and opportunities associated with FPGA’s LUT-based computation structures. Similarly, the few works considering the LUT-based computing for AxO design use generic optimization approaches that do not allow integrating problem-specific prior knowledge—empirical and/or statistical. To this end, we propose a novel tree search-based approach to AxO synthesis for FPGAs. Specifically, we present a design methodology using Monte Carlo Tree Search (MCTS)-based search tree traversal that allows the designer to integrate statistical data, such as correlation, into the AxOs optimization. With the proposed methods, we report improvements over standard MCTS algorithm-based results as well as improved hypervolume for both operator-level and application-specific DSE, compared to state-of-the-art design methodologies.
Protection Window Based Security-Aware Scheduling against Schedule-Based Attacks
Authors: Ren Jiankang , Liu Chunxiao , Lin Chi , Bi Ran , Li Simeng , Wang Zheng , Qian Yicheng , Zhao Zhichao , Tan Guozhen
Published: September 2023

With widespread use of common-off-the-shelf components and the drive towards connection with external environments, the real-time systems are facing more and more security problems. In particular, the real-time systems are vulnerable to the schedule-based attacks because of their predictable and deterministic nature in operation. In this paper, we present a security-aware real-time scheduling scheme to counteract the schedule-based attacks by preventing the untrusted tasks from executing during the attack effective window (AEW). In order to minimize the AEW untrusted coverage ratio for the system with uncertain AEW size, we introduce the protection window to characterize the system protection capability limit due to the system schedulability constraint. To increase the opportunity of the priority inversion for the security-aware scheduling, we design an online feasibility test method based on the busy interval analysis. In addition, to reduce the run-time overhead of the online feasibility test, we also propose an efficient online feasibility test method based on the priority inversion budget analysis to avoid online iterative calculation through the offline maximum slack analysis. Owing to the protection window and the online feasibility test, our proposed approach can efficiently provide best-effort protection to mitigate the schedule-based attack vulnerability while ensuring system schedulability. Experiments show the significant security capability improvement of our proposed approach over the state-of-the-art coverage oriented scheduling algorithm.
PReFeR :

P

hysically

Re

lated

F

unction bas

e

d

R

emote Attestation Protocol
Authors: Mondal Anupam , Gangopadhyay Shreya , Chatterjee Durba , Boyapally Harishma , Mukhopadhyay Debdeep
Published: September 2023

Remote attestation is a request-response based security service that permits a trusted entity (verifier) to check the current state of an untrusted remote device (prover). The verifier initiates the attestation process by sending an attestation challenge to the prover; the prover responds with its current state, which establishes its trustworthiness. Physically Unclonable Function (PUF) offers an attractive choice for hybrid attestation schemes owing to its low overhead security guarantees. However, this comes with the limitation of secure storage of the PUF model or large challenge-response database on the verifier end. To address these issues, in this work, we propose a hybrid attestation framework, named
PReFeR
, that leverages a new class of hardware primitive known as Physically Related Function (PReF) to remotely attest low-end devices without the requirement of secure storage or heavy cryptographic operations. It comprises a static attestation scheme that validates the memory state of the remote device prior to code execution, followed by a dynamic run-time attestation scheme that asserts the correct code execution by evaluating the content of special registers present in embedded systems, known as hardware performance counters (HPC). The use of HPCs in the dynamic attestation scheme mitigates the popular class of attack known as the time-of-check-time-of-use (TOCTOU) attack, which has broken several state-of-the-art hybrid attestation schemes. We demonstrate our protocol and present our experimental results using a prototype implementation on Digilent Cora Z7 board, a low-cost embedded platform, specially designed for IoT applications.
DTRL: Decision Tree-based Multi-Objective Reinforcement Learning for Runtime Task Scheduling in Domain-Specific System-on-Chips
Authors: Basaklar Toygun , Goksoy A. Alper , Krishnakumar Anish , Gumussoy Suat , Ogras Umit Y.
Published: September 2023

