bluuebunny/crossref_metadata_2025 huggingface dataset:
Preclinical optimization of a GPC2-targeting CAR T-cell therapy for neuroblastoma
Authors: Sun Ming , Cao Yingying , Okada Reona , Reyes-González Jeyshka M , Stack Hannah G , Qin Haiying , Li Nan , Seibert Charlie , Kelly Michael C , Ruppin Eytan , Ho Mitchell , Thiele Carol J , Nguyen Rosa
Published: January 2023

Background

Although most patients with newly diagnosed high-risk neuroblastoma (NB) achieve remission after initial therapy, more than 50% experience late relapses caused by minimal residual disease (MRD) and succumb to their cancer. Therapeutic strategies to target MRD may benefit these children. We developed a new chimeric antigen receptor (CAR) targeting glypican-2 (GPC2) and conducted iterative preclinical engineering of the CAR structure to maximize its anti-tumor efficacy before clinical translation.

Methods

We evaluated different GPC2-CAR constructs by measuring the CAR activity in vitro. NOD-SCID mice engrafted orthotopically with human NB cell lines or patient-derived xenografts and treated with human CAR T cells served as in vivo models. Mechanistic studies were performed using single-cell RNA-sequencing.

Results

Applying stringent in vitro assays and orthotopic in vivo NB models, we demonstrated that our single-chain variable fragment, CT3, integrated into a CAR vector with a CD28 hinge, CD28 transmembrane, and 4-1BB co-stimulatory domain (CT3.28H.BBζ) elicits the best preclinical anti-NB activity compared with other tested CAR constructs. This enhanced activity was associated with an enrichment of CD8+effector T cells in the tumor-microenvironment and upregulation of several effector molecules such asGNLY,GZMB,ZNF683, andHMGN2. Finally, we also showed that the CT3.28H.BBζ CAR we developed was more potent than a recently clinically tested GD2-targeted CAR to control NB growth in vivo.

Conclusion

Given the robust preclinical activity of CT3.28H.BBζ, these results form a promising basis for further clinical testing in children with NB.
Hydroxychloroquine daily dose, hydroxychloroquine blood levels and the risk of flares in patients with systemic lupus erythematosus
Authors: Fasano Serena , Messiniti Valentina , Iudici Michele , Coscia Melania Alessia , Ciccia Francesco
Published: January 2023

Background

Recent guidelines for SLE recommend using a hydroxychloroquine (HCQ) dose less than 5.0 mg/kg/day to reduce the risk of retinopathy. To determine if this dose reduction would have an impact on the clinical course of SLE, we compared flare incidence in a cohort of patients with SLE treated with two different oral HCQ dosages (≤5 mg/kg/day or >5 mg/kg/day). As a secondary analysis, we compared HCQ blood levels between the two different oral dosages, and evaluated the frequency of non-adherence in patients with SLE treated with HCQ.

Methods

We identified a cohort of patients with SLE taking HCQ for at least 6 months and followed for 24 months. At study entry and 6 months later, a blood venous sample was taken to measure HCQ blood levels by liquid chromatography. Incidence of new SLE flares after recruitment was put in relation to daily HCQ dose and mean HCQ blood levels. Cox regression analysis served to identify factors associated with SLE flares.

Results

83 patients were enrolled. We observed 11 (16%) flares that developed in mean 14.8 months of follow-up. The difference in terms of flare rate and mean HCQ blood levels between the two oral dosages was not statistically significant. There was a trend (p=0.08) for high HCQ dose being associated with a lower flare rate. At Cox analysis, higher HCQ blood levels and older age at baseline were protective against flare occurrence, while concomitant immunosuppressant therapy showed significant positive association. HCQ blood levels did not correlate with prescribed HCQ dose.

Conclusion

Patients with low oral HCQ dosage tend to have more flares, although the difference was not statistically significant. Higher HCQ blood levels were protective against flare occurrence. The risks and benefits must be balanced in choosing HCQ dose.
Sarcopenia is associated with an increase in long-term use of analgesics after elective surgery under general anesthesia
Authors: Sun Mingyang , Chen Wan-Ming , Wu Szu-Yuan , Zhang Jiaqiang
Published: January 2023

Purpose

To elucidate the association of presurgical sarcopenia and long-term non-opioid analgesic and opioid use after elective surgery under general anesthesia.

Methods

We conducted this population-based propensity score matched to investigate the effects of sarcopenia and long-term non-opioid analgesic and opioid use after elective surgery under general anesthesia between 1 October 2016 and 31 December 2019 from Taiwan’s National Health Insurance Research Database. Sarcopenia is a disease and coded as M62.84 in the International Classification of Diseases, 10th Revision, Clinical Modification. The primary outcome was the combined rate of the long-term use of all non-opioid analgesics or opioids over 3 and 6 months after elective surgery. By performing a logistic regression analysis, we calculated the adjusted ORs (aORs) with 95% CIs to identify the independent predictors for long-term non-opioid analgesic and opioid use after surgery.

