bluuebunny/crossref_metadata_2025 huggingface dataset:
DosEmi study protocol: a phase IV, multicentre, open-label, crossover study to evaluate non-inferiority of pharmacokinetic-guided reduced dosing compared with conventional dosing of emicizumab in people with haemophilia A
Authors: Donners Anouk , van der Zwet Konrad , Egberts Antoine C G , Fijnvandraat Karin , Mathôt Ron , Kruis Ilmar , Cnossen Marjon H , Schutgens Roger , Urbanus Rolf T , Fischer Kathelijn
Published: June 2023

Introduction

Emicizumab effectively prevents bleeding in people with haemophilia A (PwHA), but is a burden for national healthcare budgets and consequently may limit access. According to the drug label, dosing of emicizumab is based on body weight with fixed intervals of 7, 14 or 28 days, which leads to mean plasma concentrations of 55 µg/mL (SD 15 µg/mL). However, a moderate variability of concentrations and a minimal effective concentration of 30 µg/mL have been suggested in studies. Therefore, a dose of emicizumab that targets a trough concentration of 30 µg/mL is hypothesised to be equally effective as conventional dosing in the prevention of bleeding.

Methods and analysis

We designed a phase IV, multicentre, open-label, crossover study to evaluate non-inferiority of bleed control of ≥6 months on conventional dosing in comparison to ≥6 months on dose intervention. This dose intervention consists of reducing the dose of emicizumab to target a trough concentrations of 30 µg/mL using individual pharmacokinetic (PK) parameters. Ninety-five PwHA aged >1 years who received conventional dosing of emicizumab for ≥12 months with good bleeding control during the last 6 months will be recruited from all Dutch haemophilia treatment centres. The study is powered to detect a clinically relevant decrease (risk difference) of 15% in the proportion of patients without treated bleeds during follow-up. Secondary endpoints are spontaneous joint or muscle bleeds, and annualised treated bleeding rates (using negative binomial regression). Cost-effectivity between conventional dosing and individualised PK-guided dosing of emicizumab will be compared.

Ethics and dissemination

The DosEmi study was approved by the Medical Ethics Review Committee NedMec of the University Medical Center of Utrecht, The Netherlands. Study results will be communicated through publications in international scientific journals and presentations at (inter)national conferences.

Trial registration number

EUCTR2021-004039-10-NL athttps://trialsearch.who.int.

Protocol version

V.4.1 on 28 October 2022 (DosEmi protocol_V4.1; NL81112.041.22).
Codesigning a user-centred digital psychoeducational tool for youth mental well-being with families in Canada: study protocol for a sequential exploratory mixed methods study
Authors: Moss Stephana Julia , Racine Nicole , Ahmed Sofia , Birnie Kathryn , Cherak Michal S , Curran Janet A , Halperin Donna , Halperin Scott A , Harley Micaela , Hu Jia , Leppan Laura , Nickel Angie , Russell Kristine , Solis May , Smith Stacie , Soo Andrea , Stelfox Maia , Tutelman Perri R , Stelfox Henry Thomas , Fiest Kirsten M , Parsons Leigh Jeanna
Published: June 2023

Introduction

On 11 March 2020, WHO declared the novel coronavirus (COVID-19) disease a global pandemic. Governments globally implemented physical distancing measures and closure of public institutions that resulted in varying implications to youth mental well-being (eg, social isolation, reduced extracurricular activities). These impacts may have detrimental short-term and long-term effects on youth mental well-being; care for youth with mental health disorders was already overstretched, underfunded and fragmented before the pandemic and youth are not often considered in mental health initiatives. There is a pressing need to partner with youth and families to target and improve youth mental well-being prior to the onset of a mental health disorder, as well as to conduct research on youth mental well-being needs related to pandemic recovery. Here we present a protocol for partnering with youth and families to codesign a user-centred digital tool for youth mental well-being.

Methods and analysis

We will conduct a national research study to develop a catalogue of recommendations specific to supporting youth mental well-being, and a digital tool to support youth mental well-being through three phases of work: (1) expert consultation on data related to supporting youth mental well-being existing within our Pandemic Preparedness Research Program; (2) codesign of an innovative digital tool for youth mental well-being; and (3) assessment of the tool’s usability and acceptability.

Ethics and dissemination

This study has been approved by the Dalhousie Research Ethics Board (2023-6538) and the Conjoint Health Research Ethics Board (23-0039). This study will complement ongoing foundational research in youth conducted by our team that involves partnering with youth and families to understand the unique implications of the pandemic on this population.
On testing proportional odds assumptions for proportional odds models
Authors: Liu Anqi , He Hua , Tu Xin M , Tang Wan
Published: June 2023

Proportional odds models are commonly used to model ordinal responses, but the proportional odds assumption may not hold in practice, leading to biased inference. Tests such as score, Wald and likelihood ratio (LR) have been proposed to evaluate the proportional odds assumption based on models without the assumption. Brant has proposed an independent binary model-based Wald-type test, and Wolfe and Gould have extended the idea to propose an LR-type test.