Domain-specific systems-on-chip (DSSoCs) combine general-purpose processors and specialized hardware accelerators to improve performance and energy efficiency for a specific domain. The optimal allocation of tasks to processing elements (PEs) with minimal runtime overheads is crucial to achieving this potential. However, this problem remains challenging as prior approaches suffer from non-optimal scheduling decisions or significant runtime overheads. Moreover, existing techniques focus on a single optimization objective, such as maximizing performance. This work proposes DTRL, a decision-tree-based multi-objective reinforcement learning technique for runtime task scheduling in DSSoCs. DTRL trains a single global differentiable decision tree (DDT) policy that covers the entire objective space quantified by a preference vector. Our extensive experimental evaluations using our novel reinforcement learning environment demonstrate that DTRL captures the trade-off between execution time and power consumption, thereby generating a Pareto set of solutions using a single policy. Furthermore, comparison with state-of-the-art heuristic–, optimization–, and machine learning-based schedulers shows that DTRL achieves up to 9× higher performance and up to 3.08× reduction in energy consumption. The trained DDT policy achieves 120 ns inference latency on Xilinx Zynq ZCU102 FPGA at 1.2 GHz, resulting in negligible runtime overheads. Evaluation on the same hardware shows that DTRL achieves up to 16% higher performance than a state-of-the-art heuristic scheduler.
CIM: A Novel Clustering-based Energy-Efficient Data Imputation Method for Human Activity Recognition
Authors: Hussein Dina , Bhat Ganapati
Published: September 2023

Human activity recognition (HAR) is an important component in a number of health applications, including rehabilitation, Parkinson’s disease, daily activity monitoring, and fitness monitoring. State-of-the-art HAR approaches use multiple sensors on the body to accurately identify activities at runtime. These approaches typically assume that data from all sensors are available for runtime activity recognition. However, data from one or more sensors may be unavailable due to malfunction, energy constraints, or communication challenges between the sensors. Missing data can lead to significant degradation in the accuracy, thus affecting quality of service to users. A common approach for handling missing data is to train classifiers or sensor data recovery algorithms for each combination of missing sensors. However, this results in significant memory and energy overhead on resource-constrained wearable devices. In strong contrast to prior approaches, this paper presents a clustering-based approach (CIM) to impute missing data at runtime. We first define a set of possible clusters and representative data patterns for each sensor in HAR. Then, we create and store a mapping between clusters across sensors. At runtime, when data from a sensor are missing, we utilize the stored mapping table to obtain most likely cluster for the missing sensor. The representative window for the identified cluster is then used as imputation to perform activity classification. We also provide a method to obtain imputation-aware activity prediction sets to handle uncertainty in data when using imputation. Experiments on three HAR datasets show that CIM achieves accuracy within 10% of a baseline without missing data for one missing sensor when providing single activity labels. The accuracy gap drops to less than 1% with imputation-aware classification. Measurements on a low-power processor show that CIM achieves close to 100% energy savings compared to state-of-the-art generative approaches.
Optimal Synthesis of Robust IDK Classifier Cascades
Authors: Baruah Sanjoy , Burns Alan , Davis Robert Ian
Published: September 2023

An
IDK classifier
is a computing component that categorizes inputs into one of a number of classes, if it is able to do so with the required level of confidence, otherwise it returns “I Don’t Know” (IDK).
IDK classifier cascades
have been proposed as a way of balancing the needs for fast response and high accuracy in classification-based machine perception. Efficient algorithms for the synthesis of IDK classifier cascades have been derived; however, the responsiveness of these cascades is highly dependent on the accuracy of predictions regarding the run-time behavior of the classifiers from which they are built. Accurate predictions of such run-time behavior is difficult to obtain for many of the classifiers used for perception. By applying the
algorithms using predictions
framework, we propose efficient algorithms for the synthesis of IDK classifier cascades that are
robust
to inaccurate predictions in the following sense: the IDK classifier cascades synthesized by our algorithms have short expected execution durations when the predictions are accurate, and these expected durations increase only within specified bounds when the predictions are inaccurate.
A Constructive State-based Semantics and Interpreter for a Synchronous Data-flow Language with State Machines
Authors: Colaço Jean-Louis , Mendler Michael , Pauget Baptiste , Pouzet Marc
Published: September 2023

Scade is a domain-specific synchronous functional language used to implement safety-critical real-time software for more than twenty years. Two main approaches have been considered for its semantics: (i) an
indirect collapsing semantics
based on a source-to-source translation of high-level constructs into a data-flow core language whose semantics is precisely specified and is the entry for code generation; a
relational synchronous semantics
, akin to Esterel, that applies
directly
to the source. It defines what is a valid synchronous reaction but hides, on purpose, if a semantics exists, is unique and can be computed; hence, it is not executable.

This paper presents, for the first time, an
executable
, state-based semantics for a language that has the key constructs of Scade all together, in particular the arbitrary combination of data-flow equations and hierarchical state machines. It can apply
directly
to the source language before static checks and compilation steps. It is
constructive
in the sense that the language in which the semantics is defined is a statically typed functional language with call-by-value and strong normalization, e.g., it is expressible in a proof-assistant where all functions terminate. It leads to a reference, purely functional, interpreter. This semantics is modular and can account for possible errors, allowing to establish what property is ensured by each static verification performed by the compiler. It also clarifies how causality is treated in Scade compared with Esterel.

