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Background: Auditory electrophysiological tests of the cortex, which are processed in or close to the auditory cortex, are brain reactions to sound. A variation in a continuous stimulus causes the acoustic change complex potential (ACC), which is a wave following the P1-N1-P2 response. Objective: To measure the amplitude and latency of different components of ACC in normal subjects and across individuals with mild and moderate degrees of sensorineural hearing loss (SNHL). Patients and methods: The study includes 100 individuals with the age ranged from 10 to 50 years with different degrees of SNHL. The ACC was evoked by a change of second formant in the middle of ongoing steady-state synthetic, 3 formant vowels (ooee). The total duration was 500 ms. Changing occurred at 250 ms. Results: The SNHL subgroups showed statistically significantly longer P1 and N1 latencies. N1 and P2 amplitudes of ACC onset response were larger with a statistical significance as compared to controls. Post hoc analysis revealed no statistically significant difference between mild and moderate SNHL on ACC parameters. Age showed a significant negative correlation with ACC N1 and P2 latency, ACC P1 and N1 amplitude, and onset P2 latency. Onset response P1 latency was significantly higher in children than adults. Median ACC P1 amplitude significantly increased in children than adults. Conclusion: ACC is a reliable tool for testing the auditory cortex function of detecting difference in sounds presented that can be recorded readily in patients with mild and moderate SNHL. [ABSTRACT FROM AUTHOR]

Ecological restoration and environmental risk control on bauxite residue disposal areas have received persistent attention worldwide as it concerns the sustainable development of global alumina enterprises. Currently, microbially driven ecological restoration has been regarded as a promising way in consideration of its cost effectiveness, lower risks, and durability. However, the extremely degraded environment (e.g., high alkalinity, poor physical structure, and low organic carbon content) poses significant challenges for the activity and effectiveness of functional microorganisms involved in bioremediation. Moreover, the specific mechanisms of functional microorganisms, including alkaline regulation, stable aggregate formation, organic carbon accumulation, and plant growth promotion, in the soil-formation and revegetation process are not well understood, hindering the widespread application of microbially driven ecological restoration strategies in large-scale bauxite residue disposal. To date, these strategies have not been demonstrated in a large scale. To address these gaps, this review focuses on the microbial properties of bauxite residue disposal areas and the limiting factors of microbial functions. Furthermore, considering the differences between bauxite residue and natural soils, the roles of microorganisms in alkaline stabilization, aggregate formation, and organic carbon accumulation in bauxite residue during the soil-formation process are explored. Meanwhile, the microbially driven ecological restoration on bauxite residue disposal areas, including plant growth capacity and restoration process regulation, are discussed. Lastly, future research directions for understanding microbially driven soil-formation processes and ecological restoration on bauxite residue disposal areas are envisaged. This review provides new insights into the driving mechanisms of microorganisms on the soil formation of bauxite residue, offering a theoretical foundation and practical guidance for ecological restoration on bauxite residue disposal areas. [ABSTRACT FROM AUTHOR]

Obesity is a complex, chronic disease requiring a multidisciplinary approach to its management. In clinical practice, body mass index and waist-related measurements can be used for obesity screening. The estimated prevalence of obesity among adults worldwide is 12%. With the expected further increase in overall obesity prevalence, clinicians will increasingly be managing patients with obesity. Energy balance is regulated by a complex neurohumoral system that involves the central nervous system and circulating mediators, among which leptin is the most studied. The functioning of these systems is influenced by both genetic and environmental factors. Obesity generally occurs when a genetically predisposed individual lives in an obesogenic environment for a long period. Cardiologists are deeply involved in evaluating patients with obesity. Cardiovascular risk profile is one of the most important items to be quantified to understand the health risk due to obesity and the clinical benefit that a single patient can obtain with weight loss. At the individual level, appropriate patient involvement, the detection of potential obesity causes, and a multidisciplinary approach are tools that can improve clinical outcomes. In the near future, we will probably have new pharmacological tools at our disposal that will facilitate achieving and maintaining weight loss. However, pharmacological treatment alone cannot cure such a complex disease. The aim of this paper is to summarize some key points of this field, such as obesity definition and measurement tools, its epidemiology, the main mechanisms underlying energy homeostasis, health consequences of obesity with a focus on cardiovascular diseases and the obesity paradox. Level of evidence V: report of expert committees. [ABSTRACT FROM AUTHOR]

Purpose: Orthorexia nervosa (ON), a proposed disorder describing an obsessive focus on “healthy” eating, is characterised as having overlapping symptoms with obsessive–compulsive disorder. However, ON/obsessive–compulsive (OC) symptom relationships are inconsistently reported. The current study aimed to investigate if the contribution of OC symptoms and beliefs explain variability in ON symptoms and determine if perfectionism, a transdiagnostic factor, moderates the ON/OC symptom relationship. Methods: The study comprised 190 participants (Mage = 28.63, SDage = 9.88; 80% female) recruited via an undergraduate research programme, social media, advocacy organisations, and a participant registry. Participants completed an online questionnaire assessing ON, OC, and perfectionism symptoms. Results: A linear regression analysis found OC symptoms and beliefs explained 22.9% variability in ON symptoms (p < 0.001, f2 = 0.38) and perfectionism moderated the ON/OC symptom relationship, where higher levels of perfectionism with higher levels of OC symptoms was associated with higher levels of ON symptoms, explaining 2.2% variability (p =.01, f2 = 0.03). Conclusion: OC symptoms appear more common in ON than previous studies indicate. However, the interaction between perfectionism and OC symptoms may drive obsessions in ON. Findings help refine our current understanding of ON phenomenology with implications for ON treatment development. Future research should further explore perfectionism in ON phenomenology. Level of evidence: Level V (Opinions of respected authorities, based on descriptive studies, narrative reviews, clinical experience, or reports of expert committees). [ABSTRACT FROM AUTHOR]

Purpose: This paper advocates for the inclusion of patient perspectives in the diagnosis and treatment of eating disorders (EDs) for ethical, epistemological, and pragmatic reasons. We build upon the ideas of a recent editorial published in this journal. Using EDs as their example, the authors argue against dominant DSM-oriented approaches in favor of an increased focus on understanding patients’ subjective experiences. We argue that their analysis stops too soon for the development of practical—and actionable—insights into how to effect the integration of first-person and third-person accounts of EDs. Methods: Contextual analysis was used to make the case for patient perspectives. Results: We use anorexia nervosa (AN) as an example to demonstrate how the integration of patient manifestations and voices offers a promising methodology to improve patient diagnosis and treatment. We suggest that Acceptance and Commitment Therapy (ACT) can support patients with AN by reconciling their values with the values that arise from a clinician’s duty of care. Conclusions: We conclude that there are no good scientific reasons to exclude first-person perspectives of EDs in psychiatry. Level of evidence: Level V: Opinions based on clinical experience. [ABSTRACT FROM AUTHOR]