Results

In total, 2860 patients underwent elective surgery. The 3-month non-opioid analgesic and opioid use rates were respectively 49.7% and 1.8% in the sarcopenia group and 37.9% and 0.9% in the non-sarcopenia group; by contrast, 6-month non-opioid analgesic and opioid use rates were respectively 31.6% and 1.2% in the sarcopenia group and 17.2% and 0.3% in the non-sarcopenia group. Moreover, presurgical sarcopenia increased the risk of long-term non-opioid analgesic and opioid use after elective surgery under general anesthesia (aORs for non-opioid analgesic use over 3 and 6 months after surgery: 1.17 (95% CI 1.05 to 2.23) and 1.26 (95% CI 1.04 to 1.45), respectively; aORs for opioid use over 3 and 6 months after surgery: 1.17 (95% CI 1.07 to 2.21) and 1.23 (95% CI 1.10 to 3.64), respectively).

Conclusion

Sarcopenia is associated with higher rates of long-term non-opioid analgesic and opioid use after elective surgery under general anesthesia.

The aim of this study was to compare the long-term use of non-opioid analgesics and opioids after elective surgery under general anesthesia between patients with and without sarcopenia. Results suggest that patients with sarcopenia are more likely to have increased use of non-opioid analgesics and opioids after surgery. Further research is needed to determine if sarcopenia can be modified prior to surgery and if this impacts the need for long-term pain management with these medications.
LRFD calibration for soil failure limit state using the Stiffness Method
Authors: Bathurst Richard J. , Allen Tony M.
Published: January 2023

The paper describes load and resistance factor design (LRFD) calibration for the resistance factor used in the Stiffness Method internal stability soil failure limit state for geogrid mechanically stabilized earth (MSE) walls. The Stiffness Method was recently adopted in the current American Association of State Highway and Transportation Officials LRFD Bridge Design Specifications in the US, and will appear in the next edition of the Canadian Highway Bridge Design Code. The paper describes the details of the calibration of the soil failure limit state which is unique to the Stiffness Method. Calibration outcomes include consideration of the concept of level of understanding in the selection of nominal load and resistance values which is unique to LRFD foundation engineering practice in Canada. A practical conclusion from these calculations is that if product line-specific creep test data are available to estimate the reinforcement secant creep stiffness used for design, then a resistance factor of 1.0 is reasonable for US practice. If only minimum average roll value tensile strength data are available, then a value of 0.95 is recommended for US practice. For Canadian practice, the corresponding values for typical level of understanding are 0.90 and 0.85, respectively.
Snowmass white paper: beyond the standard model effects on neutrino flavor
Authors: Argüelles C. A. , Barenboim G. , Bustamante M. , Coloma P. , Denton P. B. , Esteban I. , Farzan Y. , Martínez E. Fernández , Forero D. V. , Gago A. M. , Katori T. , Lehnert R. , Ross-Lonergan M. , Suliga A. M. , Tabrizi Z. , Anchordoqui L. , Chakraborty K. , Conrad J. , Das A. , Fong C. S. , Littlejohn B. R. , Maltoni M. , Parno D. , Spitz J. , Tang J. , Wissel S.
Published: January 2023

Neutrinos are one of the most promising messengers for signals of new physics Beyond the Standard Model (BSM). On the theoretical side, their elusive nature, combined with their unknown mass mechanism, seems to indicate that the neutrino sector is indeed opening a window to new physics. On the experimental side, several long-standing anomalies have been reported in the past decades, providing a strong motivation to thoroughly test the standard three-neutrino oscillation paradigm. In this Snowmass21 white paper, we explore the potential of current and future neutrino experiments to explore BSM effects on neutrino flavor during the next decade.
Bremsstrahlung as a probe of baryon stopping in heavy-ion collisions
Authors: Nese Sigurd , Nystrand Joakim
Published: January 2023

In collisions between heavy ions at ultra-relativistic energies the participating protons lose energy, which is converted into new particles. As the protons slow down, they emit bremsstrahlung radiation. The yield and angular distribution of the emitted radiation are sensitive probes of how much energy the incoming protons have lost. In this paper, the spectrum of bremsstrahlung radiation is calculated for different stopping scenarios, and the results are compared with the expected yield of photons from hadronic interactions.
Online Parallel-Batch Scheduling of Learning Effect Jobs with Incompatible Job Families for Prefabricated Components
Authors: Li Na , Ma Ran
Published: January 2023

