Miracle-dz/UniFAR-FTUs huggingface dataset:
IntSight: diagnosing SLO violations with in-band network telemetry

Performance requirements for many of today's high-perfor-mance networks are expressed as service-level objectives (SLOs), i.e., precise guarantees, typically on latency and bandwidth, that a user can expect from the network. For network operators, monitoring their own SLO compliance, and quickly diagnosing any violations, is a critical element for effective operations. Unfortunately, existing network architectures are not engineered for this purpose; there is no mechanism, for example, for the operator to monitor the 95th per-centile latency experienced by a customer. Data plane programmability has made per-packet measurements possible but brings the challenge of keeping the monitoring overhead low and practical. In this paper, we present IntSight, a system for highly accurate and fine-grained detection and diagnosis of SLO violations. The main contribution of IntSight is, building upon in-band telemetry, introducing path-wise computation of network metrics and selective generation of reports. We show the effectiveness of IntSight by way of two use cases. Our evaluation using real networks also shows that IntSight generates up to two orders of magnitude less monitoring traffic than state-of-the-art approaches. Furthermore, its processing and memory requirements are low and therefore compatible with currently existing programmable platforms.
Impact of Sino-US Commodity Trade Structure on China's Industrial Structure in the Backdrop of Trade Friction

Through empirical analysis, this article finds that Sino-US industrial manufactured goods trade is inconsistent with the development of China's primary and secondary industries; the United States has a deficit in China's merchandise trade, and China's heavy dependence on US primary product imports. The finished product trade has a positive impact on China's industrial -36+6structure. It can increase the added value and industrial technology of industrial finished products and adjust the industrial structure to enhance the promotion of the primary and secondary industries.At the same time, it advocates the diversification of China's export market, and improves independent innovation capabilities, and disperses Sino-US trade conflicts; enhances farmers' enthusiasm for planting and reduces China's dependence on US primary products.
RoCC: robust congestion control for RDMA

In this paper, we present RoCC, a robust congestion control approach for datacenter networks based on RDMA. RoCC leverages switch queue size as an input to a PI controller, which computes the fair data rate of flows in the queue, signaling it to the flow sources. The PI parameters are self-tuning to guarantee stability, rapid convergence, and fair and near-optimal throughput in a wide range of congestion scenarios. Our simulation and DPDK implementation results show that RoCC can achieve up to 7× reduction in PFC frames generated under high average load levels, compared to DCQCN. At the same time, RoCC can achieve up to 8× lower tail latency, compared to DCQCN and HPCC. We also find that RoCC does not require PFC. The functional components of RoCC are implementable in P4-based and fixed-function switch ASICs.
Adult Mouse Retina Explants: From ex vivo to in vivo Model of Central Nervous System Injuries

In mammals, adult neurons fail to regenerate following any insult to adult central nervous system (CNS), which leads to a permanent and irreversible loss of motor and cognitive functions. For a long time, much effort has been deployed to uncover mechanisms of axon regeneration in the CNS. Even if some cases of functional recovery have been reported, there is still a discrepancy regarding the functionality of a neuronal circuit upon lesion. Today, there is a need not only to identify new molecules implicated in adult CNS axon regeneration, but also to decipher the fine molecular mechanisms associated with regeneration failure. Here, we propose to use cultures of adult retina explants to study all molecular and cellular mechanisms that occur during CNS regeneration. We show that adult retinal explant cultures have the advantages to (i) recapitulate all the features observed in vivo, including axon regeneration induced by intrinsic factors, and (ii) be an ex vivo set-up with high accessibility and many downstream applications. Thanks to several examples, we demonstrate that adult explants can be used to address many questions, such as axon guidance, growth cone formation and cytoskeleton dynamics. Using laser guided ablation of a single axon, axonal injury can be performed at a single axon level, which allows to record early and late molecular events that occur after the lesion. Our model is the ideal tool to study all molecular and cellular events that occur during CNS regeneration at a single-axon level, which is currently not doable in vivo. It is extremely valuable to address unanswered questions of neuroprotection and neuroregeneration in the context of CNS lesion and neurodegenerative diseases.
Rapid Manipulation in Irradiance Induces Oxidative Free-Radical Release in a Fast-Ice Algal Community (McMurdo Sound, Antarctica)