Background: Research suggests that food choices, preferences, and tastes change after bariatric surgery, but evidence regarding changes in food cravings is mixed. Objectives: The primary aim of this cohort study was to compare food cravings during the first year following bariatric surgery in patients who had undergone sleeve gastrectomy (SG) versus Roux-en-Y gastric bypass (RYGB). Setting: Integrated multispecialty health system, United States. Methods: Patients aged ≥ 18 years seen between May 2017 and July 2019, provided informed consent, completed the Food Craving Inventory (FCI), and had ≥ 1 year of follow-up after undergoing primary SG or RYGB were included in the study. Secondary data captured included psychological and behavioral measures. Preoperative and postoperative (3, 6, 9, and 12 months) FCI scores of patients who underwent SG and RYGB were compared. Results: Some attrition occurred postoperatively (N = 187 at baseline, 141 at 3 months, 108 at 6 months, 89 at 9 months, and 84 at 12 months). No significant relationship between pre- or postoperative food cravings and surgery type was found except on the carbohydrate subscale. Patients with higher preoperative food addiction symptoms were not more likely to experience an earlier reoccurrence of food cravings during the first 12 months after surgery. Likewise, patients with higher levels of preoperative depression and anxiety were not more likely to have early reoccurrence of food cravings during the first 12 months after surgery; however, those with higher PHQ9 scores at baseline had uniformly higher food craving scores at all timepoints (pre-surgery, 3 m, 6 m, 9 m, and 12 m). Conclusions: Results suggest that food cravings in the year after bariatric surgery are equivalent by surgery type and do not appear to be related to preoperative psychological factors or eating behaviors. Level of evidence: Level III: Evidence obtained from well-designed cohort.Highlights: Food cravings are significantly reduced after bariatric surgery, although they generally do not differ by surgery type. (With the exception of carb cravings, specifically, which were higher for those undergoing RYGB than SG). Patients with higher levels of preoperative depression and anxiety were not more likely to have early reoccurrence of food cravings during the first 12 months after surgery; however, those with higher PHQ9 scores at baseline had uniformly higher food craving scores at all timepoints (pre-surgery, 3 m, 6 m, 9 m, and 12 m). Patients with higher preoperative food addiction symptoms were not more likely to experience an earlier reoccurrence of food cravings during the first 12 months after surgery. [ABSTRACT FROM AUTHOR]

Purpose: Eating disorder recovery is a poorly defined concept, with large variations among researchers’ definitions. Weight maintenance is a key aspect of recovery that remains relatively underexplored in the literature. Understanding the role of weight maintenance may help guide the development of treatments. This paper aims to address this by (1) investigating the factors predicting long-term weight maintenance in anorexia nervosa (AN) patients; (2) exploring differences in predictive factors between adolescent and adult populations; and (3) exploring how weight maintenance is conceptualised in the literature. Methods: We conducted a systematic review following PRISMA guidelines to address our research questions. Five databases were searched and filtered according to our exclusion criteria. Results: From the search, 1059 studies were yielded, and 13 studies were included for review. A range of weight, biological and psychological factors were found to predict weight maintenance among these papers. BMI at admission and discharge from inpatient treatment was the most common predictor among the papers. Few studies investigated biological factors and mixed evidence was found for psychological factors. We found no observable differences between adult and adolescent populations. Finally, weight maintenance was defined and measured differently across studies. Conclusion: This review’s findings can help contribute to a well-rounded understanding of weight maintenance, and ultimately, of recovery. This can help support clinicians in tailoring interventions to improve long-term outcomes in AN. Future research should aim to replicate studies to better understand the relationship between the factors identified and weight maintenance. Level I: Systematic review. [ABSTRACT FROM AUTHOR]

Purpose: The relationship between autistic traits and eating disturbances has been given considerable attention over the last decades. The rise of a dimensional approach to psychopathology has expanded the way we think about autism, acknowledging that subthreshold autistic manifestations span across the general population and are more pronounced in psychiatric patients. Here we investigated the prevalence of eating disorders and its potential relationship with autistic traits and sensory sensitivity in a group of patients who were referred for the first time to a mental health outpatient clinic, without a formal diagnosis yet. Methods: 259 young adults (between 18 and 24 years old) completed: the Eating Attitude Test (EAT-26), the Swedish Eating Assessment for Autism Spectrum Disorders (SWEAA), the Autism Quotient (AQ), the Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R), and the Sensory Perception Quotient—Short Form 35 item (SPQ-SF35). Results: 23.55% of participants scored above the cut-off at the EAT-26, suggesting that they presented a risk for eating disorders and should be assessed by a specialized clinician; associations emerged between hypersensitivity in the touch and vision domain and both the EAT-26 and the SWEAA; the presence of autistic traits was largely associated with eating disturbances. Conclusions: This study underlines the significance of the eating domain as a central psychopathological feature in the distress experienced by young adults with general psychiatric symptoms and psychological suffering; it adds evidence to the association between autistic traits and eating disorders and opens to new research questions about the role of subthreshold autistic traits in general psychopathology. Level of evidence: Level I: Evidence obtained from experimental studies. [ABSTRACT FROM AUTHOR]

Background: Phenomenological research has enriched the scientific and clinical understanding of Eating Disorders (ED), describing the significant role played by disorders of embodiment in shaping the lived experience of patients with ED. According to the phenomenological perspective, disorders of embodiment in ED are associated with feelings of alienation from one’s own body, determining an excessive concern for external appearance as a form of dysfunctional coping. The purpose of the present narrative review is to address the role of gender identity as a risk factor for EDs in the light of phenomenological approaches. Methods: Narrative review. Results: The current study discusses the interplay between perception, gender identity, and embodiment, all posited to influence eating psychopathology. Internalized concerns for body appearance are described as potentially associated with self-objectification. Furthermore, concerns on body appearance are discussed in relation to gendered social expectations. The current review also explores how societal norms and gender stereotypes can contribute to dysfunctional self-identification with external appearances, particularly through an excessive focus on the optical dimension. The socio-cultural perspective on gender identity was considered as a further explanation of the lived experience of individuals with ED. Conclusions: By acknowledging the interplay between these factors, clinicians and researchers can gain a deeper understanding of these disorders and develop more effective interventions for affected individuals. Level of evidence: Level V narrative review. [ABSTRACT FROM AUTHOR]