This semantics can serve as an oracle for compiler testing and validation; to prototype novel language constructs before they are implemented, to execute possibly unfinished models or that are correct but rejected by the compiler; to prove the correctness of compilation steps.

The semantics given in the paper is implemented as an interpreter in a purely functional style, in OCaml.
EMS-i
: An Efficient Memory System Design with Specialized Caching Mechanism for Recommendation Inference
Authors: Wang Yitu , Li Shiyu , Zheng Qilin , Chang Andrew , Li Hai , Chen Yiran
Published: September 2023

Recommendation systems have been widely embedded into many Internet services. For example, Meta’s deep learning recommendation model (DLRM) shows high prefictive accuracy of click-through rate in processing large-scale embedding tables. The SparseLengthSum (SLS) kernel of the DLRM dominates the inference time of the DLRM due to intensive irregular memory accesses to the embedding vectors. Some prior works directly adopt near data processing (NDP) solutions to obtain higher memory bandwidth to accelerate SLS. However, their inferior memory hierarchy induces low performance-cost ratio and fails to fully exploit the data locality. Although some software-managed cache policies were proposed to improve the cache hit rate, the incurred cache miss penalty is unacceptable considering the high overheads of executing the corresponding programs and the communication between the host and the accelerator. To address the issues aforementioned, we propose
EMS-i
, an efficient memory system design that integrates Solide State Drive (SSD) into the memory hierarchy using Compute Express Link (CXL) for recommendation system inference. We specialize the caching mechanism according to the characteristics of various DLRM workloads and propose a novel prefetching mechanism to further improve the performance. In addition, we delicately design the inference kernel and develop a customized mapping scheme for SLS operation, considering the multi-level parallelism in SLS and the data locality within a batch of queries. Compared to the state-of-the-art NDP solutions,
EMS-i
achieves up to 10.9× speedup over RecSSD and the performance comparable to RecNMP with 72% energy savings.
EMS-i
also saves up to 8.7× and 6.6 × memory cost w.r.t. RecSSD and RecNMP, respectively.
Overflow-free Compute Memories for Edge AI Acceleration
Authors: Ponzina Flavio , Rios Marco , Levisse Alexandre , Ansaloni Giovanni , Atienza David
Published: September 2023

Compute memories are memory arrays augmented with dedicated logic to support arithmetic. They support the efficient execution of data-centric computing patterns, such as those characterizing Artificial Intelligence (AI) algorithms. These architectures can provide computing capabilities as part of the memory array structures (In-Memory Computing, IMC) or at their immediate periphery (Near-Memory Computing, NMC). By bringing the processing elements inside (or very close to) storage, compute memories minimize the cost of data access. Moreover, highly parallel (and, hence, high-performance) computations are enabled by exploiting the regular structure of memory arrays. However, the regular layout of memory elements also constrains the data range of inputs and outputs, since the bitwidths of operands and results stored at each address cannot be freely varied. Addressing this challenge, we herein propose a HW/SW co-design methodology combining careful per-layer quantization and inter-layer scaling with lightweight hardware support for overflow-free computation of dot-vector operations. We demonstrate their use to implement the convolutional and fully connected layers of AI models. We embody our strategy in two implementations, based on IMC and NMC, respectively. Experimental results highlight that an area overhead of only 10.5% (for IMC) and 12.9% (for NMC) is required when interfacing with a 2KB subarray. Furthermore, inferences on benchmark CNNs show negligible accuracy degradation due to quantization for equivalent floating-point implementations.
Intelligent Cooperative Caching at Mobile Edge based on Offline Deep Reinforcement Learning
Authors: Wang Zhe , Hu Jia , Min Geyong , Zhao Zhiwei
Published: September 2023

