Miracle-dz/UniFAR-FTUs huggingface dataset:
CD45RA, CD8β, and IFNγ Are Potential Immune Biomarkers of Human Cognitive Function

There is increasing evidence that in humans the adaptive immunological system can influence cognitive functions of the brain. We have undertaken a comprehensive immunological analysis of lymphocyte and monocyte populations as well as of HLA molecules expression in a cohort of elderly volunteers (age range, 64–101) differing in their cognitive status. Hereby, we report on the identification of a novel signature in cognitively impaired elderly characterized by: (1) elevated percentages of CD8+ T effector-memory cells expressing high levels of the CD45RA phosphate receptor (Temra hi); (2) high percentages of CD8+ T cells expressing high levels of the CD8β chain (CD8βhi); (3) augmented production of IFNγ by in vitro activated CD4+ T cells. Noteworthy, CD3+CD8+ Temra hi and CD3+CD8βhi cells were associated with impaired cognition. Cytomegalovirus seroprevalence showed that all volunteers studied but one were CMV positive. Finally, we show that some of these phenotypic and functional features are associated with an increased frequency of the HLA-B8 serotype, which belongs to the ancestral haplotype HLA-A1, Cw7, B8, DR3, DQ2, among cognitively impaired volunteers. To our knowledge, this is the first proof in humans linking the amount of cell surface CD45RA and CD8β chain expressed by CD8+ Temra cells, and the amount of IFNγ produced by in vitro activated CD4+ T cells, with impaired cognitive function in the elderly.
In situ Generated Ru(0)-HRO@Na-β From Hydrous Ruthenium Oxide (HRO)/Na-β: An Energy-Efficient Catalyst for Selective Hydrogenation of Sugars

A green process for the hydrogenation of sugars to sugar alcohols was designed in aqueous medium using hydrous ruthenium oxide (HRO) as a pre-catalyst supported on Na-β zeolite. Under optimized reaction conditions, sugars such as xylose, glucose, and mannose converted completely to the corresponding sugar alcohols xylitol, sorbitol, and mannitol with 100% selectivity. The pre-catalyst (HRO) is converted in situ to active Ru(0) species during the reaction under H2, which is responsible for the hydrogenation. The catalyst was recyclable up to five cycles with no loss in activity. The reduction of HRO to the active Ru(0) species is dependent on the reaction temperature and H2 pressure. Ru(0) formation increased and consequently an increased hydrogenation of sugars was observed with an increase in reaction temperature and hydrogen pressure. Further, in situ generation of Ru(0) from HRO was assessed in different solvents such as water, methanol, and tetrahydrofuran; aqueous medium was found to be the most efficient in reducing HRO. This work further demonstrates the use of supported HRO as an efficient pre-catalyst for biomass-based hydrogenation reactions.
Alcohol Consumption in China Before and During COVID-19: Preliminary Results From an Online Retrospective Survey

Background: Alcohol is an important aspect of Chinese culture, and alcohol use has been traditionally accepted in China. People with stress, anxiety, and depression may use more alcohol. More people reported symptoms of anxiety and depression during the outbreak of COVID-19. Thus, people may drink more alcohol during the outbreak of COVID-19 than before COVID-19. Methods: An online retrospective survey was conducted on a total sample of 2,229 participants. Drinking behaviors before and during COVID-19, current risky drinking and hazardous drinking, and the association between high-risk drinking and mental health problems (depression, anxiety, and stress) were assessed via self-reported measures on the Alcohol Use Disorders Identification Test (AUDIT) and the 21-item Depression Anxiety Stress Scales (DASS−21). Results: This study found that, compared with before COVID-19, alcohol consumption was slightly decreased during COVID-19 (from 3.5 drinks to 3.4 drinks, p = 0.035) in the overall sample. Most (78.7%) alcohol drinkers were males. Before and during COVID-19, males consumed more drinks per week (4.2 and 4.0 vs. 1.3 and 1.2 drinks), had a higher percentage of heavy drinking (8.1 and 7.7% vs. 4.4 and 2.7%), and more drinking days per week (2.1 and 2.1 vs. 1.0 and 0.9 days). Males also had more risky drinking (43.2 vs. 9.3%) and hazardous drinking (70.2 vs. 46.6%) than female counterparts. This study also found that high-risk drinking predicted anxiety in females. Conclusions: This study suggests a slight reduction in alcohol consumption during COVID-19. However, hazardous drinking is common, especially among male alcohol drinkers. Males consumed more alcohol, had more risky and hazardous drinking than female counterparts both before and during COVID-19. Public health policy makers should pay more attention to developing effective, population-based strategies to prevent harmful alcohol consumption.
A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Introduction: Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores. Methods: A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n=1,244) and validation dataset (n=100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality. Results: A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80-0.84) and 0.84 (95% CI: 0.81-0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78-0.92) compared to abbMEDS (0.63,0.54-0.73), mREMS (0.63,0.54-0.72) and internal medicine physicians (0.74,0.65-0.82). Conclusion: Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.
TRIZ Technical Contradiction Extraction Method Based on Patent Semantic Space Mapping