Sea ice supports a unique assemblage of microorganisms that underpin Antarctic coastal food-webs, but reduced ice thickness coupled with increased snow cover will modify energy flow and could lead to photodamage in ice-associated microalgae. In this study, microsensors were used to examine the influence of rapid shifts in irradiance on extracellular oxidative free radicals produced by sea-ice algae. Bottom-ice algal communities were exposed to one of three levels of incident light for 10 days: low (0.5 μmol photons m−2 s−1, 30 cm snow cover), mid-range (5 μmol photons m−2 s−1, 10 cm snow), or high light (13 μmol photons m−2 s−1, no snow). After 10 days, the snow cover was reversed (either removed or added), resulting in a rapid change in irradiance at the ice-water interface. In treatments acclimated to low light, the subsequent exposure to high irradiance resulted in a ~400× increase in the production of hydrogen peroxide (H2O2) and a 10× increase in nitric oxide (NO) concentration after 24 h. The observed increase in oxidative free radicals also resulted in significant changes in photosynthetic electron flow, RNA-oxidative damage, and community structural dynamics. In contrast, there was no significant response in sea-ice algae acclimated to high light and then exposed to a significantly lower irradiance at either 24 or 72 h. Our results demonstrate that microsensors can be used to track real-time in-situ stress in sea-ice microbial communities. Extrapolating to ecologically relevant spatiotemporal scales remains a significant challenge, but this approach offers a fundamentally enhanced level of resolution for quantifying the microbial response to global change.
ePerceptive: energy reactive embedded intelligence for batteryless sensors

For long, we have studied tiny energy harvesters to liberate sensors from batteries. With remarkable progress in embedded deep learning, we are now re-imagining these sensors as intelligent compute nodes. Naturally, we are approaching a crossroad where sensor intelligence is meeting energy autonomy enabling maintenance-free swarm intelligence and unleashing a plethora of applications ranging from precision agriculture to ubiquitous asset tracking to infrastructure monitoring. One of the critical challenges, however, is to adapt intelligence fidelity in response to available energy to maximise the overall system availability. To this end, we present the design and implementation of ePerceptive: a novel framework for best-effort embedded intelligence, i.e., inference fidelity varies in proportion to the instantaneous energy supplied. ePerceptive operates on two core principles. First, it enables training a single deep neural network (DNN) to operate on multiple input resolutions without compromising accuracy or incurring memory overhead. Second, it modifies a DNN architecture by injecting multiple exits to guarantee valid, albeit lower-fidelity inferences in the event of energy interruption. The combination of these techniques offers a smooth adaptation between inference latency and recognition accuracy while matching the computational load to the available power budget. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and off-the-shelf RF and solar energy harvesters. Our evaluation of these batteryless sensors with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%.
Price Prediction of Peer-to-Peer Accommodation Based on Sentiment Analysis and POI Feature Extraction

In recent years, sharing economy is very popular around the world. Airbnb is the pioneer of sharing economy and the originator of peer-to-peer accommodation. This paper utilizes open source data on Airbnb website and Point-of-Interest data of Beijing to explore the influencing factors of housing rental. We perform sentiment analysis on textual reviews to obtain sentiment score and review features. By estimating the number of various Points of Interest around the house, we get geographical features. Price prediction of Airbnb houses has been done after extracting listing features, host features, review features and geographic features. After analysis, we use the Decision Tree (DT), the Support Vector Regressor (SVR), the Random Forest (RF) and other models to train the data, and finally achieve 76% of model accuracy of price prediction. There are three main contributions in this research. Firstly, the article proposes a new feature extraction method for Airbnb price prediction. It is verified on the data set that the method can effectively improve the model performance. Secondly, the article studies the influencing factors of Airbnb prices and explains the principles of smart pricing tools, which can provide hosts with improved strategies, helping them set more suitable prices and obtain greater benefits. Thirdly, the research results of this article are universal and suitable for online housing rental platforms, which can provide advice for the platform's recommended pricing and host's independent pricing.
ConSysT: tunable, safe consistency meets object-oriented programming

Data replication is essential in scenarios like geo-distributed datacenters, but poses challenges for data consistency. Developers adopt Strong consistency at the cost of performance or embrace Weak consistency and face a higher programming complexity. We argue that languages should associate consistency to data types. We present , a programming language and middleware that provides abstractions to specify consistency types, enabling mixing different consistency levels in the same application. Such mechanism is fully integrated with object-oriented programming and type system guarantees that different levels can only be mixed correctly.
Return of version downgrade attack in the era of TLS 1.3

Transport Layer Security (TLS) protocol is often vulnerable to version downgrade attacks, where a man-in-the-middle attacker interferes with the handshake protocol and leads the communicating parties to fall back from a higher version of TLS to lower ones, which are typically provided for backward compatibility. In order to thwart the downgrade attack, several defense mechanisms are adopted in most of the recent TLS versions. However, there have not been many studies on analyzing what conditions are needed to guarantee the theoretical security, and understanding how they are implemented in practice in the era of TLS 1.3. To understand the current deployment of downgrade protection mechanisms and their security in the real world, in this paper, we investigated ten major web browsers in five operating systems with diverse implementation conditions of TLS clients and servers. As a result, we identified that two network stacks of Microsoft and Apple are vulnerable to downgrade attacks. We then demonstrate TLS sessions can be downgraded from TLS 1.3 to 1.0 by exploiting the vulnerability. Drawing on our experiment, we analyze the root cause for the vulnerability, and present several mitigation strategies.
Tumor Necrosis Factor Receptors: Pleiotropic Signaling Complexes and Their Differential Effects