In the production scheduling of prefabricated components, we study an online [Formula: see text] parallel-batch machines scheduling model considering learning effect jobs with [Formula: see text] incompatible job families to minimize the makespan in this paper, where the capacity of batch is unbounded. Job families indicate that a job must belong to some job family and jobs of distinct job families are incapable to be executed in the same batch. The information of each job including its basic processing time [Formula: see text] and release time [Formula: see text] is unknown in advance and is revealed at the instant of its arrival. Moreover, the actual processing time of job [Formula: see text] with learning effect is [Formula: see text], where [Formula: see text] and [Formula: see text] are non-negative parameters and [Formula: see text] denotes the starting time of prefabricated job [Formula: see text], respectively. When [Formula: see text], we propose an online algorithm with a competitive ratio of [Formula: see text]. Furthermore, the performance of the online algorithm is demonstrated by numerical experiments.
Differential Evolution Algorithm with Dual Information Guidance
Authors: Zhou Xinyu , Wu Yanlin , Peng Hu , Wu Shuixiu , Wang Mingwen
Published: January 2023

As an effective tool to solve continuous optimization problems, differential evolution (DE) algorithm has been widely used in numerous fields. To enhance the performance, in recent years, many DE variants have been developed based on the idea of multiple strategies. However, there still exists an issue for them that the strategy selection method relies on the historical search experience. The experience may be suitable for the problems with smooth fitness landscapes, but not for the problems with rugged fitness landscapes. To alleviate this issue, in this work, a new multiple strategies-based DE variant with dual information guidance is proposed, called DIGDE. In the DIGDE, to avoid the unreliable historical search experience, the fitness information and spatial information are utilized simultaneously as a guidance to estimate the evolutionary states for each individual, and then the most appropriate strategy can be chosen correspondingly. To verify the effectiveness of the DIGDE, 52 test functions are included in the experiments, and three well-established DE variants and four other evolutionary algorithms are involved in the performance comparison. The results show that the DIGDE achieves competitive performance on not only the result accuracy but also the convergence rate.
PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks
Authors: Vimala D. , Manikandan K.
Published: January 2023

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.
Family of Structure-Dependent Methods for Structural Dynamics
Authors: Meng Fantao , Zhao Jianfeng , Ruan Xingqun , Zhang Chunwei
Published: January 2023

A family of structure-dependent methods is proposed based on discrete control theory. Although the displacement and velocity expression of this family method are similar to those of the previously published method developed by Mohammad Rezaiee-Pajand, the structure-dependent parameters of this family are different from the previously published method. The family of structure-dependent methods is named the MUSE algorithm method. Based on discrete control theory, a new family of integration algorithms is proposed by using the poles of the Newmark-[Formula: see text] method. Theoretical analysis indicated that the MUSE algorithm method possesses properties of zero amplitude decay and is self-starting. Also, its Period Elongation can be reduced by parameter ‘[Formula: see text]’. Numerical examples show that parameter ‘[Formula: see text]’ introduced in this paper can improve control Period Elongation and improve the accuracy of this method.
Development of a YOLO-KCF Coupling Algorithm for Miniature Fixed-Wing UAVs in Target Detection and Tracking
Authors: Xiao Quan , Kong Linghua , Zou Cheng , Cai Guowei , Yu Kun
Published: January 2023

Target detection and tracking represent key challenges facing miniature fixed-wing unmanned aerial vehicles (UAVs), particularly at high cruising speeds. Therefore, this paper proposes a vision-based target detection and tracking algorithm that systematically couples two mainstream methods, namely, you only look once (YOLO) and kernel correlation filter (KCF) algorithms. This combination enables small fixed-wing UAVs to achieve reliable target detection and rapid target tracking. A customized vision-guidance module is constructed to implement this algorithm, and a dual-thread execution mechanism is developed to ensure that the computational resources are used effectively. A miniature fixed-wing UAV experimental platform is also constructed and evaluated. Flight experiments are performed, and the results demonstrate that the developed algorithm can achieve satisfactory detection and tracking accuracy for stationary and moving ground targets in complex environments.
ConvMS: Improving Convolutional Knowledge Graph Embeddings via Integrating Information of Multiple Subspaces
Authors: Li Yanhong , Su Housheng
Published: January 2023

Knowledge graphs are involved in more and more applications to further improve intelligence. Owing to the inherent incompleteness of knowledge graphs resulted from data updating and missing, a number of knowledge graph completion models are proposed in succession. To obtain better performance, many methods are of high complexity, making it time-consuming for training and inference. This paper proposes a simple but effective model using only shallow neural networks, which combines enhanced feature interaction and multi-subspace information integration. In the enhanced feature interaction module, entity and relation embeddings are almost peer-to-peer interacted via multi-channel 2D convolution. In the multi-subspace information integration module, entity and relation embeddings are projected to multiple subspaces to extract multi-view information to further boost performance. Extensive experiments on widely used datasets show that the proposed model outperforms a series of strong baselines. And ablation studies demonstrate the effectiveness of each submodule in the model.
AdaEnlight
Authors: Liu Sicong , Li Xiaochen , Zhou Zimu , Guo Bin , Zhang Meng , Shen Haocheng , Yu Zhiwen
Published: January 2023