Cooperative edge caching enables edge servers to jointly utilize their cache to store popular contents, thus drastically reducing the latency of content acquisition. One fundamental problem of cooperative caching is how to coordinate the cache replacement decisions at edge servers to meet users’ dynamic requirements and avoid caching redundant contents. Online deep reinforcement learning (DRL) is a promising way to solve this problem by learning a cooperative cache replacement policy using continuous interactions (trial and error) with the environment. However, the sampling process of the interactions is usually expensive and time-consuming, thus hindering the practical deployment of online DRL-based methods. To bridge this gap, we propose a novel Delay-awarE Cooperative cache replacement method based on Offline deep Reinforcement learning (DECOR), which can exploit the existing data at the mobile edge to train an effective policy while avoiding expensive data sampling in the environment. A specific convolutional neural network is also developed to improve the training efficiency and cache performance. Experimental results show that DECOR can learn a superior offline policy from a static dataset compared to an advanced online DRL-based method. Moreover, the learned offline policy outperforms the behavior policy used to collect the dataset by up to 35.9%.
Nonsingular Fast Terminal Sliding Mode Neural Network Decentralized Control of a Quadrotor Unmanned Aerial Vehicle
Authors: Mao Yuqing , Chen Jing
Published: September 2023

A nonsingular terminal sliding mode decentralized controller that can ensure the tracking errors of the trajectories and attitude rapid convergence in finite time is proposed for an insufficient driven and strongly coupled nonlinear four-rotor unmanned aerial vehicle (UAV). The total lift of the UAV system is decomposed into three virtual drive separation forces corresponding to the three positions. The insufficient drive UAV system is transformed into a virtual full-drive model for research. The three position states and the three attitude states of UAV are placed correspondingly to the six subsystems by variable substitution. The model uncertainty and unknown disturbance term for each subsystem serves as total coupling terms among the subsystems. The upper bounds of the total coupling terms are considered as unknown ordinary higher order polynomials varying with the six states of the system under the action of time change. With the help of Cauchy inequality, the estimates of the upper bounds are obtained from the approximation performance of the RBF neural network. Finally, the decentralized controller is designed for each attitude subsystem and the virtual decentralized controller for each position subsystem. It is also mapped to the tracking total lift controller by using the virtual decentralized position controller. The controller design process uses the nonsingular terminal sliding mode control technology to ensure that the quadrotor attitude and position variables can quickly converge to the desired value in a short time. Simulation experiments verify that the proposed control method is effective and feasible.
Effect of Temperature on Interface Characteristics between Compacted Recycled Asphalt Pavement and Geogrid
Authors: Liu Lin , Zhang Zhaoer , Zhang Lexuan , Tang Yong , Yin Jie
Published: September 2023

This study presents an experimental investigation of the interface behaviors of recycled asphalt pavement (RAP) reinforced with biaxial geogrid. A series of direct shear tests were conducted on compacted RAP specimens with or without geogrid under different normal stresses (50, 75, and 100 kPa) and test temperatures (0, 20, and 35°C) using an improved temperature-controlled direct shear apparatus. The effect of test temperature on interface shear strength and strength parameters was systematically examined. Test results showed that shear stress versus shear displacement curves of RAP specimens with or without geogrid show strain-softening characteristics. Under the same normal stress, the curve of the reinforced RAP sample with a high temperature is always below that with a low temperature, which indicates the test temperature has an adverse effect on the strength of the reinforced RAP sample. The shear strength consistently increases linearly with the increase of applied normal stress for all RAP samples. The shear strength with geogrid is greater than pure RAP under a given test temperature and normal stress. The shear strength of RAP samples at lower test temperatures is higher than that at a higher temperature. Both apparent adhesion intercept of RAP-geogrid and cohesion of RAP show a decreasing trend with the increase of the test temperature and tend to be stable with increasing temperature. Both the interface friction angle of RAP-geogrid and the internal friction angle of RAP slightly decrease with the increase of test temperature. For RAP-geogrid, the value of the apparent adhesion intercept is higher than that for sand and gravel with geosynthetics. The interface friction angle is close to that of sand and gravel with geosynthetics. Therefore, RAP material can be well used as alternative backfill materials in lieu of natural aggregates such as gravel and sand in geotechnical applications.
Fabrication of Microcapsule-Type Long-Lasting Phosphorescent Sensor for Concrete Crack Monitoring
Authors: Zhao Yayun , Li Yao , Wang Qing , Zhang Haohui
Published: September 2023