Since its discovery in 1975, TNFα has been a subject of intense study as it plays significant roles in both immunity and cancer. Such attention is well deserved as TNFα is unique in its engagement of pleiotropic signaling via its two receptors: TNFR1 and TNFR2. Extensive research has yielded mechanistic insights into how a single cytokine can provoke a disparate range of cellular responses, from proliferation and survival to apoptosis and necrosis. Understanding the intracellular signaling pathways induced by this single cytokine via its two receptors is key to further revelation of its exact functions in the many disease states and immune responses in which it plays a role. In this review, we describe the signaling complexes formed by TNFR1 and TNFR2 that lead to each potential cellular response, namely, canonical and non-canonical NF-κB activation, apoptosis and necrosis. This is followed by a discussion of data from in vivo mouse and human studies to examine the differential impacts of TNFR1 versus TNFR2 signaling.
Visual and inertial sensor fusion for mobile X-ray detector tracking: demo abstract

Robust 3D pose tracking of an object is a critical technique for various mobile sensing applications. Computer vision-based pose tracking method provides a cost-effective solution, but it is sensitive to occlusion and illumination change issues. In this work, we propose a novel visual-inertial sensor fusion framework and demonstrate the real-time implementation of a tightly-coupled sensor fusion algorithm: inertial perspective-n-point (IPNP) algorithm. With measurements from an inertial measurement unit (IMU), the prototype system only needs to detect two keypoints to track all six degrees of freedom of a planar object, e.g., a mobile X-ray detector, a 50% reduction on required number of keypoints, compared with the vision-based perspective-n-point algorithm.
Analysis of gaze points when looking at paintings and saliency map to improve the accuracy of ROI (region of interest): demo abstract

As a preliminary step to improve the accuracy of ROI (Region of Interest) based on human visual characteristics, we measured and analyzed the eye movements when looking at paintings. We assumed that there was regularity in viewers' eye movement when looking at a world-renowned painting. This time, we introduce our experimental method, and compare for gaze points distributions and saliency map.
Multiprotocol backscatter for personal IoT sensors

We present multiscatter, a novel backscatter design that can simultaneously work with multiple excitation signals for personal IoT sensors. Specifically, we show for the first time that the backscatter tag can identify various excitation signals in an ultra-low-power way, including WiFi, Bluetooth, and ZigBee. Further, we employ a new modulation approach, overlay modulation, that can leverage those excitation signals to convey tag data on top of productive data, which makes decoding both data possible with only a single personal radio. Since 2.4 GHz signals and personal radios are everywhere, multiscatter is readily deployable in our everyday IoT applications. We prototype multiscatter using an FPGA and various commodity radios. Extensive experiments show that for mixed 802.11b&n, Bluetooth and ZigBee signals, the average identification accuracy of four protocols is more than 93%. The maximal aggregate throughput of both productive and tag data is 278.4 kbps with a single Bluetooth radio, and the maximal backscatter ranges are 28 m, 22 m, and 20 m for WiFi, Bluetooth, and ZigBee, respectively. We also demonstrate that it can leverage excitation diversity to provide uninterrupted communication and greater throughput gains, whereas the single-protocol tag being idle when target carriers are not available.
Adversarial attacks on malware detection models for smartphones using reinforcement learning: PhD forum abstract

Malware analysis and detection is a rat race between malware designer and anti-malware community. Most of the current Smartphone antivirus(s) are based on the signature, heuristic and behaviour based mechanisms which are unable to detect advanced polymorphic and metamorphic malware. Recently, researchers have developed state-of-the-art Android malware detection systems based on machine learning and deep learning. However, these models are prone to adversarial attacks which threaten the anti-malware ecosystem. Therefore in this work, we are investigating the robustness of Android malware detection models against adversarial attacks. We crafted adversarial attacks using reinforcement learning against detection models built using a variety of machine learning (classical, bagging, boosting) and deep learning algorithms. We are designing two adversarial attack strategies, namely single-policy and multi-policy attack for white-box and grey-box scenarios which are based on adversary's knowledge about the system. We designed the attack using Q-learning where a malicious application(s) is modified to generate variants which will force the detection models to misclassify them. The goal of the attack policy is to convert maximum Android applications (such that they are misclassified) with minimum modifications while maintaining the functional and behavioural integrity of applications. Preliminary results show an average fooling rate of around 40% across twelve distinct detection models based on different classification algorithms. We are also designing defence against these adversarial attack using model retraining and distillation.
ERICA: enabling real-time mistake detection & corrective feedback for free-weights exercises