This paper provides a brief review of the Brant and Wolfe-Gould tests for evaluating the proportional odds assumption and evaluates their performance through simulation studies and a real data example. Sample programs are provided in SAS, SPSS and Stata to facilitate the implementation of these tests using standard statistical software packages.

This study highlights the importance of evaluating the proportional odds assumption when using proportional odds models for ordinal responses. The sample programs provided in this paper make it easy for researchers to apply these tests in their own analyses using standard statistical software packages.
Impact of built environment change on all-cause and cause-specific mortality: a novel longitudinal method and study
Authors: Macdonald Laura , Nicholls Natalie , Brown Denise , Mitchell Richard
Published: June 2023

Background

Public health research increasingly acknowledges the influence of built environments (BE) on health; however, it is uncertain how BE change is associated with better population health and whether BE change can help narrow health inequalities. This knowledge gap is partly due to a lack of suitable longitudinal BE data in most countries. We devised a method to quantify BE change longitudinally and explored associations with mortality. The method is replicable in any nation that captures BE vector map data.

Methods

Ordnance Survey data were used to categorise small areas as having no change, loss or gain, in buildings, roads, and woodland between 2015 and 2019. We examined individual mortality records for 2012–2015 and 2016–2019, using negative binomial regression to explore associations between BE change and all-cause and cause-specific mortality, adjusting for income deprivation.

Results

BE change varied significantly by deprivation and urbanicity. Change in the BE and change in mortality were not related, however, areas that went on to experience BE change had different baseline mortality rates compared with those that did not. For example, areas that gained infrastructure already had lower mortality rates.

Conclusion

We provide new methodology to quantify BE change over time across a nation. Findings provide insight into the health of areas that do/do not experience change, prompting critical perspectives on cross-sectional studies of associations between BE and health. Methods and findings applied internationally could explore the context of BE change and its potential to improve health in areas most in need beyond the UK.
Tobacco exposure primes the secretion of CCL21 positively associated with tertiary lymphoid structure and response to immunotherapy
Authors: Yin Xuewen , Wang Hui , Li Rutao , Song Xuming , Zhang Te , Liang Yingkuan , Chen Yu-Zhong , Yu Xinnian , Mao Qixing , Xia Wenjie , Chen Bing , Xu Lin , Dong Gaochao , Jiang Feng
Published: June 2023

Background

It has been reported that smoking history as a predictor of immunotherapy efficacy in patients with advanced lung cancer, however, the underlying mechanisms of this phenomenon remain largely unknown.

Methods

The patients with lung adenocarcinoma’s (LUAD) cohort and the orthotopical transplanted mouse model were used to explore the correlation between smoking status and tertiary lymphoid structure (TLS) and chemokine CCL21, respectively. Cell adhesion and co-immunoprecipitation assays were performed to explore the interaction between CD4+T cells and CD20+B cells under tobacco exposure. Chromatin immunoprecipitation-PCR was used to dissect the mechanism of upregulated CCL21 secretion in tobacco treatment. Serum CCL21 level was recorded in patients with LUAD treated with immunotherapy.

Results

Here we observed that individuals with a smoking history exhibit an increased quantity and maturation level of TLS compared with non-smokers, along with higher levels of CCL21 secretion. Tobacco exposure promoted CCL21 expression in an epithelial cell-intrinsic manner, of which BaP, the main component of tobacco, facilitated the nuclear retention of the aryl hydrocarbon receptor that occupied the promoter of CCL21. Additionally, the activated CCL21/CCR7 axis increased the CD11a expression of CD4+T cells, boosting the interaction with CD20+B cells dependent on ICAM1, which potentially induced the TLSs formation. Patients with elevated serum levels of CCL21 benefited more from immunotherapy.

Conclusions

Patients with a smoking history exhibited higher levels of TLS via the CCL21-dependent mechanism, serum CCL21 was identified as a reliable biomarker for predicting the efficacy of immunotherapy.
On Two Modifications of the McEliece PKE and the CFS Signature Scheme
Authors: D’Alconzo Giuseppe
Published: June 2023

This paper analyzes two schemes from a 2019 work by Liu, Yang, Han and Wang, namely, adapting the McEliece cryptosystem to obtain a new public-key encryption scheme, and designing an efficient CFS-like digital signature. It is shown that the new encryption scheme based on McEliece, even if it has longer public keys, is not more secure than the standard one. The security gap between the original scheme and its modification is presented. Moreover, while the proposed parameters for the digital signature scheme lead to a significant performance improvement, however, they introduce a vulnerability in the protocol. A key-forgery attack using the Support Splitting Algorithm as a subroutine is described, with a study of the computational cost of retrieving the secret key from the public one.
Synthesis of styrene, phenyl acrylate and allyloxyphenyl-functionalized porphyrins, and preliminary exploration of their utility for assembling multiple porphyrin units
Authors: Bhimpuria Rohan , Baddigam Kiran Reddy , Xu Felix , Wells Jordann A. L. , Borbas K. Eszter
Published: June 2023

