Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Tail Latency in Commodity Datacenters using Forward Error Correction

Long tail latency of short flows (or messages) greatly affects user-facing applications in datacenters. Prior solutions to the problem introduce significant implementation complexities, such as global state monitoring, complex network control, or non-trivial switch modifications. While promising superior performance, they are hard to implement in practice. This paper presents CloudBurst, a simple, effective yet readily deployable solution achieving similar or even better results without introducing the above complexities. At its core, CloudBurst explores forward error correction (FEC) over multipath - it proactively spreads FEC-coded packets generated from messages over multipath in parallel, and recovers them with the first few arriving ones. As a result, CloudBurst is able to obliviously exploit underutilized paths, thus achieving low tail latency. We have implemented CloudBurst as a user-space library, and deployed it on a testbed with commodity switches. Our testbed and simulation experiments show the superior performance of CloudBurst. For example, CloudBurst achieves 63.69% and 60.06% reduction in 99th percentile message/flow completion time (FCT) compared to DCTCP and PIAS, respectively.
A first-order primal-dual method with adaptivity to local smoothness

We consider the problem of finding a saddle point for the convex-concave objective $\min_x \max_y f(x) + \langle Ax, y\rangle - g^*(y)$, where $f$ is a convex function with locally Lipschitz gradient and $g$ is convex and possibly non-smooth. We propose an adaptive version of the Condat-V\~u algorithm, which alternates between primal gradient steps and dual proximal steps. The method achieves stepsize adaptivity through a simple rule involving $\|A\|$ and the norm of recently computed gradients of $f$. Under standard assumptions, we prove an $\mathcal{O}(k^{-1})$ ergodic convergence rate. Furthermore, when $f$ is also locally strongly convex and $A$ has full row rank we show that our method converges with a linear rate. Numerical experiments are provided for illustrating the practical performance of the algorithm.
Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination

In this paper, we propose Normality-Calibrated Autoencoder (NCAE), which can boost anomaly detection performance on the contaminated datasets without any prior information or explicit abnormal samples in the training phase. The NCAE adversarially generates high confident normal samples from a latent space having low entropy and leverages them to predict abnormal samples in a training dataset. NCAE is trained to minimise reconstruction errors in uncontaminated samples and maximise reconstruction errors in contaminated samples. The experimental results demonstrate that our method outperforms shallow, hybrid, and deep methods for unsupervised anomaly detection and achieves comparable performance compared with semi-supervised methods using labelled anomaly samples in the training phase. The source code is publicly available on `https://github.com/andreYoo/NCAE_UAD. git'.
Privacy Aware Person Detection in Surveillance Data

Crowd management relies on inspection of surveillance video either by operators or by object detection models. These models are large, making it difficult to deploy them on resource constrained edge hardware. Instead, the computations are often offloaded to a (third party) cloud platform. While crowd management may be a legitimate application, transferring video from the camera to remote infrastructure may open the door for extracting additional information that are infringements of privacy, like person tracking or face recognition. In this paper, we use adversarial training to obtain a lightweight obfuscator that transforms video frames to only retain the necessary information for person detection. Importantly, the obfuscated data can be processed by publicly available object detectors without retraining and without significant loss of accuracy.
Online Facility Location with Linear Delay

In the problem of online facility location with delay, a sequence of n clients appear in the metric space, and they need to be eventually connected to some open facility. The clients do not have to be connected immediately, but such a choice comes with a certain penalty: each client incurs a waiting cost (equal to the difference between its arrival and its connection time). At any point in time, an algorithm may decide to open a facility and connect any subset of clients to it. That is, an algorithm needs to balance three types of costs: cost of opening facilities, costs of connecting clients, and the waiting costs of clients. We study a natural variant of this problem, where clients may be connected also to an already open facility, but such action incurs an extra cost: an algorithm pays for waiting of the facility (a cost incurred separately for each such “late” connection). This is reminiscent of online matching with delays, where both sides of the connection incur a waiting cost. We call this variant two-sided delay to differentiate it from the previously studied one-sided delay , where clients may connect to a facility only at its opening time. We present an O (1)-competitive deterministic algorithm for the two-sided delay variant. Our approach is an extension of the approach used by Jain, Mahdian and Saberi [STOC 2002] for analyzing the performance of offline algorithms for facility location. To this end, we substantially simplify the part of the original argument in which a bound on the sequence of factor-revealing LPs is derived. We then show how to transform our O (1)-competitive algorithm for the two-sided delay variant to O (log n/ log log n )-competitive deterministic algorithm for one-sided delays. This improves the known O (log n ) bound by Azar and Touitou [FOCS 2020]. We note that all previous online algorithms for problems with delays in general metrics have at least logarithmic ratios.
Deep Calibration of Interest Rates Model

