Real-time Short Video Recommendation on Mobile Devices

Short video applications have attracted billions of users in recent years, fulfilling their various needs with diverse content. Users usually watch short videos on many topics on mobile devices in a short period of time, and give explicit or implicit feedback very quickly to the short videos they watch. The recommender system needs to perceive users' preferences in real-time in order to satisfy their changing interests. Traditionally, recommender systems deployed at server side return a ranked list of videos for each request from client. Thus it cannot adjust the recommendation results according to the user's real-time feedback before the next request. Due to client-server transmitting latency, it is also unable to make immediate use of users' real-time feedback. However, as users continue to watch videos and feedback, the changing context leads the ranking of the server-side recommendation system inaccurate. In this paper, we propose to deploy a short video recommendation framework on mobile devices to solve these problems. Specifically, we design and deploy a tiny on-device ranking model to enable real-time re-ranking of server-side recommendation results. We improve its prediction accuracy by exploiting users' real-time feedback of watched videos and client-specific real-time features. With more accurate predictions, we further consider interactions among candidate videos, and propose a context-aware re-ranking method based on adaptive beam search. The framework has been deployed on Kuaishou, a billion-user scale short video application, and improved effective view, like and follow by 1.28%, 8.22% and 13.6% respectively.Comment: Accepted by CIKM 2022, 10 page

Propofol EC50 for inducing loss of consciousness in patients under combined epidural-general anesthesia or general anesthesia alone: a randomized double-blind study

BackgroundCombined epidural-general anesthesia (GA + EA) has been recommended as a preferred technique for both thoracic and abdominal surgery. The epidural anesthesia on the general anesthetic (GA) requirements has not been well investigated. Therefore, we conducted the present study to explore the predicted effect-site concentration of propofol (Ceprop) required for achieving the loss of consciousness (LOC) in 50% of patients (EC50) with or without epidural anesthesia.MethodsSixty patients scheduled for gastrectomy were randomized into the GA + EA group or GA alone group to receive general anesthesia alone. Ropivacaine 0.375% was used for epidural anesthesia to achieve a sensory level of T4 or above prior to the induction of general anesthesia. The EC50 of predicted Ceprop for LOC was determined by the up–down sequential method. The consumption of anesthetics, emergence time from anesthesia, and postoperative outcomes were also recorded and compared.ResultsThe EC50 of predicted Ceprop for LOC was lower in the GA + EA group than in the GA alone group [2.97 (95% CI: 2.63–3.31) vs. 3.36 (95% CI: 3.19–3.53) μg mL−1, (p = 0.036)]. The consumption of anesthetics was lower in the GA + EA group than in the GA alone group (propofol: 0.11 ± 0.02 vs. 0.13 ± 0.02 mg kg−1 min−1, p = 0.014; remifentanil: 0.08 ± 0.03 vs. 0.14 ± 0.04 μg kg−1 min−1, p < 0.001). The emergence time was shorter in the GA + EA group than in the GA alone group (16.0 vs. 20.5 min, p = 0.013).ConclusionConcomitant epidural anesthesia reduced by 15% the EC50 of predicted Ceprop for LOC, decreased the consumptions of propofol and remifentanil during maintenance of anesthesia, and fastened recovery from anesthesia.Clinical trial registrationClinicalTrials.gov, identifier: NCT05124704

Effect of neonatal and adult sepsis on inflammation-related diseases in multiple physiological systems: a Mendelian randomization study

BackgroundLong-term impact of sepsis on whole body systems is not well investigated. The aim of the study was to explore the potential association of neonatal/adult sepsis with several inflammation-related diseases in multiple physiological systems.MethodsInstrumental variables for neonatal and adult sepsis were collected from the public genome-wide association studies, which must satisfy the correlation, exclusivity and independence assumptions. Mendelian randomization methods (including random-effect inverse-variance weighted, MR-PRESSO, weighted median and MR-Egger) were used to determine the genetic association of neonatal/adult sepsis with asthma, allergy, rheumatoid arthritis, body mass index/obesity, type 1/type 2 diabetes and intelligence/dementia. Sensitivity analyses were conducted to assess heterogeneity and horizontal pleiotropy. The study was performed by TwoSampleMR in R software.ResultsThe inverse-variance weighted method reported that neonatal sepsis was related to the decreased level of body mass index (OR = 0.988, 95%CI = 0.980 ~ 0.997, P = 0.007), and adult sepsis was related to the decreased risk of obesity (OR = 0.785, 95%CI = 0.655 ~ 0.940, P = 0.009). These results were supported by the other Mendelian randomization methods. In addition, the study did not find any association of neonatal/adult sepsis with the other inflammation-related diseases. No heterogeneity and horizontal pleiotropy were found using sensitivity analyses.ConclusionSepsis had the potential to reduce the risk of obesity or body mass index level at a genetic level, both in neonates and in adults