The ubiquity of camera-embedded devices and the advances in deep learning have stimulated various intelligent mobile video applications. These applications often demand on-device processing of video streams to deliver real-time, high-quality services for privacy and robustness concerns. However, the performance of these applications is constrained by the raw video streams, which tend to be taken with small-aperture cameras of ubiquitous mobile platforms in dim light. Despite extensive low-light video enhancement solutions, they are unfit for deployment to mobile devices due to their complex models and and ignorance of system dynamics like energy budgets. In this paper, we propose AdaEnlight, an energy-aware low-light video stream enhancement system on mobile devices. It achieves real-time video enhancement with competitive visual quality while allowing runtime behavior adaptation to the platform-imposed dynamic energy budgets. We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
GPS-assisted Indoor Pedestrian Dead Reckoning
Authors: Zhou Heng , Maekawa Takuya
Published: January 2023

Indoor pedestrian dead reckoning (PDR) using embedded inertial sensors in smartphones has been actively studied in the ubicomp community. However, PDR relying only on inertial sensors suffers from the accumulation of errors from the sensors. Researchers have employed various indoor landmarks detectable by smartphone sensors such as magnetic fingerprints caused by elevators and Bluetooth signals from beacons with known coordinates to compensate for the errors. This study proposes a new type of indoor landmark that does not require additional device installation, e.g., beacons, and training data collection in a target environment, e.g., magnetic fingerprints, unlike existing landmarks. This study proposes the use of GPS signals received by a smartphone to correct the accumulated errors of the PDR. While it is impossible to locate the smartphone indoors using GPS satellites, the smartphone can receive signals at a window-side area through windows from satellites aligned with the orientation of the window normal. Based on this idea, we design a machine-learning-based module for detecting the proximity of a user to a window and the orientation of the window, which enables us to roughly determine the absolute coordinates of the smartphone and to correct the accumulated errors by referring to positions of window-side areas found in the floor plan of the environment. A key technical contribution of this study is designing the module, such that it can be trained based on data from environments other than the target environment yet work in any environment by extracting GPS-related information independent of wall orientation. We evaluated the effectiveness of the proposed method using sensor data collected in real environments.
WristAcoustic
Authors: Huh Jun Ho , Shin Hyejin , Kim HongMin , Cheon Eunyong , Song Youngeun , Lee Choong-Hoon , Oakley Ian
Published: January 2023

PIN and pattern lock are difficult to accurately enter on small watch screens, and are vulnerable against guessing attacks. To address these problems, this paper proposes a novel implicit biometric scheme based on through-wrist acoustic responses. A cue signal is played on a surface transducer mounted on the dorsal wrist and the acoustic response recorded by a contact microphone on the volar wrist. We build classifiers using these recordings for each of three simple hand poses (relax, fist and open), and use an ensemble approach to make final authentication decisions. In an initial single session study (N=25), we achieve an Equal Error Rate (EER) of 0.01%, substantially outperforming prior on-wrist biometric solutions. A subsequent five recall-session study (N=20) shows reduced performance with 5.06% EER. We attribute this to increased variability in how participants perform hand poses over time. However, after retraining classifiers performance improved substantially, ultimately achieving 0.79% EER. We observed most variability with the relax pose. Consequently, we achieve the most reliable multi-session performance by combining the fist and open poses: 0.51% EER. Further studies elaborate on these basic results. A usability evaluation reveals users experience low workload as well as reporting high SUS scores and fluctuating levels of perceived exertion: moderate during initial enrollment dropping to slight during authentication. A final study examining performance in various poses and in the presence of noise demonstrates the system is robust to such disturbances and likely to work well in wide range of real-world contexts.
EFRing
Authors: Chen Taizhou , Li Tianpei , Yang Xingyu , Zhu Kening
Published: January 2023

We present EFRing, an index-finger-worn ring-form device for detecting thumb-to-index-finger (T2I) microgestures through the approach of electric-field (EF) sensing. Based on the signal change induced by the T2I motions, we proposed two machine-learning-based data-processing pipelines: one for recognizing/classifying discrete T2I microgestures, and the other for tracking continuous 1D T2I movements. Our experiments on the EFRing microgesture classification showed an average within-user accuracy of 89.5% and an average cross-user accuracy of 85.2%, for 9 discrete T2I microgestures. For the continuous tracking of 1D T2I movements, our method can achieve the mean-square error of 3.5% for the generic model and 2.3% for the personalized model. Our 1D-Fitts'-Law target-selection study shows that the proposed tracking method with EFRing is intuitive and accurate for real-time usage. Lastly, we proposed and discussed the potential applications for EFRing.
Investigating In-Situ Personal Health Data Queries on Smartwatches
Authors: Rey Bradley , Lee Bongshin , Choe Eun Kyoung , Irani Pourang
Published: January 2023