For any financial institution it is a necessity to be able to apprehend the behavior of interest rates. Despite the use of Deep Learning that is growing very fastly, due to many reasons (expertise, ease of use, .) classic rates models such as CIR, or the Gaussian family are still being used widely. We propose to calibrate the five parameters of the G2++ model using Neural Networks. To achieve that, we construct synthetic data sets of parameters drawn uniformly from a reference set of parameters calibrated from the market. From those parameters, we compute Zero-Coupon and Forward rates and their covariances and correlations. Our first model is a Fully Connected Neural network and uses only covariances and correlations. We show that covariances are more suited to the problem than correlations. The second model is a Convulutional Neural Network using only Zero-Coupon rates with no transformation. The methods we propose perform very quickly (less than 0.3 seconds for 2 000 calibrations) and have low errors and good fitting.
Feedback-Induced Flutter Oscillations in a Flexible Tail-Like Appendage for Underwater Propulsion

Oscillating propulsors can provide both propulsive and maneuvering forces to an underwater vehicle. Because the working area is the same, it is possible to provide maneuvering forces that are similar in magnitude to that of the propulsive forces. An oscillating propulsor in the form of a flexible beam is investigated; the flexible beam is appended to an underwater vehicle by a pin joint and actuated by a motor. It is shown that the flexible propulsor can be driven into post-flutter limit cycle oscillations using feedback of the state of the propulsor. The limit cycle oscillations result in traveling waves, which in turn generate propulsive forces. The elastic strain of the propulsor is used to provide feedback; it is shown that changing the location of strain measurement can result in a rich set of stability transitions, each stability transition is associated with a specific mode of flutter-based propulsion with unique thrust and efficiency characteristics.
KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System

Transiting hot Jupiters present a unique opportunity to measure absolute planetary masses due to the magnitude of their radial velocity signals and known orbital inclination. Measuring planet mass is critical to understanding atmospheric dynamics and escape under extreme stellar irradiation. Here we present the ultrahot Jupiter system KELT-9 as a double-lined spectroscopic binary. This allows us to directly and empirically constrain the mass of the star and its planetary companion without reference to any theoretical stellar evolutionary models or empirical stellar scaling relations. Using data from the PEPSI, HARPS-N, and TRES spectrographs across multiple epochs, we apply least-squares deconvolution to measure out-of-transit stellar radial velocities. With the PEPSI and HARPS-N data sets, we measure in-transit planet radial velocities using transmission spectroscopy. By fitting the circular orbital solution that captures these Keplerian motions, we recover a planetary dynamical mass of 2.17 ± 0.56 M J and stellar dynamical mass of 2.11 ± 0.78 M ⊙, both of which agree with the discovery paper. Furthermore, we argue that this system, as well as systems like it, are highly overconstrained, providing multiple independent avenues for empirically cross-validating model-independent solutions to the system parameters. We also discuss the implications of this revised mass for studies of atmospheric escape.
On Families of Numerical Semigroups with two Coprime Generators and Dimension three

This paper examines in a new way some known facts about numerical semigroups especially when the number of minimal generators (that is the embedding dimension) is at most three and at least two minimal generators are coprime. For such semigroups, an algorithm is re-investigated to find the pseudo-Frobenius numbers. A certain family of $n$ dimensional numerical semigroups of type at most $n-1$ is also given.
Efficient and Near-optimal Algorithms for Sampling Small Connected Subgraphs