Recently advancements in deep learning models have significantly facilitated the development of sequential recommender systems (SRS). However, the current deep model structures are limited in their ability to learn high-quality embeddings with insufficient data. Meanwhile, highly skewed long-tail distribution is very common in recommender systems. Therefore, in this paper, we focus on enhancing the representation of tail items to improve sequential recommendation performance. Through empirical studies on benchmarks, we surprisingly observe that both the ranking performance and training procedure are greatly hindered by the poorly optimized tail item embeddings. To address this issue, we propose a sequential recommendation framework named TailRec that enables contextual information of tail item well-leveraged and greatly improves its corresponding representation. Given the characteristics of the sequential recommendation task, the surrounding interaction records of each tail item are regarded as contextual information without leveraging any additional side information. This approach allows for the mining of contextual information from cross-sequence behaviors to boost the performance of sequential recommendations. Such a light contextual filtering component is plug-and-play for a series of SRS models. To verify the effectiveness of the proposed TailRec, we conduct extensive experiments over several popular benchmark recommenders. The experimental results demonstrate that TailRec can greatly improve the recommendation results and speed up the training process. The codes of our methods have been available. [ABSTRACT FROM AUTHOR]

For the high-performance computing in a WAN environment, the geographical locations of national supercomputing centers are scattered and the network topology is complex, so it is difficult to form a unified view of resources. To aggregate the widely dispersed storage resources of national supercomputing centers in China, we have previously proposed a global virtual data space named GVDS in the project of “High Performance Computing Virtual Data Space”, a part of the National Key Research and Development Program of China. The GVDS enables large-scale applications of the high-performance computing to run efficiently across WAN. However, the applications running on the GVDS are often data-intensive, requiring large amounts of data from multiple supercomputing centers across WANs. In this regard, the GVDS suffers from performance bottlenecks in data migration and access across WANs. To solve the above-mentioned problem, this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation strategy. In a WAN environment, the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources, guaranteeing lower average migration latency when multiple data migration tasks are running in parallel. In addition, it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests (the IO proxy is a module of GVDS), so as to improve the application’s performance across WANs. The experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly. [ABSTRACT FROM AUTHOR]

Though obstruction-free progress property is weaker than other non-blocking properties including lock-freedom and wait-freedom, it has advantages that have led to the use of obstruction-free implementations for software transactional memory (STM) and in anonymous and fault-tolerant distributed computing. However, existing work can only verify obstruction-freedom of specific data structures (e.g., STM and list-based algorithms). In this paper, to fill this gap, we propose a program logic that can formally verify obstruction-freedom of practical implementations, as well as verify linearizability, a safety property, at the same time. We also propose informal principles to extend a logic for verifying linearizability to verifying obstruction-freedom. With this approach, the existing proof for linearizability can be reused directly to construct the proof for both linearizability and obstruction-freedom. Finally, we have successfully applied our logic to verifying a practical obstruction-free double-ended queue implementation in the first classic paper that has proposed the definition of obstruction-freedom. [ABSTRACT FROM AUTHOR]

Spatial crowdsourcing (SC) is a popular data collection paradigm for numerous applications. With the increment of tasks and workers in SC, heterogeneity becomes an unavoidable difficulty in task allocation. Existing researches only focus on the single-heterogeneous task allocation. However, a variety of heterogeneous objects coexist in real-world SC systems. This dramatically expands the space for searching the optimal task allocation solution, affecting the quality and efficiency of data collection. In this paper, an aggregation-based dual heterogeneous task allocation algorithm is put forth. It investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel distance. This problem is first proved to be NP-hard. Then, a task aggregation method based on locations and requirements is built to reduce task failures. Meanwhile, a time-constrained shortest path planning is also developed to shorten the travel distance in a community. After that, two evolutionary task allocation schemes are presented. Finally, extensive experiments are conducted based on real-world datasets in various contexts. Compared with baseline algorithms, our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance. [ABSTRACT FROM AUTHOR]

With the recent demonstration of quantum computers, interests in the field of reversible logic synthesis and optimization have taken a different turn. As every quantum operation is inherently reversible, there is an immense motivation for exploring reversible circuit design and optimization. When it comes to faults in circuits, the parity-preserving feature donates to the detection of permanent and temporary faults. In the context of reversible circuits, the parity-preserving property ensures that the input and output parities are equal. In this paper we suggest six parity-preserving reversible blocks (Z, F, A, T, S, and L) with improved quantum cost. The reversible blocks are synthesized using an existing synthesis method that generates a netlist of multiple-control Toffoli (MCT) gates. Various optimization rules are applied at the reversible circuit level, followed by transformation into a netlist of elementary quantum gates from the NCV library. The designs of full-adder and unsigned and signed multipliers are proposed using the functional blocks that possess parity-preserving properties. The proposed designs are compared with state-of-the-art methods and found to be better in terms of cost of realization. Average savings of 25.04%, 20.89%, 21.17%, and 51.03%, and 18.59%, 13.82%, 13.82%, and 27.65% respectively, are observed for 4-bit unsigned and 5-bit signed multipliers in terms of quantum cost, garbage output, constant input, and gate count as compared to recent works. [ABSTRACT FROM AUTHOR]

Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field. [ABSTRACT FROM AUTHOR]

Communication can promote coordination in cooperative Multi-Agent Reinforcement Learning (MARL). Nowadays, existing works mainly focus on improving the communication efficiency of agents, neglecting that real-world communication is much more challenging as there may exist noise or potential attackers. Thus the robustness of the communication-based policies becomes an emergent and severe issue that needs more exploration. In this paper, we posit that the ego system1) trained with auxiliary adversaries may handle this limitation and propose an adaptable method of Multi-Agent Auxiliary Adversaries Generation for robust Communication, dubbed MA3C, to obtain a robust communication-based policy. In specific, we introduce a novel message-attacking approach that models the learning of the auxiliary attacker as a cooperative problem under a shared goal to minimize the coordination ability of the ego system, with which every information channel may suffer from distinct message attacks. Furthermore, as naive adversarial training may impede the generalization ability of the ego system, we design an attacker population generation approach based on evolutionary learning. Finally, the ego system is paired with an attacker population and then alternatively trained against the continuously evolving attackers to improve its robustness, meaning that both the ego system and the attackers are adaptable. Extensive experiments on multiple benchmarks indicate that our proposed MA3C provides comparable or better robustness and generalization ability than other baselines. [ABSTRACT FROM AUTHOR]