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that can handle multiple (even 15) concurrently exercising users; (b) develop a suite of statistical models to detect several commonplace repetition-level mistakes; and (c) experimentally study the efficacy of multiple in-situ corrective feedback strategies. Via an end-to-end evaluation of ERICA with 33 participants naturally performing 3 dumbbell-based exercises, we show that (a) ERICA identifies over 94% of mistakes during the first 5 repetitions of a set, (b) the resulting feedback is viewed favorably by 78% of users, and (c) the feedback is effective, reducing mistakes by 10+% during subsequent repetitions.
When filtering is not possible caching negatives with fingerprints comes to the rescue

Bloom filters are widely used in networking to accelerate checks and in particular to avoid accessing slow memories when a match will not be found. Unfortunately, filtering requires several on-chip memory bits per element and thus when the tables are large and the on-chip memory small is not applicable. In those cases, caching the most frequently accessed elements on-chip seems the only viable option. However, the key is typically formed by several packet header fields, which means that each cache entry consumes a significant amount of on-chip memory bits. In this paper, an efficient scheme to cache negatives, that is elements that will not find a match on the table is presented. In more detail, the proposed scheme enables the caching of negatives using less than 16 bits per entry regardless of the size of the key. This translates to a reduction of at least 6x in the size of the cache when used for example flow tracking.
Films ultraminces épitaxiés de MnFe2O4, CoFe2O4 et NiFe2O4 pour le filtrage de spin à température ambiante

Dans le domaine de l'electronique de spin, le filtrage de spin est un phenomene physique qui permet de generer des courants d'electrons polarises en spin grâce au transport dependant du spin a travers une barriere tunnel ferromagnetique. Alors que le filtrage de spin a temperature ambiante est tres attractif pour les applications, il existe peu de materiaux ayant les proprieties electriques et magnetiques requises. Les ferrites isolants XFe2O4 (X= Co, Ni, Mn), qui presentent des temperatures de Curie nettement superieures a 300 K, sont de bons candidats pour jouer le role de filtre a spin a temperature ambiante. Dans cette these, je presente une etude approfondie des ferrites MnFe2O4, NiFe2O4 et CoFe2O4 en films ultraminces epitaxies pour le filtrage de spin a temperature ambiante, en me penchant sur leur croissance par epitaxie par jets moleculaires. Les proprieties structurales, chimiques, magnetiques et electriques ont ete etudiees par plusieurs methodes de caracterisation in situ et ex situ, qui ont permis de demontrer le fort potentiel de ces oxydes pour jouer le role de barriere tunnel magnetique a temperature ambiante. Les filtres a spin ont ensuite ete integres dans des jonctions tunnel afin de faire des mesures de transport tunnel polarise en spin, soit par la methode de Meservey-Tedrow, soit par des mesures de magnetoresistance tunnel (TMR). Ces mesures ont revele pour la premiere fois un effet de filtrage de spin a travers MnFe2O4(111) et des effets de TMR ont ete obtenus dans des nano-jonctions tunnel a base de CoFe2O4(111), permettant d'obtenir la plus forte polarisation en spin actuelle a temperature ambiante par effet de filtrage de spin.
Removal of gaseous acetaldehyde and fine particles using corona discharge with wet electrostatic flocking electrode

Electrostatic precipitator (ESP) has been widely utilized to collect airborne particles in industrial and indoor ventilation systems. However, conventional ESP has not been used to remove gaseous pollutants from a gas flow. Recently, a wet-type ESP has been used for removing them. In this study, to improve the removal performance of gaseous pollutants and particulate matters, a wet-type ESP with a wet electrostatic flocking electrode was experimentally studied by measuring the removal efficiency of acetaldehyde and fine particles. And a carbon fabric plate as a collecting electrode was suggested to effectively remove ozone and fine particles in ESPs. Removal efficiency of acetaldehyde (CH3CHO: about 30 ppm) in one-pass test was about 68.2% at 8.4 W of input power with residence time of 2.1 s and high collection efficiency was obtained to submicron particles (0.3-0.5 Pm in diameter) with residence time of 1.05 s. The combined system of the wet type ESP and ESP with a carbon fabric plate electrode also was examined for removing them. These results suggest that the wet-type ESP has a potential of the simultaneous removal of gaseous and particulate pollutants.