We study the following problem: Given an integer k ≥ 3 and a simple graph G, sample a connected induced k-vertex subgraph of G uniformly at random. This is a fundamental graph mining primitive with applications in social network analysis, bioinformatics, and more. Surprisingly, no efficient algorithm is known for uniform sampling; the only somewhat efficient algorithms available yield samples that are only approximately uniform, with running times that are unclear or suboptimal. In this work, we provide: (i) a near-optimal mixing time bound for a well-known random walk technique, (ii) the first efficient algorithm for truly uniform graphlet sampling, and (iii) the first sublinear-time algorithm for ε-uniform graphlet sampling.
Crosstalk between gut microbiota and sepsis

Abstract Sepsis is an overwhelming inflammatory response to microbial infection. Sepsis management remains a clinical challenge. The role of the gut microbiome in sepsis has gained some attention. Recent evidence has demonstrated that gut microbiota regulate host physiological homeostasis mediators, including the immune system, gut barrier function and disease susceptibility pathways. Therefore, maintenance or restoration of microbiota and metabolite composition might be a therapeutic or prophylactic target against critical illness. Fecal microbiota transplantation and supplementation of probiotics are microbiota-based treatment methods that are somewhat limited in terms of evidence-based efficacy. This review focuses on the importance of the crosstalk between the gastrointestinal ecosystem and sepsis to highlight novel microbiota-targeted therapies to improve the outcomes of sepsis treatment.
A polyketide synthase gene cluster required for pathogenicity of Pseudocercospora fijiensis on banana

Pseudocercospora fijiensis is the causal agent of the highly destructive black Sigatoka disease of banana. Previous research has focused on polyketide synthase gene clusters in the fungus, given the importance of polyketide pathways in related plant pathogenic fungi. A time course study of expression of the previously identified PKS7-1, PKS8-2, and PKS10-2 gene clusters showed high expression of all three PKS genes and the associated clustered genes in infected banana plants from 2 weeks post-inoculation through 9 weeks. Engineered transformants silenced for PKS8-2 and PKS10-2 were developed and tested for pathogenicity. Inoculation of banana plants with silencing transformants for PKS10-2 showed significant reduction in disease symptoms and severity that correlated with the degree of silencing in the conidia used for inoculation, supporting a critical role for PKS10-2 in disease development. Unlike PKS10-2, a clear role for PKS8-2 could not be determined. Two of four PKS8-2 silencing transformants showed reduced disease development, but disease did not correlate with the degree of PKS8-2 silencing in the transformants. Overall, the degree of silencing obtained for the PKS8-2 transformants was less than that obtained for the PKS10-2 transformants, which may have limited the utility of the silencing strategy to identify a role for PKS8-2 in disease. Orthologous PKS10-2 clusters had previously been identified in the related banana pathogens Pseudocercospora musae and Pseudocercospora eumusae. Genome analysis identified orthologous gene clusters to that of PKS10-2 in the newly sequenced genomes of Pseudocercospora fuligena and Pseudocercospora cruenta, pathogens of tomato and cowpea, respectively. Our results support an important role for the PKS10-2 polyketide pathway in pathogenicity of Pseudocercospora fijiensis, and suggest a possible role for this pathway in disease development by other Pseudocercospora species.
Chronic marijuana usage by human pancreas donors is associated with impaired islet function

We investigated the effect of chronic marijuana use, defined as 4 times weekly for more than 3 years, on human pancreatic islets. Pancreata from deceased donors who chronically used marijuana were compared to those from age, sex and ethnicity matched non-users. The islets from marijuana-users displayed reduced insulin secretion as compared to islets from non-users upon stimulation with high glucose (AUC, 3.41 ± 0.62 versus 5.14 ±0.47, p<0.05) and high glucose plus KCl (AUC, 4.48 ± 0.41 versus 7.69 ± 0.58, p<0.001). When human islets from chronic marijuana-users were transplanted into diabetic mice, the mean reversal rate of diabetes was 35% versus 77% in animals receiving islets from non-users (p<0.01). Immunofluorescent staining for cannabinoid receptor type 1 (CB1R) was shown to be colocalized with insulin and enhanced significantly in beta cells from marijuana-users vs. non-users (CB1R intensity/islet area, 14.95 ± 2.71 vs. 3.23 ± 0.87, p<0.001). In contrast, CB1R expression was not co-localized with glucagon or somatostatin. Furthermore, isolated islets from chronic marijuana-users appeared hypertrophic. In conclusion, excessive marijuana use affects islet endocrine phenotype and function in vitro and in vivo. Given the increasing use of marijuana, our results underline the importance of including lifestyle when evaluating human islets for transplantation or research.
Interactive effects of light, CO2 and temperature on growth and resource partitioning by the mixotrophic dinoflagellate, Karlodinium veneficum