IP geolocation is essential for the territorial analysis of sensitive network entities, location-based services (LBS) and network fraud detection. It has important theoretical significance and application value. Measurement-based IP geolocation is a hot research topic. However, the existing IP geolocation algorithms cannot effectively utilize the distance characteristics of the delay, and the nodes’ connection relation, resulting in high geolocation error. It is challenging to obtain the mapping between delay, nodes’ connection relation, and geographical location. Based on the idea of network representation learning, we propose a representation learning model for IP nodes (IP2vec for short) and apply it to street-level IP geolocation. IP2vec model vectorizes nodes according to the connection relation and delay between nodes so that the IP vectors can reflect the distance and topological proximity between IP nodes. The steps of the street-level IP geolocation algorithm based on IP2vec model are as follows: Firstly, we measure landmarks and target IP to obtain delay and path information to construct the network topology. Secondly, we use the IP2vec model to obtain the IP vectors from the network topology. Thirdly, we train a neural network to fit the mapping relation between vectors and locations of landmarks. Finally, the vector of target IP is fed into the neural network to obtain the geographical location of target IP. The algorithm can accurately infer geographical locations of target IPs based on delay and topological proximity embedded in the IP vectors. The cross-validation experimental results on 10023 target IPs in New York, Beijing, Hong Kong, and Zhengzhou demonstrate that the proposed algorithm can achieve street-level geolocation. Compared with the existing algorithms such as Hop-Hot, IP-geolocater and SLG, the mean geolocation error of the proposed algorithm is reduced by 33%, 39%, and 51%, respectively. [ABSTRACT FROM AUTHOR]

Learning-outcome prediction (LOP) is a longstanding and critical problem in educational routes. Many studies have contributed to developing effective models while often suffering from data shortage and low generalization to various institutions due to the privacy-protection issue. To this end, this study proposes a distributed grade prediction model, dubbed FecMap, by exploiting the federated learning (FL) framework that preserves the private data of local clients and communicates with others through a global generalized model. FecMap considers local subspace learning (LSL), which explicitly learns the local features against the global features, and multi-layer privacy protection (MPP), which hierarchically protects the private features, including model-shareable features and not-allowably shared features, to achieve client-specific classifiers of high performance on LOP per institution. FecMap is then achieved in an iteration manner with all datasets distributed on clients by training a local neural network composed of a global part, a local part, and a classification head in clients and averaging the global parts from clients on the server. To evaluate the FecMap model, we collected three higher-educational datasets of student academic records from engineering majors. Experiment results manifest that FecMap benefits from the proposed LSL and MPP and achieves steady performance on the task of LOP, compared with the state-of-the-art models. This study makes a fresh attempt at the use of federated learning in the learning-analytical task, potentially paving the way to facilitating personalized education with privacy protection. [ABSTRACT FROM AUTHOR]

Significant progress has been made in machine learning with large amounts of clean labels and static data. However, in many real-world applications, the data often changes with time and it is difficult to obtain massive clean annotations, that is, noisy labels and time series are faced simultaneously. For example, in product-buyer evaluation, each sample records the daily time behavior of users, but the long transaction period brings difficulties to analysis, and salespeople often erroneously annotate the user’s purchase behavior. Such a novel setting, to our best knowledge, has not been thoroughly studied yet, and there is still a lack of effective machine learning methods. In this paper, we present a systematic approach RTS both theoretically and empirically, consisting of two components, Noise-Tolerant Time Series Representation and Purified Oversampling Learning. Specifically, we propose reducing label noise’s destructive impact to obtain robust feature representations and potential clean samples. Then, a novel learning method based on the purified data and time series oversampling is adopted to train an unbiased model. Theoretical analysis proves that our proposal can improve the quality of the noisy data set. Empirical experiments on diverse tasks, such as the house-buyer evaluation task from real-world applications and various benchmark tasks, clearly demonstrate that our new algorithm robustly outperforms many competitive methods. [ABSTRACT FROM AUTHOR]

Interpretability has drawn increasing attention in machine learning. Most works focus on post-hoc explanations rather than building a self-explaining model. So, we propose a Neural Partially Linear Additive Model (NPLAM), which automatically distinguishes insignificant, linear, and nonlinear features in neural networks. On the one hand, neural network construction fits data better than spline function under the same parameter amount; on the other hand, learnable gate design and sparsity regular-term maintain the ability of feature selection and structure discovery. We theoretically establish the generalization error bounds of the proposed method with Rademacher complexity. Experiments based on both simulations and real-world datasets verify its good performance and interpretability. [ABSTRACT FROM AUTHOR]

Two new species of the lichen genus Lecaimmeria are described and illustrated from Pakistan, namely, Lecaimmeria asiatica and L. crispula. The former taxon is characterized by pale brown to dark brown thallus, up to 2-mm large areoles, flat to strongly convex apothecial disc, taller hymenium 120–150 µm, apically branched, not anastomosing paraphyses, large conidia 5–7 × 1.1–1.4 µm, with respect to ecology, and the absence of secondary metabolites, whereas L. crispula can be characterized by having a light brown to chocolate brown thallus, apothecia having flat to slightly concave discs with wavy margins that curl inward, a tall hymenium150–180 μm, branched and anastomosing paraphyses, large ascospores 25–33 × 12–17 μm, and absence of secondary metabolites. A phylogenetic analysis is provided based on nrITS and nrLSU sequences that support the separation of both novel species. Comparative analyses with related species of Lecaimmeria are provided to confirm the status of these species. [ABSTRACT FROM AUTHOR]

During our studies on the genus Lepiota in Pakistan, we collected two putatively new species from Punjab Province, with distinct morphology, ITS, and 28S of nrDNA profile. L. aurantiopilea is featured by orange to yellow-orange pileus with a reddish-brown umbo, tiny granules on the surface that are concolorous to pileus, absence of annulus, subglobose to oblong, ellipsoid or spurred basidiospores, clavate fusoid-ventricose, utriform cheilocystidia and hymeniderm made up clavate to subfusiform elements. Another new species, L. bahawalnagarensis has a light grayish-brown pileus with dark grayish-brown reddish-brown umbo, grayish-yellow brown zonation on the surface, pale yellow stipe, single-edged annulus, ellipsoid basidiospores, versiform cheilocystidia, hymeniderm pileipellis, and clavate to utriform caulocystidia. Photographs of fresh basidiomata, descriptions, and line drawings of key microscopic features are provided. Morphological characters and phylogenetic trees inferred from nrITS and 28S of nrDNA sequences show that both of our new species clustered within section Liliaceae. [ABSTRACT FROM AUTHOR]