Three free-base and two Zn(II) porphyrins carrying one alkene-substituted meso-aryl group and three solubilizing pentyl groups were prepared via mixed aldehyde-type syntheses. A meso [Formula: see text]-allyloxyphenyl porphyrin was obtained via the corresponding 5-([Formula: see text]-allyloxyphenyl) dipyrromethane. The porphyrins were fully characterized using a combination of NMR spectroscopy, high-resolution mass spectrometry, and UV-Vis absorption and emission spectroscopies. Two of the free-base porphyrins were heated in the presence of AIBN or benzoyl peroxide as the initiator. The major isolated products of these reactions contained two porphyrin units based on the results of MALDI-MS and 1H NMR analysis data, which was supported by the results of FTIR, UV-Vis absorption and emission spectroscopies, and gel permeation and liquid chromatographies. Additionally, the acrylamide-functionalized porphyrin could be attached to a polylysine scaffold under basic conditions. These results suggest that the olefinic handle can be used to assemble structures containing multiple porphyrinic macrocycles.
Dynamics of a Newly Established Agricultural Commodities Market: Financialization, Hedging and Portfolio Diversification in Turkey
Authors: Acikgoz Turker , Alp Ozge Sezgin , Alkan Nazlan Belemir
Published: June 2023

The purpose of this study is to investigate the dynamic relationship between financial assets and agricultural commodities market in Turkey. For this purpose, first, this study investigates financialization of agricultural commodities with a newly established agricultural commodities market of Turkey. Second, this paper analyzes hedging and portfolio diversification performances of agricultural commodities. By employing the DCC-GARCH approach of Engle (2002) and spillover index approach of Diebold and Yilmaz (2014), we analyze the financialization of agricultural commodities and calculate optimal hedge ratios and portfolio weights between two classes of assets. The results show the financialization of agricultural commodities at a very early age in Turkey. There exists a low degree of comovements and volatility spillovers between financial assets and agricultural commodities. Second, we show that agricultural commodities do not hedge financial investments sufficiently but they perform very well in terms of diversification and boost portfolio performances. Considering the previous limited literature on the topic, this study contributes to the literature by displaying the dynamics of a newly established agricultural commodities market in an emerging market. The findings of this study have many important implications for farmers and producers of agricultural commodities, and financial market actors such as individual investors, hedge funds and investment banks in terms of risk management and hedging.
Forgery Detection by Weighted Complementarity between Significant Invariance and Detail Enhancement
Authors: Xiao Shuai , Zhang Zhuo , Yang Jiachen , Wen Jiabao , Li Yang
Published: June 2023

Generative adversarial networks have shown impressive results in the modeling of movies and games, but what if such powerful image generation capability is used to harm the Multimedia? The face replacement methods represented by Deepfakes are becoming a threat to everyone, so the development of image authenticity detection methods has become a top priority. For achieving accurate detection resistant to compression effects, we propose a weighted complementary dual-stream detection method. First, to alleviate the influence of image compression on manipulation detection, we propose the concept of pixel-wise saliency invariance. We map fake images onto saliency maps via Quaternary Fourier Transform, which discovers the invariant properties of image phase spectra on different compressions. Meanwhile, to capture boundary traces more easily, we propose the concept of pixel-wise detail enhancement. We apply Bilateral Filtering to preserve the texture edges of fake images and amplify the fake boundaries. Finally, to take full advantage of the two proposed concepts, a weighted complementary dual-stream network is designed as a classifier to fuse features and identify real and fake. On different benchmarks like FaceForensics++ (FF++), Celeb-DF, and DFDC, the experimental results show that the proposed method has the average best detection accuracy compared to existing methods.
A Comparative Analysis of Ookla Speedtest and Measurement Labs Network Diagnostic Test (NDT7)
Authors: MacMillan Kyle , Mangla Tarun , Saxon James , Marwell Nicole P. , Feamster Nick
Published: June 2023

Consumers, regulators, and ISPs all use client-based "speed tests" to measure network performance, both in single-user settings and in aggregate. Two prevalent speed tests, Ookla's Speedtest and Measurement Lab's Network Diagnostic Test (NDT), are often used for similar purposes, despite having significant differences in both the test design and implementation, and in the infrastructure used to perform measurements. In this paper, we present the first-ever comparative evaluation of Ookla and NDT7 (the latest version of NDT), both in controlled and wide-area settings. Our goal is to characterize when and to what extent these two speed tests yield different results, as well as the factors that contribute to the differences. To study the effects of the test design, we conduct a series of controlled, in-lab experiments under a comprehensive set of network conditions and usage modes (e.g., TCP congestion control, native vs. browser client). Our results show that Ookla and NDT7 report similar speeds under most in-lab conditions, with the exception of networks that experience high latency, where Ookla consistently reports higher throughput. To characterize the behavior of these tools in wide-area deployment, we collect more than 80,000 pairs of Ookla and NDT7 measurements across nine months and 126 households, with a range of ISPs and speed tiers. This first-of-its-kind paired-test analysis reveals many previously unknown systemic issues, including high variability in NDT7 test results and systematically under-performing servers in the Ookla network.
A First Look at Wi-Fi 6 in Action: Throughput, Latency, Energy Efficiency, and Security
Authors: Liu Ruofeng , Choi Nakjung
Published: June 2023