Analyzing technical contradictions according to TRIZ theory can help us solve the innovative and inventive problems effectively. However, the existing analysis and extraction methods of technical contradiction mainly rely on manual rule formulation and make less use of the semantics information, which limits the improvement of the efficiency and accuracy of the extraction. Consequently, this paper proposed an extraction method based on patent semantic space mapping. It adopted the Doc2Vec model to construct the semantic space of the patent text and trained extraction model by using feature vectors covering rich semantic relationships, which could make us recognize the technical contradictions from patents better. This paper used patent data in the automobile field as a sample to perform experiments. The accuracy of the technical contradiction recognition was improved compared with the baseline model based on rule formulation.
Accelerating Chip Design with Machine Learning

As Moore's law has provided an exponential increase in chip transistor density, the unique features we can now include in large chips are no longer predominantly limited by area constraints. Instead, new capabilities are increasingly limited by the engineering effort associated with digital design, verification, and implementation. As applications demand more performance and energy efficiency from specialization in the post -Moore's-law era, we expect required complexity and design effort to increase.
The Island of Pain: a Virtual Reality Experience for Patients with Chronic Pain

Millions of people around the world suffer from chronic pain. Chronic pain can be managed with drugs or psychological techniques. However, drugs have side effects and psychological treatment is expensive. In our Virtual Reality experience patients learn how to manage their pain by following voice-over instructions of a clinical psychologist and through interaction with a virtual pain character. This Virtual Reality experience mimics one of the sessions from a psychological intervention tailored for chronic pain sufferers. The objective of our work is to develop a self-contained training program that would supplement the clinical psychologist and would be more entertaining, scalable and accessible than traditional therapy.
Towards checkpoint placement for dynamic memory allocation in intermittent computing

Energy harvesting allows computational devices to run without a battery, opening new application domains of computing. Such devices work under an intermittent computing model, where the system may power cycle several times a second. To ensure progress, intermittent computing uses checkpoints, with much work being dedicated to this direction. However, no existing approaches handle programs using dynamically allocated memory in the intermittent computing model. We pose this as a challenge area, demonstrate the complexities of checkpointing in this space, and propose key characteristics of an effective solution.
Indoor positioning system in visually-degraded environments with millimetre-wave radar and inertial sensors: demo abstract

Positional estimation is of great importance in the public safety sector. Emergency responders such as fire fighters, medical rescue teams, and the police will all benefit from a resilient positioning system to deliver safe and effective emergency services. Unfortunately, satellite navigation (e.g., GPS) offers limited coverage in indoor environments. It is also not possible to rely on infrastructure based solutions. To this end, wearable sensor-aided navigation techniques, such as those based on camera and Inertial Measurement Units (IMU), have recently emerged recently as an accurate, infrastructure-free solution. Together with an increase in the computational capabilities of mobile devices, motion estimation can be performed in real-time. In this demonstration, we present a real-time indoor positioning system which fuses millimetre-wave (mmWave) radar and IMU data via deep sensor fusion. We employ mmWave radar rather than an RGB camera as it provides better robustness to visual degradation (e.g., smoke, darkness, etc.) while at the same time requiring lower computational resources to enable runtime computation. We implemented the sensor system on a handheld device and a mobile computer running at 10 FPS to track a user inside an apartment. Good accuracy and resilience were exhibited even in poorly illuminated scenes.
Meeting SLOs in cross-platform NFV