Smartwatches enable not only the continuous collection of but also ubiquitous access to personal health data. However, exploring this data in-situ on a smartwatch is often reserved for singular and generic metrics, without the capacity for further insight. To address our limited knowledge surrounding smartwatch data exploration needs, we collect and characterize desired personal health data queries from smartwatch users. We conducted a week-long study (N = 18), providing participants with an application for recording responses that contain their query and current activity related information, throughout their daily lives. From the responses, we curated a dataset of 205 natural language queries. Upon analysis, we highlight a new preemptive and proactive data insight category, an activity-based lens for data exploration, and see the desired use of a smartwatch for data exploration throughout daily life. To aid in future research and the development of smartwatch health applications, we contribute the dataset and discuss implications of our findings.
ODSearch
Authors: Rawassizadeh Reza , Rong Yi
Published: January 2023

Mobile and wearable technologies have promised significant changes to the healthcare industry. Although cutting-edge communication and cloud-based technologies have allowed for these upgrades, their implementation and popularization in low-income countries have been challenging. We propose ODSearch, an On-device Search framework equipped with a natural language interface for mobile and wearable devices. To implement search, ODSearch employs compression and Bloom filter, it provides near real-time search query responses without network dependency. In particular, the Bloom filter reduces the temporal scope of the search and compression reduces the size of the data to be searched. Our experiments were conducted on a mobile phone and smartwatch. We compared ODSearch with current state-of-the-art search mechanisms, and it outperformed them on average by 53 times in execution time, 26 times in energy usage, and 2.3% in memory utilization.
PrinType
Authors: Liu Zongjian , He Jieling , Feng Jianjiang , Zhou Jie
Published: January 2023

We present PrinType, a fingerprint recognition based typing technique for virtual reality. Different regions of fingers covered by friction ridges are assigned to different keys (i.e. letters, numbers, punctuation, or functions). Once the thumb-worn fingerprint sensor touches a finger, the contact region (and its key) is recognized by matching the current image with registered templates. Using only a small sensor, PrinType turns each segment of all fingers into a touchable key. We designed keyboard layouts corresponding to three interaction sub-spaces: whole finger keyboard, fingertip keyboard, and single-handed keyboard. A 12-person user study was conducted to evaluate the performance of different strategies. Our user evaluation showed that participants achieved an average of 29.56, 32.38, and 34.22 WPM with 0.79%, 0.20%, and 0.21% not corrected error rate in the three strategies. In addition, we provided a detailed analysis of various micro metrics to further understand user performance and technical characteristics. Overall, PrinType is favored by users for its usability, efficiency, and novelty.
RF-Chain
Authors: Wang Ge , Shi Shouqian , Wang Minmei , Qian Chen , Zhao Cong , Ding Han , Xi Wei , Zhao Jizhong
Published: January 2023

Blockchain-based supply chains provide a new solution to decentralized multi-party product management. However, existing methods, including ID-based and cryptographic-based solutions, cannot achieve both counterfeit resistance and decentralization in supply chain management. We argue that this dilemma comes from the disconnection and inconsistency of the data records and physical product entities. This paper proposes RF-Chain, a novel decentralized supply chain management solution that uniquely combines data record authentication and physical-layer RFID tag authentication to effectively achieve credibility and counterfeit resistance. The main contribution of this work is to integrate physical-layer authentication of cheap commodity RFID tags into a blockchain-based information management system, and RF-Chain is the first to do so. The proposed cross-layer authentication can effectively defend against counterfeit attacks without relying on a central key management service. Real-world experiments utilizing the Ethereum (ETH) platform and more than 100 RFID tags demonstrate that RF-Chain is secure, effective, time-efficient, and cost-efficient.
Multi-Vib
Authors: Yang Yanni , Xu Huafeng , Chen Qianyi , Cao Jiannong , Wang Yanwen
Published: January 2023

Vibration measurement is vital for fault diagnosis of structures (e.g., machines and civil structures). Different structure components undergo distinct vibration patterns, which jointly determine the structure's health condition, thus demanding simultaneous multi-point vibration monitoring. Existing solutions deploy multiple accelerometers along with their power supplies or laser vibrometers on the monitored object to measure multi-point vibration, which is inconvenient and costly. Cameras provide a less expensive solution while heavily relying on good lighting conditions. To overcome these limitations, we propose a cost-effective and passive system, called Multi-Vib, for precise multi-point vibration monitoring. Multi-Vib is implemented using a single mmWave radar to remotely and separately sense the vibration displacement of multiple points via signal reflection. However, simultaneously detecting and monitoring multiple points on a single object is a daunting task. This is because most radar signals are scattered away from vibration points due to their tilted locations and shapes by nature, causing an extremely weak reflected signal to the radar. To solve this issue, we dedicatedly design a physical marker placed on the target point, which can force the direction of the reflected signal towards the radar and significantly increase the reflected signal strength. Another practical issue is that the reflected signal from each point endures interferences and noises from the surroundings. Thus, we develop a series of effective signal processing methods to denoise the signal for accurate vibration frequency and displacement estimation. Extensive experimental results show that the average errors in multi-point vibration frequency and displacement estimation are around 0.16Hz and 14μm, respectively.
HearFire
Authors: Wang Zheng , Wang Yanwen , Tian Mi , Shen Jiaxing
Published: January 2023