We present the performance measurement of Wi-Fi 6 (IEEE 802.11ax). Our experiments focus on multi-client scenarios. The results reveal the impact of the new channel access mechanisms (i.e., OFDMA and TWT) on spectrum efficiency, latency, energy consumption, and security. (i) A comparison with the legacy CSMA/CA scheme shows that the commodity Wi-Fi 6 achieves 3× overall throughput and dramatically reduces the latency (5×) when coexisting with the legacy Wi-Fi network. (ii) However, the current OFDMA implementation significantly increases the power consumption (6×), implying a design tradeoff between the gain of throughput/latency and the cost of energy consumption. We believe that our findings provide critical insights for the scheduling algorithm design, power optimization, and security protection of the next-generation WLANs. The full paper is provided in \cite10.1145/3579451.
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection
Authors: Goldberg Alexander , Fanti Giulia , Shah Nihar B.
Published: June 2023

In a number of applications where anonymity is critical, users act under pseudonyms to preserve their privacy. For instance, in scientific peer review using forums like OpenReview.net, reviewers make comments on papers that are publicly viewable. Reviewers who have been assigned multiple papers operate under different pseudonyms across their papers to remain anonymous. Other examples of publicly visible tasks where users operate under pseudonyms include Wikipedia editing and cryptocurrency transactions.

In these settings, it is common for users to engage in batching - the completion of several similar tasks at the same time. Batching occurs both due to natural bursts in activity (e.g., a person visits a website and makes many comments at once) or as a productivity strategy used to streamline work.

In peer-review forums such as computer science conferences, reviewers and meta-reviewers are often assigned multiple papers. We find empirically that reviewers are highly likely to batch their comments and/or reviews across papers. In analysis of data from a top Computer Science conference with thousands of papers, reviewers, and discussion comments we find that when reviewers and meta-reviewers comment on multiple papers, they have a 30.10% chance of batching their comments within 5 minutes of one other. In comparison, any randomly chosen pair of reviewers and meta- reviewers had only a 0.66% chance of making comments on different papers within 5 minutes of each other.
Constant Regret Primal-Dual Policy for Multi-way Dynamic Matching
Authors: Wei Yehua , Xu Jiaming , Yu Sophie H.
Published: June 2023

We study a discrete-time dynamic multi-way matching model. There are finitely many agent types that arrive stochastically and wait to be matched. State-of-the-art dynamic matching policies in the literature require the knowledge of all system parameters to determine an optimal basis of the fluid relaxation, and focus on controlling the number of waiting agents using only matches in the optimal basis [4,6,7]. In this paper, we propose a primal-dual policy that schedule matches for future arrivals based on an estimator for the dual solution. Our policy does not require the knowledge of optimal bases, and is the first to achieve constant regret at all times under unknown arrival rates. In addition, we show that when the arrival rates are known, the primal-dual policy achieves the optimal scaling as the lower-bound described in [6,7]. Furthermore, we find that when the arrival rates are known, the primal-dual policy can significantly outperform alternative dynamic matching policies in numerical simulations.
Detecting and Measuring Aggressive Location Harvesting in Mobile Apps via Data-flow Path Embedding
Authors: Lu Haoran , Zhao Qingchuan , Chen Yongliang , Liao Xiaojing , Lin Zhiqiang
Published: June 2023

Today, location-based services have become prevalent in the mobile platform, where mobile apps provide specific services to a user based on his or her location. Unfortunately, mobile apps can aggressively harvest location data with much higher accuracy and frequency than they need because the coarse-grained access control mechanism currently implemented in mobile operating systems (e.g., Android) cannot regulate such behavior. This unnecessary data collection violates the data minimization policy, yet no previous studies have investigated privacy violations from this perspective, and existing techniques are insufficient to address this violation. To fill this knowledge gap, we take the first step toward detecting and measuring this privacy risk in mobile apps at scale. Particularly, we annotate and release the first dataset to characterize those aggressive location harvesting apps and understand the challenges of automatic detection and classification. Next, we present a novel system, LocationScope, to address these challenges by (i) uncovering how an app collects locations and how to use such data through a fine-tuned value set analysis technique, (ii) recognizing the fine-grained location-based services an app provides via embedding data-flow paths, which is a combination of program analysis and machine learning techniques, extracted from its location data usages, and (iii) identifying aggressive apps with an outlier detection technique achieving a precision of 97% in aggressive app detection. Our technique has further been applied to millions of free Android apps from Google Play as of 2019 and 2021. Highlights of our measurements on detected aggressive apps include their growing trend from 2019 to 2021 and the app generators' significant contribution of aggressive location harvesting apps.
Duo: A High-Throughput Reconfigurable Datacenter Network Using Local Routing and Control
Authors: Zerwas Johannes , Györgyi Csaba , Blenk Andreas , Schmid Stefan , Avin Chen
Published: June 2023

The performance of many cloud-based applications critically depends on the capacity of the underlying datacenter network. A particularly innovative approach to improve the throughput in datacenters is enabled by emerging optical technologies, which allow to dynamically adjust the physical network topology, both in an oblivious or demand-aware manner. However, such topology engineering, i.e., the operation and control of dynamic datacenter networks, is considered complex and currently comes with restrictions and overheads.