Fungal pathogens pose threats to crop productivity and food security. Management of fungal diseases can be achieved through an integrated disease management approach, including accurate identification of the causative agents. Diaporthe blight is a common disease of eggplant (Solanum melongena). Although Diaporthevexans has been implicated as the causative agent, it was unclear until this study whether other Diaporthe species also contribute to this disease. In this study, leaf and fruit blights of eggplants were collected from different provinces in the Philippines. Through a polyphasic approach—morphological, cultural, pathogenicity, and multi-locus phylogenetic analyses of ITS, TEF1-α, TUB2, CAL, and HIS3 coupled with genealogical concordance phylogenetic species recognition analyses—we identified six Diaporthe speciesfrom the D. sojae species complex and D. arecae species complex. Two novel species, D. solani-melongenae and D. talong, along with the re-validated D.melongenae, and known species, viz., D. arecae, D.passifloricola, and D. vexans were identified as the causative agents of Diaporthe leaf and fruit blight of eggplant in the Philippines. Additionally, we also provide evidence supporting the synonymization of D. etinsidea with D. tulliensis, D. durionigena with D. rosae, and D. griceae with D. vexans. Our study confirmed that D. vexans is the major causative agent of eggplant blight in the country. All Diaporthe species were found to be pathogenic to eggplant. The results of this study contribute to the understanding of the eggplant blight disease, its potential spread, and the development of more targeted management strategies. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate whether the application of four bioremediation strategies: mycoaugmentation with Actinomucor elegans LBM 239; biostimulation with carbon, nitrogen, and phosphorus sources; combined mycoaugmentation and biostimulation; and natural attenuation could improve the quality of carbendazim-contaminated soil. The experiments were done in microcosm soil and remediation effectiveness was assessed based on pollutant content, soil characteristics, and ecotoxicological tests. A greenhouse experiment was conducted to evaluate the effect of A. elegans LBM 239 on plant growth promotion. Results revealed that mycoaugmentation, biostimulation, and combined strategy (mycoaugmentation + biostimulation) significantly decreased the concentration of carbendazim, with removal percentages higher than 90%, with a significant toxicity reduction at 15 days. Mycoaugmentation and biostimulation combined strategy improved the content of carbon, nitrogen, and phosphorus in the soil. A. elegans did not demonstrate plant growth-promoting properties and no signs and symptoms of the disease were observed on inoculated tomato seedlings. Finally, the principal component analysis revealed that the combined treatment of mycoaugmentation and biostimulation constitute the most efficient bioremediation alternative to restore carbendazim-contaminated soils. [ABSTRACT FROM AUTHOR]

Sequencing ribosomal DNA loci, such as ITS and LSU, are among the first steps to characterize and identify newly isolated fungal strains. Substantial doubt has recently been raised concerning the unequivocal suitability of using this sequence information for inferring inter- and intraspecific relationships, as especially the ITS locus was repeatedly shown to harbor high intragenomic plasticity. The evolutionary mechanisms leading to this phenomenon are poorly understood, but this is about to change, owing to the availability of third generation sequencing techniques. The current study attempts a comprehensive analysis of rDNA cistrons in the genomes of over 40 selected species of Hypoxylaceae (Xylariales), using a combination of 2nd generation (Illumina) and 3rd generation sequencing technologies (Oxford Nanopore), along with extensive usage of bioinformatic tools to obtain high-quality genomes. Our findings revealed high intragenomic polymorphisms in distinct Hypoxylaceae species, indicating the presence of deep rDNA paralogs. Phylogenetic inference of the ITS rDNA showed low support for the primary clades within Hypoxylaceae, while inference using the protein encoding gene RPB2 showed better support. We also found that proteinogenic genes are not prone to intraspecific and intragenomic polymorphisms as much as the rDNA loci. Our study highlights the limitations of solely relying on the rDNA cistron as a barcode for fungi. We propose TUB2 as a new primary barcoding marker for Hypoxylaceae and other Xylariales. Moreover, we demonstrate the feasibility to retrieve some DNA loci that had hitherto not been used for classical phylogenetic studies (such as TEF1) in Xylariales directly from genomes without the necessity to work with primers. [ABSTRACT FROM AUTHOR]

Diversispora cerifera and Diversispora succinacia are new arbuscular mycorrhizal fungal species that have been isolated and propagated from spores extracted from rhizosphere soils of native vegetation that had naturally established from seeds on a nickel mine tailing test basin in New Caledonia. Interestingly, these species were not recorded from ultramafic soils of maquis vegetation endemic to New Caledonia surrounding the tailing basin. In greenhouse trap and single-species cultures, the fungi produced numerous spores, which were formed terminally or intercalary on subtending hyphae. Spores of D. cerifera are white-yellow with a waxy appearance and 70–100–120 µm in diameter; spores of D. succinacia are translucent, amber in color, and 60–80–110 µm in diameter; both species have three spore wall layers. A phylogenetic analysis placed D. cerifera in a clade sister to D. succinacia. The same analysis showed that the sister species of D. succinacia is D. sabulosa. [ABSTRACT FROM AUTHOR]

During the assessment of coprophilous mucoralean diversity in northeastern areas of Brazil, a Lichtheimia-like specimen (URM 8358) was isolated from rabbit dung. It was characterized based on morphological, physiological, and molecular analyses of the internal transcribed spacer (ITS), large subunit ribosomal (LSU) and actin DNA sequences. These analyses revealed a novel Lichtheimia species, which is described herein. The novel species forms abundantly branched sporangiophores, with most branches arising near the main sporangia, various-shaped columellae, and irregularly shaped sporangiospores. Furthermore, this manuscript provides notes of all Lichtheimia species, and an identification key for the genus is provided. These findings contribute to the existing knowledge on the taxonomy and distribution of Lichtheimia. Because of the clinical relevance of other Lichtheimia species, we encourage future research to determine whether URM 8358 can be pathogenic to humans. [ABSTRACT FROM AUTHOR]

Soybean diseases induced by Cercospora spp. exhibit a global prevalence worldwide. Cercospora kikuchii causes both Cercospora leaf blight (CLB) and purple seed stain (PSS), whereas Cercospora sojina is a causal agent of frogeye leaf spot (FLS). Eighteen Cercospora isolates originating from soybean plants exhibiting CLB, PSS, and FLS symptoms were obtained from continental Russia, the Crimea Peninsula, and South America. The identification was based on the Consolidated Species Concept and involved multi-locus phylogenetic analysis, assessment of cercosporin production capacity, and pathogenicity testing. Ten isolates were identified as C. sojina; the eight remaining ones were categorized into seven distinct species. Two isolates of C. kikuchii were obtained from South America, along with a single isolate each of Cercospora cf. sigesbeckiae and Cercospora sp. Q from the Russian Far East and South America, respectively. Three isolates from the Russian Far East were identified as Cercospora cf. alchemillicola and Cercospora celosiae. A single isolate formed a distinct monophyletic clade that did not include ex-type or representative Cercospora strains and is, therefore, considered a candidate for a new Cercospora species. Cercosporin production in vitro is not a stable and reliable feature for species identification; it could vary and depends on factors such as the nutrient medium composition and the specific lighting conditions during the culturing process. In Russia, multiple Cercospora species are associated with PSS: at least C. cf. alchemillicola, C. cf. sigesbeckiae, and C. celosiae, which are new records for Russia. Cercospora kikuchii and Cercospora sp. Q emerge as causal agents of PSS in South America. PSS and CLB symptoms evident on soybeans are intricate features; thus, they can no longer be definitively regarded as unequivocal signs for the presence of C. kikuchii. [ABSTRACT FROM AUTHOR]