There is little information on the impacts of climate change on resource partitioning for mixotrophic phytoplankton. Here, we investigated the hypothesis that light interacts with temperature and CO2 to affect changes in growth and cellular carbon and nitrogen content of the mixotrophic dinoflagellate, Karlodinium veneficum, with increasing cellular carbon and nitrogen content under low light conditions and increased growth under high light conditions. Using a multifactorial design, the interactive effects of light, temperature and CO2 were investigated on K. veneficum at ambient temperature and CO2 levels (25°C, 375 ppm), high temperature (30°C, 375 ppm CO2), high CO2 (30°C, 750 ppm CO2), or a combination of both high temperature and CO2 (30°C, 750 ppm CO2) at low light intensities (LL: 70 μmol photons m-2 s-2) and light-saturated conditions (HL: 140 μmol photons m-2 s-2). Results revealed significant interactions between light and temperature for all parameters. Growth rates were not significantly different among LL treatments, but increased significantly with temperature or a combination of elevated temperature and CO2 under HL compared to ambient conditions. Particulate carbon and nitrogen content increased in response to temperature or a combination of elevated temperature and CO2 under LL conditions, but significantly decreased in HL cultures exposed to elevated temperature and/or CO2 compared to ambient conditions at HL. Significant increases in C:N ratios were observed only in the combined treatment under LL, suggesting a synergistic effect of temperature and CO2 on carbon assimilation, while increases in C:N under HL were driven only by an increase in CO2. Results indicate light-driven variations in growth and nutrient acquisition strategies for K. veneficum that may benefit this species under anticipated climate change conditions (elevated light, temperature and pCO2) while also affecting trophic transfer efficiency during blooms of this species.
Evaluation of different diagnostic modalities of minimal hepatic encephalopathy in cirrhotic patients: case-control study

Introduction Minimal hepatic encephalopathy (MHE) represents one of the most overlooked complications of liver cirrhosis. Aim of the study To compare the utility and efficacy of different MHE diagnostic modalities. Material and methods This case-control study was conducted on hepatitis C virus (HCV)-related compensated cirrhotic patients. The Psychometric Hepatic Encephalopathy Score (PHES) was used to assign patients to MHE and controls. All patients were subjected to plasma ammonia, serum 3-nitrotyrosine (3-NT), critical flicker frequency (CFF), and the modified inhibitory control test (ICT). Results CFF was significantly lower in the control group (38.5, 40 Hz, p = 0.003). The unweighted lures on ICT were 8.7, 4.9 in MHE and controls (p < 0.001). Moreover, ammonia was higher in the MHE group (89, 61.5 µmol/l, p < 0.001). 3-NT was also higher in the MHE group (31.5, 13.7 nmol/l, p < 0.001) respectively. CFF at cutoff < 39 Hz had sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 57.5%, 77.5%, 71.9% and 64.6%, respectively; in modified ICT, at cutoff > 5 unweighted lures the values were 87.5%, 80%, 81.4% and 86.5%, respectively; in ammonia, at cutoff ≥ 76.45 µmol/l the values were 65%, 72.5%, 70.3% and 67.4%, respectively; for 3-NT at cutoff ≥ 14.15 nmol/l the values were 85%, 82.5%, 82.9% and 84.6%, respectively. The accuracy for MHE diagnosis was 67.5%, 83.3%, 68.8%, 83.8% relying on CFF, 3-NT, ammonia, and ICT respectively. On multivariate analysis, CFF < 39 Hz (OR = 10.2, p = 0.04), modified ICT > 5 unweighted lures (OR = 43.2, p = 0.002), and serum 3-NT levels ≥ 14.15 nmol/l (OR = 50.4, p < 0.001) were independent predictors of MHE. Conclusions 3-NT and ICT are advantageous to reveal MHE in compensated liver cirrhosis, while CFF can be only used as adjuncts, with humble merits of ammonia.
Perspectives of healthcare providers, service users, and family members about mental illness stigma in primary care settings: A multi-site qualitative study of seven countries in Africa, Asia, and Europe