We present Duo, a novel demand-aware reconfigurable rack-to-rack datacenter network design realized with a simple and efficient control plane. Duo is based on the well-known de Bruijn topology (implemented using a small number of optical circuit switches) and the key observation that this topology can be enhanced using dynamic ("opportunistic") links between its nodes.

In contrast to previous systems, Duo has several desired features: i) It makes effective use of the network capacity by supporting integrated and multi-hop routing (paths that combine both static and dynamic links). ii) It uses a work-conserving queue scheduling which enables out-of-the-box TCP support. iii) Duo employs greedy routing that is implemented using standard IP longest prefix match with small forwarding tables. And iv) during topological reconfigurations, routing tables require only local updates, making this approach ideal for dynamic networks.

We evaluate Duo in end-to-end packet-level simulations, comparing it to the state-of-the-art static and dynamic networks designs. We show that Duo provides higher throughput, shorter paths, lower flow completion times for high priority flows, and minimal packet reordering, all using existing network and transport layer protocols. We also report on a proof-of-concept implementation of \system's control and data plane.
Enabling Long-term Fairness in Dynamic Resource Allocation
Authors: Si Salem Tareq , Iosifidis Georgios , Neglia Giovanni
Published: June 2023

We study the fairness of dynamic resource allocation problem under the α-fairness criterion. We recognize two different fairness objectives that naturally arise in this problem: the well-understood slot-fairness objective that aims to ensure fairness at every timeslot, and the less explored horizon-fairness objective that aims to ensure fairness across utilities accumulated over a time horizon. We argue that horizon-fairness comes at a lower price in terms of social welfare. We study horizon-fairness with the regret as a performance metric and show that vanishing regret cannot be achieved in presence of an unrestricted adversary. We propose restrictions on the adversary's capabilities corresponding to realistic scenarios and an online policy that indeed guarantees vanishing regret under these restrictions.
Fiat Lux: Illuminating IPv6 Apportionment with Different Datasets
Authors: Hsu Amanda , Li Frank , Pearce Paul
Published: June 2023

IPv6 adoption continues to grow, making up more than 40% of client traffic to Google globally. While the ubiquity of the IPv4 address space makes it comparably easier to understand, the vast and less studied IPv6 address space motivates a variety of works detailing methodology to collect and analyze IPv6 properties, many of which use knowledge from specific data sources as a lens for answering research questions. Despite such work, questions remain on basic properties such as the appropriate prefix size for research tasks.

Our work fills this knowledge gap by presenting an analysis of the apportionment of the IPv6 address space from the ground-up, using data and knowledge from numerous data sources simultaneously, aimed at identifying how to leverage IPv6 address information for a variety of research tasks. Utilizing WHOIS data from RIRs, routing data, and hitlists, we highlight fundamental differences in apportionment sizes and structural properties depending on data source and examination method. We focus on the different perspectives each dataset offers and the disjoint, heterogeneous nature of these datasets when taken together. We additionally leverage a graph-based analysis method for these datasets that allows us to draw conclusions regarding when and how to intersect the datasets and their utility. The differences in each dataset's perspective is not due to dataset problems but rather stems from a variety of differing structural and deployment behaviors across RIRs and IPv6 providers alike. In light of these inconsistencies, we discuss network address partitioning, best practices, and considerations for future IPv6 measurement and analysis projects.
FuncPipe: A Pipelined Serverless Framework for Fast and Cost-efficient Training of Deep Learning Models
Authors: Liu Yunzhuo , Jiang Bo , Guo Tian , Huang Zimeng , Ma Wenhao , Wang Xinbing , Zhou Chenghu
Published: June 2023

Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems researchers have recently started to provide support for serverless-based training. However, the ability to train DL models on serverless platforms is hindered by the resource limitations of today's serverless infrastructure and DL models' explosive requirement for memory and bandwidth. This paper describes FUNCPIPE, a novel pipelined training framework specifically designed for serverless platforms that enable fast and low-cost training of DL models. FUNCPIPE is designed with the key insight that model partitioning can be leveraged to bridge both memory and bandwidth gaps between the capacity of serverless functions and the requirement of DL training. Conceptually simple, we have to answer several design questions, including how to partition the model, configure each serverless function, and exploit each function's uplink/downlink bandwidth. We implement FUNCPIPE on two popular cloud serverless platforms and show that it achieves 7%-77% cost savings and 1.3X-2.2X speedup compared to state-of-the-art serverless-based frameworks.
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
Authors: Zhang Yizhou , Qu Guannan , Xu Pan , Lin Yiheng , Chen Zaiwei , Wierman Adam
Published: June 2023