Network Functions (NFs) perform on-path processing of network traffic. ISPs are deploying NF Virtualization (NFV) with software NFs run on commodity servers. ISPs aim to ensure that NF chains, directed acyclic graphs of NFs, do not violate Service Level Objectives (SLOs) promised by the ISP to its customers. To meet SLOs, NFV systems sometimes leverage on-path hardware (such as programmable switches and smart NICs) to accelerate NF execution. Lemur places and executes NF chains across heterogeneous hardware while meeting SLOs. Lemur's novel placement algorithm yields an SLO-satisfying NF placement while weighing many constraints: hardware memory and processing stages, server cores, link capacity, NF profiles, and NF chain interactions. Lemur's metacompiler automatically generates code and rules (in P4, Python, eBPF, C++, and OpenFlow) to stitch cross-platform NF chain execution while also optimizing resource usage. Our experiments show that Lemur is alone among competing strategies in meeting SLOs for canonical NF chains while maximizing marginal throughput (the traffic rate in excess of the service-level objective).
Physical distance monitoring system for COVID-19 using raspberry Pi and a monocular camera: poster abstract

During the global pandemic of coronavirus disease 2019 (COVID-19), it is crucial to minimize the spread of the infection until effective drugs and vaccines are developed. Therefore, close contact must be reduced to prevent human-to-human transmission. We propose a simplified inexpensive system using a monocular camera combined with Raspberry Pi, a small single-board computer, to measure physical distancing and warn people of their proximity with others.
Interval Validity Testing and Optimization in Quantitative Trading

Machine learning algorithm has been widely used in quantitative investment, but there are some problems in factor testing, factor construction and objective of the loss function. First of all, this paper constructs the style index of the CSI 300 index, including institutional index, QFII index and shareholder concentration index. From the CSI 300 index and these three indexes, four factors are extracted respectively to form 16 factors we will test and use in this paper. Secondly, traditional factor testing method tests the linear validity or sequence validity. In this paper, we propose a method to test the interval validity because factors may validate internally instead of linearly. Then, we can discretize the factors and complete the feature engineering. Third, current machine learning applications in the quantitative trading are through the way of discretization of the rate of return and minimizing the loss function. But it ignores a lot of information of the rate of return. Therefore, this paper reconstructs the objective function of machine learning, modifies it to maximize the cumulative rate of return. Finally we use the heuristic algorithm Particle Swaps Optimization (PSO) to solve the equation, and obtains the corresponding parameters. Finally, we use the dataset from February 9th, 2009, to October 24th, 2019, to back testing, with 80% training data. The annualized return of the strategy is 24.96%, with the maximum withdrawal of 17.2%.
Role of in vitro testing in food allergy

Oral food challenges remain the gold standard for the diagnosis of food allergy. Nevertheless, the allergy workup is based on the presence of a clinical history, which is evocative of an immune‐allergic reaction, and the first assessment is usually the performance of skin prick tests. Based on these results, allergists are used to evaluate the presence of serum‐specific IgE, which are today the most commonly prescribed in vitro test for the evaluation of a possible food allergy. Other in vitro tests include the basophil activation test, that is becoming more and more employed by clinicians and not only by researchers, and the evaluation of serum IgG4, which is still an issue of debate in the allergy community. The present paper reviews the use of these in vitro tests for the diagnosis of food allergy.
AalWiNes: a fast and quantitative what-if analysis tool for MPLS networks

We present an automated what-if analysis tool AalWiNes for MPLS networks which allows us to verify both logical properties (e.g., related to the policy compliance) as well as quantitative properties (e.g., concerning the latency) under multiple link failures. Our tool relies on weighted pushdown automata, a quantitative extension of classic automata theory, and takes into account the actual dataplane configuration, rendering it especially useful for debugging. In particular, our tool collects the different router forwarding tables and then builds a pushdown system, on which quantitative reachability is performed based on an expressive query language. Our experiments show that our tool outperforms state-of-the-art approaches (which until now have been restricted to logical properties) by several orders of magnitude; furthermore, our quantitative extension only entails a moderate overhead in terms of runtime. The tool comes with a platform-independent user interface and is publicly available as open-source, together with all other experimental artefacts.
The Business Cycle, Market Power, and Corporate Investment