Homocysteine levels in first-episode patients with psychiatric disorders

A high homocysteine (Hcy) level is a risk factor for schizophrenia, depression, and bipolar disorder. However, the role of hyperhomocysteinemia as either an independent factor or an auxiliary contributor to specific psychiatric symptoms or disorders remains unclear. This study aimed to examine Hcy levels in first-episode inpatients with psychotic symptoms and various psychiatric diseases to elucidate the association between Hcy levels and psychiatric disorders. This study enrolled 191 patients (aged 18–40 years) with psychiatric disorders. Seventy-five patients were diagnosed with schizophrenia, 48 with acute and transient psychotic disorders, 36 with manic episodes with psychosis, 32 with major depressive episodes with psychosis, and 56 healthy controls. Serum Hcy levels were measured using the enzyme cycle method. A Hcy concentration level of > 15 μmol/L was defined as hyperhomocysteinemia. Hcy levels were significantly higher in first-episode patients with psychiatric disorders compared to healthy controls (5.99 ± 3.60 vs. 19.78 ± 16.61 vs. 15.50 ± 9.08 vs. 20.00 ± 11.33 vs. 16.22 ± 12.06, F = 12.778, P < 0.001). Hcy levels were significantly higher in males with schizophrenia, acute and transient psychotic disorder, and major depressive disorder but not in mania [schizophrenia, (t = -4.727, P < 0.001); acute and transient psychotic disorders, (t = -3.389, P = 0.001); major depressive episode with psychosis, (t = -3.796, P < 0.001); manic episodes with psychosis, (t = -1.684, P = 0.101)]. However, serum Hcy levels were not significantly different among the psychiatric disorder groups (F = 0.139, P = 0.968). Multivariate linear regression showed that males had an increased risk for homocysteinemia. (95% CI = 8.192–15.370, P < 0.001). These results suggest that first-episode patients with psychiatric disorders have higher Hcy levels than in the general population, and men are at greater risk for psychiatric disorders. In conclusion, elevated Hcy levels may contribute to the pathogenesis of first-episode patients with psychotic symptoms

Sapprox: Enabling Efficient And Accurate Approximations On Sub-Datasets With Distribution-Aware Online Sampling

In this paper, we aim to enable both efficient and accurate approximations on arbitrary sub-datasets of a large dataset. Due to the prohibitive storage overhead of caching offline samples for each sub-dataset, existing offline sample based systems provide high accuracy results for only a limited number of sub-datasets, such as the popular ones. On the other hand, current online sample based approximation systems, which generate samples at runtime, do not take into account the uneven storage distribution of a sub-dataset. They work well for uniform distribution of a sub-dataset while suffer low sampling efficiency and poor estimation accuracy on unevenly distributed sub-datasets. To address the problem, we develop a distribution aware method called Sapprox. Our idea is to collect the occurrences of a sub-dataset at each logical partition of a dataset (storage distribution) in the distributed system, and make good use of such information to facilitate online sampling. There are three thrusts in Sapprox. First, we develop a probabilistic map to reduce the exponential number of recorded sub-datasets to a linear one. Second, we apply the cluster sampling with unequal probability theory to implement a distribution-aware sampling method for efficient online subdataset sampling. Third, we quantitatively derive the optimal sampling unit size in a distributed file system by associating it with approximation costs and accuracy. We have implemented Sapprox into Hadoop ecosystem as an example system and open sourced it on GitHub. Our comprehensive experimental results show that Sapprox can achieve a speedup by up to 20x over the precise execution

Association between statin use on delirium and 30-day mortality in patients with chronic obstructive pulmonary disease in the intensive care unit

Abstract Background Delirium occurs frequently in patients with chronic obstructive pulmonary disease in the intensive care unit. Effective prevention and treatment strategies for delirium remain limited. We aimed to assess delirium and 30-day mortality in patients with chronic obstructive pulmonary disease who were statin and non-statin users. Methods In this retrospective study, patients with chronic obstructive pulmonary disease were identified from the Medical Information Mart for Intensive Care database (MIMIC-IV). The primary exposure variable was the use of statins 3 days after entering the intensive care unit and the primary outcome measure was the presence of delirium. The secondary outcome measure was 30-day mortality. Since the cohort study was retrospective, we used an inverse probability weighting derived from the propensity score matching to balance different variables. Results Among a cohort of 2725 patients, 1484 (54.5%) were statin users. Before propensity score matching, the prevalence of delirium was 16% and the 30-day mortality was 18% in patients with chronic obstructive pulmonary disease. Statin use was significantly negatively correlated with delirium, with an odds ratio of 0.69 (95% CI 0.56–0.85, p < 0.001) in the inverse probability weighted cohort and 30-day mortality of 0.7 (95% CI 0.57–0.85, p < 0.001). Conclusions Statin use is associated with a lower incidence of delirium and 30-day mortality in patients with chronic obstructive pulmonary disease in the intensive care unit

On Balance Among Energy, Performance And Recovery In Storage Systems

With the increasing size of the clusters as well as the increasing capacity of each storage node, current storage systems are spending more time on recovery. When node failure happens, the system enters degradation mode in which node reconstruction/block recovery is initiated. This very process needs to wake up a number of disks and takes a substantial amount of I/O bandwidth which will not only compromise energy efficiency but also performance. This raises a natural problem: how to balance the performance, energy, and recovery in degradation mode for an energy efficient storage system? Without considering the I/O bandwidth contention between recovery and performance, we find that the current energy proportional solutions cannot answer these question accurately. This paper presents a mathematical model called Perfect Energy, Reliability, and Performance (PERP) which provides guidelines of provisioning active nodes number and recovery speed at each time slot with respect to the performance and recovery constraints. We apply our model to practical data layouts and test the effectiveness on our 25-node CASS cluster. Experimental results validate that our model helps realize 25% energy savings while meeting both performance and recovery constraints and the saving is expected to increase with a larger number of nodes