We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy Iteration (LPI) algorithm that provably learns a near-globally-optimal policy using only local information. In particular, we show that, despite restricting each agent's attention to only its κ-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in κ. In addition, we show the finite-sample convergence of LPI to the global optimal policy, which explicitly captures the trade-off between optimality and computational complexity in choosing κ. Numerical simulations demonstrate the effectiveness of LPI. This extended abstract is an abridged version of [12].
Joint Learning and Control in Stochastic Queueing Networks with Unknown Utilities
Authors: Fu Xinzhe , Modiano Eytan
Published: June 2023

We study the optimal control problem in stochastic queueing networks with a set of job dispatchers connected to a set of parallel servers with queues. Jobs arrive at the dispatchers and get routed to the servers following some routing policy. The arrival processes of jobs and the service processes of servers are stochastic with unknown arrival rates and service rates. Upon the completion of each job from dispatcher un at server sm, a random utility whose mean is unknown is obtained. We seek to design a control policy that makes routing decisions at the dispatchers and scheduling decisions at the servers to maximize the total utility obtained by the end of a finite time horizon T. The performance of policies is measured by regret, which is defined as the difference in total expected utility with respect to the optimal dynamic policy that has access to arrival rates, service rates and underlying utilities.

We first show that the expected utility of the optimal dynamic policy is upper bounded by T times the solution to a static linear program, where the optimization variables correspond to rates of jobs from dispatchers to servers and the feasibility region is parameterized by arrival rates and service rates. We next propose a policy for the optimal control problem that is an integration of a learning algorithm and a control policy. The learning algorithm seeks to learn the optimal extreme point solution to the static linear program based on the information available in the optimal control problem. The control policy, a mixture of priority-based and Joint-the-Shortest-Queue routing at the dispatchers and priority-based scheduling at the servers, makes decisions based on the graphical structure induced by the extreme point solutions provided by the learning algorithm. We prove that our policy achieves logarithmic regret whereas application of existing techniques to the optimal control problem would lead to Ω(√T)-regret. The theoretical analysis is further complemented with simulations to evaluate the empirical performance of our policy.
JS Capsules: A Framework for Capturing Fine-grained JavaScript Memory Measurements for the Mobile Web.
Authors: Naseer Usama , Benson Theophilus A.
Published: June 2023

Understanding the resource consumption of the mobile web is an important topic that has garnered much attention in recent years. However, existing works mostly focus on the networking or computational aspects of the mobile web and largely ignore memory, which is an important aspect given the mobile web's reliance on resource-heavy JavaScript.

In this paper, we propose a framework, called JS Capsule, for characterizing the memory of JavaScript functions and, using this framework, we investigate the key browser mechanics that contribute to the memory overhead. Leveraging our framework on a testbed of Android mobile phones, we conduct measurements of the Alexa top 1K websites. While most existing frameworks focus on V8 --- the JavaScript engine used in most popular browsers --- in the context of memory, our measurements show that the memory implications of JavaScript extends far beyond V8 due to the cascading effects that certain JavaScript calls have on the browser's rendering mechanics. We quantify and highlight the direct impact that website DOM have on JavaScript memory overhead and present, to our knowledge, the first root-cause analysis to dissect and characterize their impact on JavaScript memory overheads.
Mars: Near-Optimal Throughput with Shallow Buffers in Reconfigurable Datacenter Networks
Authors: Addanki Vamsi , Avin Chen , Schmid Stefan
Published: June 2023

The performance of large-scale computing systems often critically depends on high-performance communication networks. Dynamically reconfigurable topologies, e.g., based on optical circuit switches, are emerging as an innovative new technology to deal with the explosive growth of datacenter traffic. Specifically, periodic reconfigurable datacenter networks (RDCNs) such as RotorNet (SIGCOMM 2017), Opera (NSDI 2020) and Sirius (SIGCOMM 2020) have been shown to provide high throughput, by emulating a complete graph through fast periodic circuit switch scheduling.

However, to achieve such a high throughput, existing reconfigurable network designs pay a high price: in terms of potentially high delays, but also, as we show as a first contribution in this paper, in terms of the high buffer requirements. In particular, we show that under buffer constraints, emulating the high-throughput complete graph is infeasible at scale, and we uncover a spectrum of unvisited and attractive alternative RDCNs, which emulate regular graphs, but with lower node degree than the complete graph.