Indoor conflagration causes a large number of casualties and property losses worldwide every year. Yet existing indoor fire detection systems either suffer from short sensing range (e.g., ≤ 0.5m using a thermometer), susceptible to interferences (e.g., smoke detector) or high computational and deployment overhead (e.g., cameras, Wi-Fi). This paper proposes HearFire, a cost-effective, easy-to-use and timely room-scale fire detection system via acoustic sensing. HearFire consists of a collocated commodity speaker and microphone pair, which remotely senses fire by emitting inaudible sound waves. Unlike existing works that use signal reflection effect to fulfill acoustic sensing tasks, HearFire leverages sound absorption and sound speed variations to sense the fire due to unique physical properties of flame. Through a deep analysis of sound transmission, HearFire effectively achieves room-scale sensing by correlating the relationship between the transmission signal length and sensing distance. The transmission frame is carefully selected to expand sensing range and balance a series of practical factors that impact the system's performance. We further design a simple yet effective approach to remove the environmental interference caused by signal reflection by conducting a deep investigation into channel differences between sound reflection and sound absorption. Specifically, sound reflection results in a much more stable pattern in terms of signal energy than sound absorption, which can be exploited to differentiate the channel measurements caused by fire from other interferences. Extensive experiments demonstrate that HireFire enables a maximum 7m sensing range and achieves timely fire detection in indoor environments with up to 99.2% accuracy under different experiment configurations.
Lumos
Authors: Watson Amanda , Kendell Claire , Lingamoorthy Anush , Lee Insup , Weimer James
Published: January 2023

Spectroscopy, the study of the interaction between electromagnetic radiation and matter, is a vital technique in many disciplines. This technique is limited to lab settings, and, as such, sensing is isolated and infrequent. Thus, it can only provide a brief snapshot of the monitored parameter. Wearable technology brings sensing and tracking technologies out into everyday life, creating longitudinal datasets that provide more insight into the monitored parameter. In this paper, we describe Lumos, an open-source device for wearable spectroscopy research. Lumos can facilitate on-body spectroscopy research in health monitoring, athletics, rehabilitation, and more. We developed an algorithm to determine the spectral response of a medium with a mean absolute error of 13nm. From this, researchers can determine the optimal spectrum and create customized sensors for their target application. We show the utility of Lumos in a pilot study, sensing of prediabetes, where we determine the relevant spectrum for glucose and create and evaluate a targeted tracking device.
PrISM-Tracker
Authors: Arakawa Riku , Yakura Hiromu , Mollyn Vimal , Nie Suzanne , Russell Emma , DeMeo Dustin P. , Reddy Haarika A. , Maytin Alexander K. , Carroll Bryan T. , Lehman Jill Fain , Goel Mayank
Published: January 2023

A user often needs training and guidance while performing several daily life procedures, e.g., cooking, setting up a new appliance, or doing a COVID test. Watch-based human activity recognition (HAR) can track users' actions during these procedures. However, out of the box, state-of-the-art HAR struggles from noisy data and less-expressive actions that are often part of daily life tasks. This paper proposes PrISM-Tracker, a procedure-tracking framework that augments existing HAR models with (1) graph-based procedure representation and (2) a user-interaction module to handle model uncertainty. Specifically, PrISM-Tracker extends a Viterbi algorithm to update state probabilities based on time-series HAR outputs by leveraging the graph representation that embeds time information as prior. Moreover, the model identifies moments or classes of uncertainty and asks the user for guidance to improve tracking accuracy. We tested PrISM-Tracker in two procedures: latte-making in an engineering lab study and wound care for skin cancer patients at a clinic. The results showed the effectiveness of the proposed algorithm utilizing transition graphs in tracking steps and the efficacy of using simulated human input to enhance performance. This work is the first step toward human-in-the-loop intelligent systems for guiding users while performing new and complicated procedural tasks.
HIPPO
Authors: Yin Zhigang , Liyanage Mohan , Ottun Abdul-Rasheed , Paul Souvik , Zuniga Agustin , Nurmi Petteri , Flores Huber
Published: January 2023