Cortinarius (Fr.) Fr. is one of the most species-rich genera in the Agaricales (Basidiomycota). Cortinarius subgen. Dermocybe (Fr.) Trog includes brightly coloured Cortinarii with anthraquinone pigments. The chemotaxonomic approach has always been as important as classical methods for species definition of Dermocybe and helped to improve overall species concepts. However, some species concepts within this group remain unclear. We therefore address this topic based on a combined phylogenetic, morphological, and pigment-chemical approach. For this, sequence data, HPLC–MS pigment profiles and spore sizes were included were included to obtain a better resolution of taxa. The study was based on 173 recent collections and 12 type specimens. A total of 117 rDNA ITS sequences were produced from the collections in this study, 102 sequences were retrieved from databases. We could detect and clearly delimit 19 Dermocybe species occurring in central European habitats, from which 16 are discussed in detail. Additionally, we grouped the detected anthraquinone pigments into four groups. This detailed analysis of dermocyboid Cortinarius species occurring in a restricted number of habitat types confirmed our hypothesis that species diversity is much higher than currently assumed. This high diversity is blurred by too wide and incorrect species concepts of several classical species like C. croceus and C. cinnamomeus. Molecular and chemotaxonomical studies carried out together with careful phenotypical analyses resulted in a good differentiation of species. A key is presented for these taxa to allow a better identification of Cortinarius subgenus Dermocybe spp. occurring in Central Europe mainly in the alpine range. [ABSTRACT FROM AUTHOR]

Cladochytriales comprises a taxonomic order and lineage of chytrid fungi that are primarily saprobes of organic matter in aquatic and terrestrial ecosystems. The order contains species from 12 genera, though molecular confirmations of almost all type species are lacking, and many genera are considered polyphyletic. We conducted phylogenetic analyses of ribosomal RNA genes and developmental morphology of Cladochytriales isolates from the Collection of Zoosporic Eufungi at the University of Michigan and noted some strains that were distinct from described genera. In our phylogenetic analysis, Endochytriaceae represented by only one strain of Endochytrium ramosum was sister to Septochytriaceae while the incertae sedis lineage formed by the new species Thomasia carolinae was sister to Nowakowskiellaceae. Additionally, the phylogenetic relatedness of the other two new species, Dogmamyces elongatus and Allochytrium aureum, was also highly supported. A thorough revision of the order is needed because some genera, such as Catenochytridium and Nephrochytrium, remain polyphyletic or paraphyletic. In addition to indicating these problems, our updated phylogeny supports the description of two new genera and three new species and thereby begins to bring the knowledge of the Cladochytriales up to date. [ABSTRACT FROM AUTHOR]

Ten Argentinean Waitea circinata-like isolates (Corticiales, Basidiomycota) obtained from soil samples were identified to species level based on morphological features and molecular tools, and tested in order to explore their biological control abilities. Eight of them were identified as Waitea arvalis and two as W. circinata. Waitea arvalis isolates were tested as antagonists, and W. circinata isolates were used as pathogens. Most isolates performed well in in vitro antagonistic assays, inhibiting by 54–59% the growth of seven plant pathogens: Bipolaris sorokiniana, Fusarium graminearum, Fusarium oxysporum, Pythium irregulare, two different isolates of W. circinata and Sclerotium rolfsii. The most inhibited pathogen was P. irregulare (72.6%), followed by S. rolfsii (66.7%) and W. circinata (~ 65%). There was no evidence of mycoparasitism. Their capacity for chitinase, cellulase, protease and ligninase (laccase, Mn-peroxidase and lignin-peroxidase) secretion was evaluated. Most of the isolates tested positive for chitinase activity. All the evaluated W. arvalis isolates produced cellulase, protease and laccase activities, and some of them also rendered positive results for Mn-peroxidase and decolorized the dye Azure B. Isolates INTA-L003 and INTA-L005 displayed top results for cellulase activity (both in carboxymethylcellulose plate assay and in filter paper tube assay). These activities might be responsible for their better antagonistic behavior on Pythium sp. Thus, these isolates are promising as biocontrol agents and as sources of new enzymes for industrial applications. Moreover, to the best of our knowledge, this is the first citation of W. arvalis in Argentina. [ABSTRACT FROM AUTHOR]

Owing to ineffective biological nitrogen (N) fixation, and imbalanced doses of N and potassium (K) fertilization, soybean productivity is declining under intensive cropping practices. This study aimed to find out the effectiveness of Trichoderma viride priming on soybean root growth promotion, and enhancement of soil-N and K uptakes under graded fertilization. Soybean seeds were treated with freshly prepared T. viride BHU-2953 talc. Root lignification, indole acetic acid (IAA) production, and activity of 1-aminocyclopropane-1-carboxylate deaminase (ACCD) were determined to observe root growth-promoting activities. A pot experiment was conducted to evaluate root volume, and soil exploration by roots, i.e., the potential volume of influence (PVI), uptakes of N and K, and different physiological attributes at different growth periods of soybean. Results revealed that biopriming enhanced the root lignification, IAA, and ACCD, assisting more soil volume exploration (PVI) for better N and K acquisitions. Biopriming increased the plant biomasses which ultimately, led to more seed yield than untreated soybeans, receiving even higher N-K doses. The PVI correlated well with root volume (r2 = 0.95; P < 0.01), explaining good soil-nutrient foraging by bioprimed soybeans. Hence, we conclude that T. viride priming can enhance the N and K uptakes in soybeans under graded soil fertilization. [ABSTRACT FROM AUTHOR]

We analyzed smut fungi (Ustilaginales) parasitizing nine poaceous hosts from three districts (Shangla, Mansehra, and Dir Upper) of Khyber Pakhtunkhwa Province, Pakistan. Eight taxa were identified in two genera using both morphological and molecular techniques by amplifying ITS and LSU regions. We describe Ustilago pseudosyntherismae sp. nov. on Digitaria sanguinalis and Dactylis glomerata as new to science and U. neocopinata as a new record for Pakistan. We provide updated distribution records for six smut species viz. U. maydis, U. tritici, Sporisorium cruentum, S. moniliferum, S. reilianum, and S. schweinfurthianum. [ABSTRACT FROM AUTHOR]

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias — tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of recommender systems in the long run. In this paper, we investigate how graph convolutions amplify the popularity bias in RS. Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (i.e., neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential. The two points make popular items get closer to almost users and thus being recommended more frequently. To rectify this, we propose to estimate the amplified effect of popular nodes on each node’s representation, and intervene the effect after each graph convolution. Specifically, we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node, then remove the effect from the node embeddings at each graph convolution layer. Our method is simple and generic — it can be used in the inference stage to correct existing models rather than training a new model from scratch, and can be applied to various GCN models. We demonstrate our method on two representative GCN backbones LightGCN and UltraGCN, verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular items. Codes are open-sourced 1). [ABSTRACT FROM AUTHOR]