The generation and expansion of cracks in concrete structures reduce the durability and safety of structures. In order to detect cracks in concrete structures, a long-lasting phosphorescent microcapsule coating is proposed in this study. The microcapsule-based sensor is pasted on the surface of cement-based materials and solidified. The microcapsules become ruptured and cause the core material to flow out when the microcracks occur on the material, which emits strong phosphorescence at the cracked position under UV irradiation. The results indicate that the successful encapsulation of microcapsules could enhance the thermal stability of phosphorescent dye. The excitation wavelength of the phosphorescent microcapsules is also investigated. The phosphorescent microcapsules could effectively highlight unnoticeable cracks by a long-lasting phosphorescence response in the cracking region. The mechanical properties of microcapsules/epoxy resin composite coatings were studied, and the optimal content of microcapsules in the coating was determined. The as-fabricated phosphorescent microcapsules have good damage-sensing effects in conditions of different light and temperatures. The method proposed in this study will assist in the further development of damage-sensing material in the field of concrete crack monitoring.
Secure Low-Energy Routing Protocol Based on Dynamic Trust Awareness and Load Balancing in Wireless Sensor Networks
Authors: Xiaogang Yuan
Published: September 2023

Since wireless sensor networks (WSNs) have the requirements of high security and energy conservation, a distributed secure low-energy routing protocol (SLERP) based on dynamic trust awareness and load balancing is proposed. In order to reduce the adverse influence of malicious nodes in the network, the Chebyshev neural network is used to predict the dynamic trust degree of network nodes to accelerate the speed and accuracy of malicious node detection. Based on the comprehensive consideration of the average dynamic trust degree of cluster-head nodes, the load of the cluster-head node, network energy consumption, network lifetime, and accurate route evaluation model are established by the analytic hierarchy process (AHP). The search methods of secure low-energy routing and chromosome crossing and the mutation method are designed based on the genetic algorithm (GA), so as to quickly establish the optimal cluster-head node-set and the optimal routing path of each node. Simulation results show that SLERP can significantly improve the detection speed and detection success rate of malicious nodes, reduce the network energy consumption and load, and effectively extend the network lifetime.
Dual-Core Adaptive NLM Image Denoising Algorithm Based on Variable-Size Window and Neighborhood Multifeatures
Authors: Mao Jing , Sun Lianming , Chen Jie , Yu Shunyuan
Published: September 2023

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian–Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.
The Double-Mirror Gaze, Transcoded Testimony, and Disqualified Witnesses in the Talmud
Authors: Dickmann Iddo
Published: September 2023

I will argue that the underlying rationale for the talmudic list of trades disqualified from legal testimony is aesthetic. These trades involved professional mimicry, which as such incapacitated what R. Neis has termed “homovisuality” or self-referential witnessing in the Talmud. Reading talmudic laws of conjoined testimony and the induction of witnesses in light of Deleuze’s and Blanchot’s philosophy, I will argue that homovisuality entailed the witness’s reincarnation as the subject of the event, thus re-signifying rather than reporting the event. The judge, transformed into a witness, could capture the truth of the event at a glance, in a manner both prior to and irreducible to trial procedures.
Making Sense of God: Samson Raphael Hirsch and Franz Rosenzweig on Translation and Anthropomorphisms
Authors: Schonfeld Eli
Published: September 2023

Contrary to the classical denial of bodily attributes or human emotions to God, both Samson Raphael Hirsch and Franz Rosenzweig embrace biblical anthropomorphisms. Their views on anthropomorphisms are part of their critiques of philosophy, especially of the basic preconceptions of the philosophical approach to the concept of God. This article analyses their positions by examining Hirsch’s commentaries on scripture (especially Gen 6:6), and Rosenzweig’s “A Note on Anthropomorphisms in Response to the Encyclopedia Judaica’s Article.” Through a close reading and interpretation of Rabad of Posquiére’s famous animadversion against Maimonides’s rule concerning heretics, this paper retraces the rabbinical roots of Hirsch’s and Rosenzweig’s approach to anthropomorphisms.
The Dharma King Who Took Care of His Family: Penden Trashi and the Rise of a Clan-Based Tibetan Buddhist Society in the Fifteenth-Century Northern Sino-Tibetan Borderland
Authors: Hu Xiaobai
Published: September 2023

In the fifteenth century, Tibetan Buddhist monks from the northern Sino-Tibetan borderland played an important role in facilitating religious and political contacts between Ming China and the Tibetan world. With the rise of their political status and religious influence at the Ming court, these Buddhist hierarchs also projected imperial endorsements to the Sino-Tibetan borderland and reshaped the local social structure. This article examines how Penden Trashi, one of the most eminent Tibetan Buddhist monks of the fifteenth century, contributed to the rise of a clan-based Tibetan Buddhist society in early Ming Minzhou 岷州. By scrutinizing Penden Trashi’s biography and personal letters to identify his role in forging the dharma succession pattern and monastic association mode in Minzhou, this article illuminates how imperial patronage reconstructed the power dynamic in the fifteenth-century Sino-Tibetan borderland.