Background Stigma among healthcare providers is a barrier to the effective delivery of mental health services in primary care. Few studies have been conducted in primary care settings comparing the attitudes of healthcare providers and experiences of people with mental illness who are service users in those facilities. Such research is necessary across diverse global settings to characterize stigma and inform effective stigma reduction. Methods Qualitative research was conducted on mental illness stigma in primary care settings in one low-income country (Nepal), two lower-middle income countries (India, Tunisia), one upper-middle-income country (Lebanon), and three high-income countries (Czech Republic, Hungary, Italy). Qualitative interviews were conducted with 248 participants: 64 primary care providers, 11 primary care facility managers, 111 people with mental illness, and 60 family members of people with mental illness. Data were analyzed using framework analysis. Results Primary care providers endorsed some willingness to help persons with mental illness but reported not having appropriate training and supervision to deliver mental healthcare. They expressed that people with mental illness are aggressive and unpredictable. Some reported that mental illness is incurable, and mental healthcare is burdensome and leads to burnout. They preferred mental healthcare to be delivered by specialists. Service users did not report high levels of discrimination from primary care providers; however, they had limited expectations of support from primary care providers. Service users reported internalized stigma and discrimination from family and community members. Providers and service users reported unreliable psychiatric medication supply and lack of facilities for confidential consultations. Limitations of the study include conducting qualitative interviews in clinical settings and reliance on clinician-researchers in some sites to conduct interviews, which potentially biases respondents to present attitudes and experiences about primary care services in a positive manner. Conclusions Primary care providers’ willingness to interact with people with mental illness and receive more training presents an opportunity to address stigmatizing beliefs and stereotypes. This study also raises important methodological questions about the most appropriate strategies to accurately understand attitudes and experiences of people with mental illness. Recommendations are provided for future qualitative research about stigma, such as qualitative interviewing by non-clinical personnel, involving non-clinical staff for recruitment of participants, conducting interviews in non-clinical settings, and partnering with people with mental illness to facilitate qualitative data collection and analysis.
Immediate effects of red (660 nm) and infrared (808 nm) photobiomodulation therapy on fatigue of the orbicularis oris muscle: a randomized clinical study

ABSTRACT Purpose To compare the immediate effects of laser photobiomodulation at wavelengths of 660 nm and 808 nm on fatigue of the orbicularis oris. Methods This is a randomized study with 60 women aged between 19 and 43 years. The participants were divided into four groups. Group RG received photobiomodulation with a laser wavelength of 660 nm at four points of the orbicularis oris; group IRG received photobiomodulation with a laser wavelength of 808 nm at the same points; the control group did not receive light treatment; and the placebo group underwent the same procedures as RG and IRG but with the equipment switched off. The irradiation was performed with a laser of 100 mW of power, 4 J of energy per point and 133.3 J/cm2 of fluency. An electromyography evaluation was performed before and after the irradiation, concomitantly with the exercise of lip protrusion maintained until the sensation of fatigue. Fatigue was evaluated by a median frequency using the electromyographic fatigue index. The amplitude of the signal was evaluated, examining the root mean square, and the values were normalized by the peak. The difference in amplitude between the upper and lower lips was also analyzed. All variables were compared before and after irradiation. Results No significant difference was found between the measures taken before and after irradiation. Conclusion Photobiomodulation with the parameters investigated in this study had no immediate effect on orbicular oris fatigue.