Federated Learning (FL) has emerged as a powerful technology designed for collaborative training between multiple clients and a server while maintaining data privacy of clients. To enhance the privacy in FL, Differentially Private Federated Learning (DPFL) has gradually become one of the most effective approaches. As DPFL operates in the distributed settings, there exist potential malicious adversaries who manipulate some clients and the aggregation server to produce malicious parameters and disturb the learning model. However, existing aggregation protocols for DPFL concern either the existence of some corrupted clients (Byzantines) or the corrupted server. Such protocols are limited to eliminate the effects of corrupted clients and server when both are in existence simultaneously due to the complicated threat model. In this paper, we elaborate such adversarial threat model and propose BVDFed. To our best knowledge, it is the first Byzantine-resilient and Verifiable aggregation for Differentially private FEDerated learning. In specific, we propose Differentially Private Federated Averaging algorithm (DPFA) as our primary workflow of BVDFed, which is more lightweight and easily portable than traditional DPFL algorithm. We then introduce Loss Score to indicate the trustworthiness of disguised gradients in DPFL. Based on Loss Score, we propose an aggregation rule DPLoss to eliminate faulty gradients from Byzantine clients during server aggregation while preserving the privacy of clients’ data. Additionally, we design a secure verification scheme DPVeri that are compatible with DPFA and DPLoss to support the honest clients in verifying the integrity of received aggregated results. And DPVeri also provides resistance to collusion attacks with no more than t participants for our aggregation. Theoretical analysis and experimental results demonstrate our aggregation to be feasible and effective in practice. [ABSTRACT FROM AUTHOR]

Data augmentation is widely recognized as an effective means of bolstering model robustness. However, when applied to monocular 3D object detection, non-geometric image augmentation neglects the critical link between the image and physical space, resulting in the semantic collapse of the extended scene. To address this issue, we propose two geometric-level data augmentation operators named Geometric-Copy-Paste (Geo-CP) and Geometric-Crop-Shrink (Geo-CS). Both operators introduce geometric consistency based on the principle of perspective projection, complementing the options available for data augmentation in monocular 3D. Specifically, Geo-CP replicates local patches by reordering object depths to mitigate perspective occlusion conflicts, and Geo-CS re-crops local patches for simultaneous scaling of distance and scale to unify appearance and annotation. These operations ameliorate the problem of class imbalance in the monocular paradigm by increasing the quantity and distribution of geometrically consistent samples. Experiments demonstrate that our geometric-level augmentation operators effectively improve robustness and performance in the KITTI and Waymo monocular 3D detection benchmarks. [ABSTRACT FROM AUTHOR]

Random sample partition (RSP) is a newly developed big data representation and management model to deal with big data approximate computation problems. Academic research and practical applications have confirmed that RSP is an efficient solution for big data processing and analysis. However, a challenge for implementing RSP is determining an appropriate sample size for RSP data blocks. While a large sample size increases the burden of big data computation, a small size will lead to insufficient distribution information for RSP data blocks. To address this problem, this paper presents a novel density estimation-based method (DEM) to determine the optimal sample size for RSP data blocks. First, a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz (DKW) inequality by using the fixed-point iteration (FPI) method. Second, a practical sample size is determined by minimizing the validation error of a kernel density estimator (KDE) constructed on RSP data blocks for an increasing sample size. Finally, a series of persuasive experiments are conducted to validate the feasibility, rationality, and effectiveness of DEM. Experimental results show that (1) the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality; (2) the KDE constructed on RSP data blocks with sample size determined by DEM can yield a good approximation of the probability density function (p.d.f.); and (3) DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of p.d.f. estimation. This demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RSP implementation. [ABSTRACT FROM AUTHOR]

The advance in Non-Volatile Memory (NVM) has changed the traditional DRAM-only memory system. Compared to DRAM, NVM has the advantages of non-volatility and large capacity. However, as the read/write speed of NVM is still lower than that of DRAM, building DRAM/NVM-based hybrid memory systems is a feasible way of adding NVM into the current computer architecture. This paper aims to optimize the well-known B+-tree for hybrid memory. The novelty of this study is two-fold. First, we observed that the space utilization of internal nodes in B+-tree is generally below 70%. Inspired by this observation, we propose to maintain hot keys in the free space within internal nodes, yielding a new index named HATree (Hotness-Aware Tree). The new idea of HATree is to use the unused space of the parent of leaf nodes (PLNs) as the hotspot data cache. Thus, no extra space is needed, and the in-node hotspot cache can efficiently improve query performance. Second, to further improve the update performance of HATree, we propose to utilize the eADR technology supported by the third-generation Intel Xeon Scalable Processors to enhance HATree with instant log persistence, which results in the new HATree-Log structure. We conduct extensive experiments on real hybrid memory architecture involving DRAM and Intel Optane Persistent Memory to evaluate the performance of HATree and HATree-Log. Three state-of-the-art indices for hybrid memory, namely NBTree, LBTree, and FPTree, are included in the experiments, and the results suggest the efficiency of HATree and HATree-Log. [ABSTRACT FROM AUTHOR]

Human interaction recognition is an essential task in video surveillance. The current works on human interaction recognition mainly focus on the scenarios only containing the close-contact interactive subjects without other people. In this paper, we handle more practical but more challenging scenarios where interactive subjects are contactless and other subjects not involved in the interactions of interest are also present in the scene. To address this problem, we propose an Interactive Relation Embedding Network (IRE-Net) to simultaneously identify the subjects involved in the interaction and recognize their interaction category. As a new problem, we also build a new dataset with annotations and metrics for performance evaluation. Experimental results on this dataset show significant improvements of the proposed method when compared with current methods developed for human interaction recognition and group activity recognition. [ABSTRACT FROM AUTHOR]

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DeepLabel, which can achieve better performance compared with the existing issue classification approaches. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) guarantee timely data processing and secure information preservation within the range of the wireless access network, which is in urgent need of a new type of security technology. However, with the speedy development of hardware, the existing security schemes can no longer meet the new requirements of anonymity and lightweight. New solutions that do not require complex calculations, such as certificateless cryptography, attract great attention from researchers. To resolve these difficulties, Wang et al. designed a new authentication architecture for the WBANs environment, which was claimed to be secure and efficient. However, in this paper, we will show that this scheme is prone to ephemeral key leakage attacks. Further, based on this authentication scheme, an anonymous certificateless scheme is proposed for lightweight devices. Meanwhile, user anonymity is fully protected. The proposed scheme is proved to be secure under a specific security model. In addition, we assess the security attributes our scheme meets through BAN logic and Scyther tool. The comparisons of time consumption and communication cost are given at the end of the paper, to demonstrate that our scheme performs prior to several previous schemes. [ABSTRACT FROM AUTHOR]