This article uses data from CSMAR database of Chinese A-share listed companies from 2003 to 2014 to study the relationship between market power and corporate investment under different economic cycle conditions. The empirical results show that companies with strong market power tend to invest more, while companies with weak market tend to invest less. Then cross terms are introduced to test the impact of business cycle and market power on corporate investment. We find that at different stages of the business cycle, companies with different market power have different investment behaviors: when the economy expands, the positive effect of market power on corporate investment will increase; when the economy is in recession, the positive effect of market power on corporate investment will diminish. The test results of the mediation effect model show that market power will influence corporate investment through financing and profitable intermediary channels, and this effect is not affected by economic fluctuations. Our conclusions hold after testing for robustness, endogenousness, and heterogeneity. This article studies the relationship between market power and corporate investment from the perspective of the business cycle, and provides new theoretical basis for related research at the company level and the macroeconomic level.
PReGO: a generative methodology for satisfying real-time requirements on COTS-based systems: definition and experience report

Satisfying real-time requirements in cyber-physical systems is challenging as timing behaviour depends on the application software, the embedded hardware, as well as the execution environment. This challenge is exacerbated as real-world, industrial systems often use unpredictable hardware and software libraries or operating systems with timing hazards and proprietary device drivers. All these issues limit or entirely prevent the application of established real-time analysis techniques. In this paper we propose PReGO, a generative methodology for satisfying real-time requirements in industrial commercial-off-the-shelf (COTS) systems. We report on our experience in applying PReGO to a use-case: a Search & Rescue application running on a fixed-wing drone with COTS components, including an NVIDIA Jetson board and a stock Ubuntu/Linux. We empirically evaluate the impact of each integration step and demonstrate the effectiveness of our methodology in meeting real-time application requirements in terms of deadline misses and energy consumption.
Palmtrie: a ternary key matching algorithm for IP packet filtering rules

Network security has become crucial to our society and industry. A firewall is an essential function in network operations for security enhancement. Network access control lists (ACLs) have been used to describe multi-layer security rules to determine whether a packet is passed or dropped by the firewall. An entry in ACLs contains so-called don't care bits, and consequently, ACL matching is generalized as a ternary matching problem. As ternary matching typically relies on dedicated hardware, high-performance ternary matching with commodity CPUs is challenging. We propose a practical algorithm for network ACL matching trie, named Palmtrie, that allows the multi-bit stride extension to achieve better performance. We evaluate the Palmtrie using synthetic ACLs that emulate an existing campus network and Internet backbone policies. The evaluation results demonstrate that the Palmtrie outperforms the existing ACL matching algorithms on extensive ACLs. For example, the lookup performance of the optimized Palmtrie achieves 4.76 times faster than the algorithm implemented in the widely used library (DPDK) for the scanning attack traffic on an ACL with one million entries. We also prove that the Palmtrie solves the problem with the build time of data structures in the existing algorithms.
SoC Design Automation with ML – It's Time for Research

The AI-hype started a few years ago, with advances in object recognition. Soon the EDA research community made proposals on applying AI in EDA and all major players announced new AI-based tools at DAC 2018. Unfortunately, few new AI-based EDA-tools made it to productive use today. This talk analyses general challenges of AI in EDA, outlines promising use cases, and motivates more AI research in EDA: More HI (=Human Intelligence) is needed to make AI successful in EDA.
Remote Virtual Showdown

The virtual reality was originally developed for people with normal vision and therefore, people with visual impairment (PVI) had difficulties in accessing virtual reality (VR). In this project, we developed a virtual showdown, a VR sports game for PVI which supports multimodal interfaces. The showdown is one of the sports in Paralympic games, in which PVI plays by using audio feedback and haptic feedback. To implement the showdown in VR, we used the Head Related Transfer Function (HRTF) which allows PVI to recognize the location of a virtual ball with a 3D spatial sound. Our showdown game supports two modes: (1) player vs. artificial intelegenc (AI) agent (PvA) mode where PVI plays against an AI agent, and (2) player vs. player (PvP) mode where two players can play against each other remotely. With the remote virtual showdown, PVI not only experiences the virtual reality but also exercises themselves physically. Through the virtual showdown, PVI can play the game with ordinary sighted people.
Aloba: rethinking ON-OFF keying modulation for ambient LoRa backscatter

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving good tradeoff between transmission range and link throughput. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. The design of Aloba completely unleashes the backscatter tag's ability in OOK modulation and achieves flexible data rate at different transmission range. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5--199.4 Kbps data rate at various distances, 10.4--52.4X higher than PLoRa.