We present Mars, a periodic reconfigurable topology which emulates a d-regular graph with near-optimal throughput. In particular, we systematically analyze how the degree~d can be optimized for throughput given the available buffer and delay tolerance of the datacenter. We further show empirically that Mars achieves higher throughput compared to existing systems when buffer sizes are bounded.
Memtrade: Marketplace for Disaggregated Memory Clouds
Authors: Maruf Hasan Al , Zhong Yuhong , Wang Hongyi , Chowdhury Mosharaf , Cidon Asaf , Waldspurger Carl
Published: June 2023

We present Memtrade, the first practical marketplace for disaggregated memory clouds. Clouds introduce a set of unique challenges for resource disaggregation across different tenants, including resource harvesting, isolation, and matching. Memtrade allows producer virtual machines (VMs) to lease both their unallocated memory and allocated-but-idle application memory to remote consumer VMs for a limited period of time. Memtrade does not require any modifications to host-level system software or support from the cloud provider. It harvests producer memory using an application-aware control loop to form a distributed transient remote memory pool with minimal performance impact; it employs a broker to match producers with consumers while satisfying performance constraints; and it exposes the matched memory to consumers through different abstractions. As a proof of concept, we propose two such memory access interfaces for Memtrade consumers -- a transient KV cache for specified applications and a swap interface that is application-transparent. Our evaluation shows that Memtrade provides significant performance benefits for consumers (improving average read latency up to 2.8X) while preserving confidentiality and integrity, with little impact on producer applications (degrading performance by less than 2.1%).
Online Adversarial Stabilization of Unknown Networked Systems
Authors: Yu Jing , Ho Dimitar , Wierman Adam
Published: June 2023

We investigate the problem of stabilizing an unknown networked linear system under communication constraints and adversarial disturbances. We propose the first provably stabilizing algorithm for the problem. The algorithm uses a distributed version of nested convex body chasing to maintain a consistent estimate of the network dynamics and applies system level synthesis to determine a distributed controller based on this estimated model. Our approach avoids the need for system identification and accommodates a broad class of communication delay while being fully distributed and scaling favorably with the number of subsystems.
Power-of-d Choices Load Balancing in the Sub-Halfin Whitt Regime
Authors: Varma Sushil Mahavir , Castro Francisco , Maguluri Siva Theja
Published: June 2023

We characterize the steady-state queue length distribution for the Power-of-d choices routing algorithm for almost all values of d in the sub-Halfin Whitt asymptotic regime.
(Private) Kernelized Bandits with Distributed Biased Feedback
Authors: Li Fengjiao , Zhou Xingyu , Ji Bo
Published: June 2023

We study kernelized bandits with distributed biased feedback. This problem is motivated by several real-world applications (such as dynamic pricing, cellular network configuration, and policy making), where users from a large population contribute to the reward of the action chosen by a central entity, but it is difficult to collect feedback from all users. Instead, only biased feedback (due to user heterogeneity) from a subset of users may be available. In addition to such biased feedback, we are also faced with two practical challenges due to communication cost and computation complexity. To tackle these challenges, we carefully design a new distributed phase-then-batch-based elimination (DPBE) algorithm, which samples users in phases for collecting feedback to reduce the bias and employs maximum variance reduction to select actions in batches within each phase. By properly choosing the phase length, the batch size, and the confidence width used for eliminating suboptimal actions, we show that DPBE achieves a sublinear regret of Õ(T1-α/2 +γTT), where α∈ (0,1) is the user-sampling parameter one can tune. Moreover, DPBE can significantly reduce both communication cost and computation complexity in distributed kernelized bandits, compared to some variants of the state-of-the-art algorithms (originally developed for standard kernelized bandits). Furthermore, by incorporating various differential privacy models, we generalize DPBE to provide privacy guarantees for users participating in the distributed learning process. The algorithm design, analyses, and numerical experiments are provided in the full version of this paper [4].
SplitRPC: A {Control + Data} Path Splitting RPC Stack for ML Inference Serving
Authors: Kumar Adithya , Sivasubramaniam Anand , Zhu Timothy
Published: June 2023

The growing adoption of hardware accelerators driven by their intelligent compiler and runtime system counterparts has democratized ML services and precipitously reduced their execution times. This motivates us to shift our attention to characterize the overheads imposed by the RPC mechanism (`RPC tax') when serving them on accelerators. Conventional RPC implementations implicitly assume the host CPU services the requests, and we focus on expanding such works towards accelerator-based services. While SmartNIC based solutions work well for simple applications, serving complex ML models requires a more nuanced view to optimize both the data-path and the control/orchestration of these accelerators. We program commodity network interface cards (NICs) to split the control and data paths for effective transfer of control while efficiently transferring the payload to the accelerator. As opposed to unified approaches that bundle these paths together, limiting the flexibility in each of these paths, we design and implement SplitRPC - a {control + data} path optimizing RPC mechanism for ML inference serving. SplitRPC allows us to optimize the datapath to the accelerator while simultaneously allowing the CPU to maintain full orchestration capabilities. We implement SplitRPC on both commodity NICs and SmartNICs and demonstrate that SplitRPC is effective in minimizing the RPC tax while providing significant gains in throughput and latency.
Strategic Latency Reduction in Blockchain Peer-to-Peer Networks
Authors: Tang Weizhao , Kiffer Lucianna , Fanti Giulia , Juels Ari
Published: June 2023

Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Historically, this tradeoff has not been a bottleneck for most blockchains. However, an emerging host of blockchain-based applications (e.g., decentralized finance) are increasingly sensitive to latency; users who can reduce their network latency relative to other users can accrue (sometimes significant) financial gains. In this work, we initiate the study of strategic latency reduction in blockchain P2P networks. We first define two classes of latency that are of interest in blockchain applications. We then show empirically that a strategic agent who controls only their local peering decisions can manipulate both types of latency, achieving 60% of the global latency gains provided by the centralized, paid service bloXroute, or, in targeted scenarios, comparable gains. Finally, we show that our results are not due to the poor design of existing P2P networks. Under a simple network model, we theoretically prove that an adversary can always manipulate the P2P network's latency to their advantage, provided the network experiences sufficient peer churn and transaction activity.
The M/M/k with Deterministic Setup Times
Authors: Williams Jalani K. , Harchol-Balter Mor , Wang Weina
Published: June 2023

Capacity management, whether it involves servers in a data center, or human staff in a call center, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand, one would like to turn off servers when not in use (or send home staff that are idle) to save on resources. On the other hand, one wants to avoid the considerable setup time required to turn an off server back on. This paper aims to understand the delay component of this tradeoff, namely, what is the effect of setup time on average delay in a multi-server system?

Surprisingly little is known about the effect of setup times on delay. While there has been some work on studying the M/M/k with Exponentially-distributed setup times, these works provide only iterative methods for computing mean delay, giving little insight as to how delay is affected by k, by load, and by the setup time. Furthermore, setup time in practice is much better modeled by a Deterministic random variable, and, as this paper shows, the effect of a Deterministic setup time is nothing like that of an Exponentially-distributed setup time.

This paper provides the first analysis of the M/M/k with Deterministic setup times. We prove a lower bound on the effect of setup on delay. Our result is a simple algebraic formula which provides insight into how delay scales with the input parameters. For more details, see the full paper.
From the Editor: School’s Out
Authors: Kelly Rob G.
Published: June 2023

The title of this editorial is from the work of the great philosopher Alice Cooper who penned a song of the same name in 1972 (look it up, kids – it was on vinyl!). While for many it is spring that represents new life blooming, for those involved in higher education, spring simply mocks us. As the trees and flowers blossom, birds return from their migration to warmer latitudes, faculty and students are locked in an epic struggle to somehow cover all the material promised in one of the great works of fiction, the syllabus. The faculty hope that some amount of what they have helped students discover sticks through the summer and beyond. Those who teach introductory courses like yours truly live in dread of the future comment from a colleague that the students you taught the semester before say that they have “never heard of” some concept into which you poured your heart and soul to get across its importance, even beauty. Students are trying to figure out if it is possible to learn an entire semester of geology in the weekend before the final (spoiler alert—it is not).
Surface Modification Strategy for Yellow-Emitting Carbon Dots in Fluoropolymer Films
Authors: Washio Koyo , Sato Kohei , Iso Yoshiki , Isobe Tetsuhiko
Published: June 2023

Carbon dots (CDs) were prepared from p-phenylenediamine, modified with trifluoroacetic acid, and dispersed in a fluoropolymer (FP) film. Unmodified CDs did not disperse well in the FP film, resulting in concentration quenching; whereas surface modification improved the affinity between the CDs and the FP, resulting in a homogeneous dispersion and an improved photoluminescence quantum yield.
Hepatocyte-specific loss of LAP2α protects against diet-induced hepatic steatosis, steatohepatitis, and fibrosis in male mice
Authors: Upadhyay Kapil K. , Choi Eun-Young K. , Foisner Roland , Omary M. Bishr , Brady Graham F.
Published: June 2023

The nuclear envelope and lamina regulate lipid metabolism and susceptibility to nonalcoholic steatohepatitis (NASH), but the role of the nuclear lamin-binding protein LAP2α in NASH has not been explored. Our data demonstrate that hepatocyte-specific loss of LAP2α protects against diet-induced hepatic steatosis, NASH, and fibrosis in male mice, with downregulation of pro-steatotic, pro-inflammatory, and pro-fibrotic lamin-regulated genes. These findings suggest that targeting LAP2α could have future potential as a novel therapeutic avenue in NASH.
Contribution of transient receptor potential canonical channels in human and experimental pulmonary arterial hypertension
Authors: Masson Bastien , Saint-Martin Willer Anais , Dutheil Mary , Penalva Lucille , Le Ribeuz Hélène , El Jekmek Kristelle , Ruchon Yann , Cohen-Kaminsky Sylvia , Sabourin Jessica , Humbert Marc , Mercier Olaf , Montani David , Capuano Véronique , Antigny Fabrice
Published: June 2023

TRPC3 is increased in human and experimental pulmonary arterial hypertension (PAH). In PAH pulmonary arterial smooth muscle cells, TRPC3 participates in the aberrant store-operated Ca2+ entry contributing to their pathological cell phenotypes (exacerbated proliferation, enhanced migration, apoptosis resistance, and vasoconstriction). Pharmacological in vivo inhibition of TRPC3 reduces the development of experimental PAH. Even if other TRPC acts on PAH development, our results prove that TRPC3 inhibition could be considered as an innovative treatment for PAH.