In crowdsourcing scenarios, we can obtain each instance’s multiple noisy labels from different crowd workers and then infer its integrated label via label aggregation. In spite of the effectiveness of label aggregation methods, there still remains a certain level of noise in the integrated labels. Thus, some noise correction methods have been proposed to reduce the impact of noise in recent years. However, to the best of our knowledge, existing methods rarely consider an instance’s information from both its features and multiple noisy labels simultaneously when identifying a noise instance. In this study, we argue that the more distinguishable an instance’s features but the noisier its multiple noisy labels, the more likely it is a noise instance. Based on this premise, we propose a label distribution similarity-based noise correction (LDSNC) method. To measure whether an instance’s features are distinguishable, we obtain each instance’s predicted label distribution by building multiple classifiers using instances’ features and their integrated labels. To measure whether an instance’s multiple noisy labels are noisy, we obtain each instance’s multiple noisy label distribution using its multiple noisy labels. Then, we use the Kullback-Leibler (KL) divergence to calculate the similarity between the predicted label distribution and multiple noisy label distribution and define the instance with the lower similarity as a noise instance. The extensive experimental results on 34 simulated and four real-world crowdsourced datasets validate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper, we propose the concept of delegable zero knowledge succinct non-interactive arguments of knowledge (zk-SNARKs). The delegable zk-SNARK is parameterized by (μ,k,k′,k″). The delegable property of zk-SNARKs allows the prover to delegate its proving ability to μ proxies. Any k honest proxies are able to generate the correct proof for a statement, but the collusion of less than k proxies does not obtain information about the witness of the statement. We also define k′-soundness and k″-zero knowledge by taking into consider of multi-proxies. We propose a construction of (μ,2t + 1,t,t)- delegable zk-SNARK for the NPC language of arithmetic circuit satisfiability. Our delegable zk-SNARK stems from Groth’s zk-SNARK scheme (Groth16). We take advantage of the additive and multiplicative properties of polynomial-based secret sharing schemes to achieve delegation for zk-SNARK. Our secret sharing scheme works well with the pairing groups so that the nice succinct properties of Groth’s zk-SNARK scheme are preserved, while augmenting the delegable property and keeping soundness and zero-knowledge in the scenario of multi-proxies. [ABSTRACT FROM AUTHOR]

Modern recommendation systems are widely used in modern data centers. The random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and memory. ReRAM-based processing-in-memory (PIM) can resolve this problem by processing embedding vectors where they are stored. However, the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip, which induces off-chip accesses that may offset the PIM profits. Therefore, we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance loss. In this paper, we propose ARCHER, a ReRAM-based PIM architecture that implements fully on-chip recommendations under resource constraints. First, we make a full analysis of the computation pattern and access pattern on the decomposed table. Based on the computation pattern, we unify the operations of each layer of the decomposed model in multiply-and-accumulate operations. Based on the access observation, we propose a hierarchical mapping schema and a specialized hardware design to maximize resource utilization. Under the unified computation and mapping strategy, we can coordinate the inter-processing elements pipeline. The evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system, the state-of-the-art near-memory processing recommendation system RecNMP, and the ReRAM-based recommendation accelerator REREC by 15.79×, 2.21×, and 1.21× in terms of performance and 56.06×, 6.45×, and 1.71× in terms of energy savings, respectively. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT), data sharing among different devices can improve manufacture efficiency and reduce workload, and yet make the network systems be more vulnerable to various intrusion attacks. There has been realistic demand to develop an efficient intrusion detection algorithm for connected devices. Most of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack types. In this paper, a distributed federated intrusion detection method is proposed, utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack types. Besides, the blockchain technique is introduced in the federated learning process for the consensus of the entire framework. Experimental results are provided to show that our approach can identify the malicious entities, while outperforming the existing methods in discovering new intrusion attack types. [ABSTRACT FROM AUTHOR]

Question Generation (QG) is the task of generating questions according to the given contexts. Most of the existing methods are based on Recurrent Neural Networks (RNNs) for generating questions with passage-level input for providing more details, which seriously suffer from such problems as gradient vanishing and ineffective information utilization. In fact, reasonably extracting useful information from a given context is more in line with our actual needs during questioning especially in the education scenario. To that end, in this paper, we propose a novel Hierarchical Answer-Aware and Context-Aware Network (HACAN) to construct a high-quality passage representation and judge the balance between the sentences and the whole passage. Specifically, a Hierarchical Passage Encoder (HPE) is proposed to construct an answer-aware and context-aware passage representation, with a strategy of utilizing multi-hop reasoning. Then, we draw inspiration from the actual human questioning process and design a Hierarchical Passage-aware Decoder (HPD) which determines when to utilize the passage information. We conduct extensive experiments on the SQuAD dataset, where the results verify the effectiveness of our model in comparison with several baselines. [ABSTRACT FROM AUTHOR]

The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation inside each window, have achieved outstanding success. However, since the relation modeling between windows was not the primary emphasis of previous work, it was not fully utilized. To address this issue, we propose a Graph-Segmenter, including a graph transformer and a boundary-aware attention module, which is an effective network for simultaneously modeling the more profound relation between windows in a global view and various pixels inside each window as a local one, and for substantial low-cost boundary adjustment. Specifically, we treat every window and pixel inside the window as nodes to construct graphs for both views and devise the graph transformer. The introduced boundary-aware attention module optimizes the edge information of the target objects by modeling the relationship between the pixel on the object’s edge. Extensive experiments on three widely used semantic segmentation datasets (Cityscapes, ADE-20k and PASCAL Context) demonstrate that our proposed network, a Graph Transformer with Boundary-aware Attention, can achieve state-of-the-art segmentation performance. [ABSTRACT FROM AUTHOR]

Geographically replicating objects across multiple data centers improves the performance and reliability of cloud storage systems. Maintaining consistent replicas comes with high synchronization costs, as it faces more expensive WAN transport prices and increased latency. Periodic replication is the widely used technique to reduce the synchronization costs. Periodic replication strategies in existing cloud storage systems are too static to handle traffic changes, which indicates that they are inflexible in the face of unforeseen loads, resulting in additional synchronization cost. We propose quantitative analysis models to quantify consistency and synchronization cost for periodically replicated systems, and derive the optimal synchronization period to achieve the best tradeoff between consistency and synchronization cost. Based on this, we propose a dynamic periodic synchronization method, Sync-Opt, which allows systems to set the optimal synchronization period according to the variable load in clouds to minimize the synchronization cost. Simulation results demonstrate the effectiveness of our models. Compared with the policies widely used in modern cloud storage systems, the Sync-Opt strategy significantly reduces the synchronization cost. [ABSTRACT FROM AUTHOR]