Hand-grip strength is widely used to estimate muscle strength and it serves as a general indicator of the overall health of a person, particularly in aging adults. Hand-grip strength is typically estimated using dynamometers or specialized force resistant pressure sensors embedded onto objects. Both of these solutions require the user to interact with a dedicated measurement device which unnecessarily restricts the contexts where estimates are acquired. We contribute HIPPO, a novel non-intrusive and opportunistic method for estimating hand-grip strength from everyday interactions with objects. HIPPO re-purposes light sensors available in wearables (e.g., rings or gloves) to capture changes in light reflectivity when people interact with objects. This allows HIPPO to non-intrusively piggyback everyday interactions for health information without affecting the user's everyday routines. We present two prototypes integrating HIPPO, an early smart glove proof-of-concept, and a further optimized solution that uses sensors integrated onto a ring. We validate HIPPO through extensive experiments and compare HIPPO against three baselines, including a clinical dynamometer. Our results show that HIPPO operates robustly across a wide range of everyday objects, and participants. The force strength estimates correlate with estimates produced by pressure-based devices, and can also determine the correct hand grip strength category with up to 86% accuracy. Our findings also suggest that users prefer our approach to existing solutions as HIPPO blends the estimation with everyday interactions.
PhysiQ
Authors: Wang Hanchen David , Ma Meiyi
Published: January 2023

Physical therapy (PT) is crucial for patients to restore and maintain mobility, function, and well-being. Many on-site activities and body exercises are performed under the supervision of therapists or clinicians. However, the postures of some exercises at home cannot be performed accurately due to the lack of supervision, quality assessment, and self-correction. Therefore, in this paper, we design a new framework, PhysiQ, that continuously tracks and quantitatively measures people's off-site exercise activity through passive sensory detection. In the framework, we create a novel multi-task spatio-temporal Siamese Neural Network that measures the absolute quality through classification and relative quality based on an individual's PT progress through similarity comparison. PhysiQ digitizes and evaluates exercises in three different metrics: range of motions, stability, and repetition. We collect and annotate 31 participants' motion data with different levels of quality. Evaluation results show that PhysiQ recognizes the nuances in exercises, works with different numbers of repetitions, and achieves an accuracy of 89.67% in detecting levels of exercise quality and an average R-squared correlation of 0.949 in similarity comparison.
HFL(Z) Validity Checking for Automated Program Verification
Authors: Kobayashi Naoki , Tanahashi Kento , Sato Ryosuke , Tsukada Takeshi
Published: January 2023

We propose an automated method for checking the validity of a formula of HFL(Z), a higher-order logic with fixpoint operators and integers. Combined with Kobayashi et al.'s reduction from higher-order program verification to HFL(Z) validity checking, our method yields a fully automated, uniform verification method for arbitrary temporal properties of higher-order functional programs expressible in the modal mu-calculus, including termination, non-termination, fair termination, fair non-termination, and also branching-time properties. We have implemented our method and obtained promising experimental results.
Why Are Proofs Relevant in Proof-Relevant Models?
Authors: Kerinec Axel , Manzonetto Giulio , Olimpieri Federico
Published: January 2023

Relational models of λ-calculus can be presented as type systems, the relational interpretation of a λ-term being given by the set of its typings. Within a distributors-induced bicategorical semantics generalizing the relational one, we identify the class of ‘categorified’ graph models and show that they can be presented as type systems as well. We prove that all the models living in this class satisfy an Approximation Theorem stating that the interpretation of a program corresponds to the filtered colimit of the denotations of its approximants. As in the relational case, the quantitative nature of our models allows to prove this property via a simple induction, rather than using impredicative techniques. Unlike relational models, our 2-dimensional graph models are also proof-relevant in the sense that the interpretation of a λ-term does not contain only its typings, but the whole type derivations. The additional information carried by a type derivation permits to reconstruct an approximant having the same type in the same environment. From this, we obtain the characterization of the theory induced by the categorified graph models as a simple corollary of the Approximation Theorem: two λ-terms have isomorphic interpretations exactly when their B'ohm trees coincide.
Formally Verified Native Code Generation in an Effectful JIT: Turning the CompCert Backend into a Formally Verified JIT Compiler
Authors: Barrière Aurèle , Blazy Sandrine , Pichardie David
Published: January 2023

Modern Just-in-Time compilers (or JITs) typically interleave several mechanisms to execute a program. For faster startup times and to observe the initial behavior of an execution, interpretation can be initially used. But after a while, JITs dynamically produce native code for parts of the program they execute often. Although some time is spent compiling dynamically, this mechanism makes for much faster times for the remaining of the program execution. Such compilers are complex pieces of software with various components, and greatly rely on a precise interplay between the different languages being executed, including on-stack-replacement. Traditional static compilers like CompCert have been mechanized in proof assistants, but JITs have been scarcely formalized so far, partly due to their impure nature and their numerous components. This work presents a model JIT with dynamic generation of native code, implemented and formally verified in Coq. Although some parts of a JIT cannot be written in Coq, we propose a proof methodology to delimit, specify
and reason on the impure effects of a JIT. We argue that the daunting task of formally verifying a complete JIT should draw on existing proofs of native code generation. To this end, our work successfully reuses CompCert and its correctness proofs during dynamic compilation.
Finally, our prototype can be extracted and executed.
Proto-Quipper with Dynamic Lifting
Authors: Fu Peng , Kishida Kohei , Ross Neil J. , Selinger Peter
Published: January 2023

Quipper is a functional programming language for quantum computing. Proto-Quipper is a family of languages aiming to provide a formal foundation for Quipper. In this paper, we extend Proto-Quipper-M with a construct called
dynamic lifting
, which is present in Quipper. By virtue of being a circuit description language, Proto-Quipper has two separate runtimes: circuit generation time and circuit execution time. Values that are known at circuit generation time are called
parameters
, and values that are known at circuit execution time are called
states
. Dynamic lifting is an operation that enables a state, such as the result of a measurement, to be lifted to a parameter, where it can influence the generation of the next portion of the circuit. As a result, dynamic lifting enables Proto-Quipper programs to interleave classical and quantum computation. We describe the syntax of a language we call Proto-Quipper-Dyn. Its type system uses a system of modalities to keep track of the use of dynamic lifting. We also provide an operational semantics, as well as an abstract categorical semantics for dynamic lifting based on enriched category theory. We prove that both the type system and the operational semantics are sound with respect to our categorical semantics. Finally, we give some examples of Proto-Quipper-Dyn programs that make essential use of dynamic lifting.
Executing Microservice Applications on Serverless, Correctly
Authors: Kallas Konstantinos , Zhang Haoran , Alur Rajeev , Angel Sebastian , Liu Vincent
Published: January 2023

While serverless platforms substantially simplify the provisioning, configuration, and management of cloud applications, implementing correct services on top of these platforms can present significant challenges to programmers. For example, serverless infrastructures introduce a host of failure modes that are not present in traditional deployments. Individual serverless instances can fail while others continue to make progress, correct but slow instances can be killed by the cloud provider as part of resource management, and providers will often respond to such failures by re-executing requests. For functions with side-effects, these scenarios can create behaviors that are not observable in serverful deployments.

In this paper, we propose mu2sls, a framework for implementing microservice applications on serverless using standard Python code with two extra primitives: transactions and asynchronous calls. Our framework orchestrates user-written services to address several challenges, such as failures and re-executions, and provides formal guarantees that the generated serverless implementations are correct. To that end, we present a novel service specification abstraction and formalization of serverless implementations that facilitate reasoning about the correctness of a given application’s serverless implementation. This formalization forms the basis of the mu2sls prototype, which we then use to develop a few real-world microservice applications and show that the performance of the generated serverless implementations achieves significant scalability (3-5× the throughput of a sequential implementation) while providing correctness guarantees in the context of faults, re-execution, and concurrency.
MSWasm: Soundly Enforcing Memory-Safe Execution of Unsafe Code
Authors: Michael Alexandra E. , Gollamudi Anitha , Bosamiya Jay , Johnson Evan , Denlinger Aidan , Disselkoen Craig , Watt Conrad , Parno Bryan , Patrignani Marco , Vassena Marco , Stefan Deian
Published: January 2023

Most programs compiled to WebAssembly (Wasm) today are written in unsafe languages like C and C++.
Unfortunately, memory-unsafe C code remains unsafe when compiled to Wasm—and attackers can exploit
buffer overflows and use-after-frees in Wasm almost as easily as they can on native platforms. Memory-
Safe WebAssembly (MSWasm) proposes to extend Wasm with language-level memory-safety abstractions to
precisely address this problem. In this paper, we build on the original MSWasm position paper to realize this vision. We give a precise and formal semantics of MSWasm, and prove that well-typed MSWasm programs are, by construction, robustly memory safe. To this end, we develop a novel, language-independent memory-safety property based on colored memory locations and pointers. This property also lets us reason about the security guarantees of a formal C-to-MSWasm compiler—and prove that it always produces memory-safe programs (and preserves the semantics of safe programs). We use these formal results to then guide several implementations: Two compilers of MSWasm to native code, and a C-to-MSWasm compiler (that extends Clang). Our MSWasm compilers support different enforcement mechanisms, allowing developers to make security-performance trade-offs according to their needs. Our evaluation shows that on the PolyBenchC suite, the overhead of enforcing memory safety in software ranges from 22% (enforcing spatial safety alone) to 198% (enforcing full memory safety), and 51.7% when using hardware memory capabilities for spatial safety and pointer integrity.

More importantly, MSWasm’s design makes it easy to swap between enforcement mechanisms; as fast (especially hardware-based) enforcement techniques become available, MSWasm will be able to take advantage of these advances almost for free.