Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Online Sparse Decision Making

This paper presents a novel federated linear contextual bandits model, where individual clients face different K-armed stochastic bandits with high-dimensional decision context and coupled through common global parameters. By leveraging the sparsity structure of the linear reward , a collaborative algorithm named \texttt{Fedego Lasso} is proposed to cope with the heterogeneity across clients without exchanging local decision context vectors or raw reward data. \texttt{Fedego Lasso} relies on a novel multi-client teamwork-selfish bandit policy design, and achieves near-optimal regrets for shared parameter cases with logarithmic communication costs. In addition, a new conceptual tool called federated-egocentric policies is introduced to delineate exploration-exploitation trade-off. Experiments demonstrate the effectiveness of the proposed algorithms on both synthetic and real-world datasets.
AugESC: Large-scale Data Augmentation for Emotional Support Conversation with Pre-trained Language Models

Crowd-sourcing is commonly adopted for dialog data collection. However, it is highly costly and time-consuming, and the collected data is limited in scale and topic coverage. In this paper, aiming to generate emotional support conversations, we propose exploiting large-scale pre-trained language models for data augmentation, and provide key ﬁndings in our pilot exploration. Our adopted approach leverages the 6B-parameter GPT-J model and utilizes publicly available dialog posts to trigger conversations on various topics. Then we construct A UG ESC 1 , a machine-augmented dataset for emotional support conversation. It is two orders of magnitude larger than the original ESConv dataset in scale, covers more diverse topics, and is shown to be of high quality by human evaluation. Lastly, we demonstrate with interactive evaluation that A UG ESC can further enhance dialog models tuned on ES-Conv to handle various conversation topics and to provide signiﬁcantly more effective emotional support.
Fuse Local and Global Semantics in Representation Learning

We propose Fuse Local and Global Semantics in Representation Learning (FLAGS) to generate richer representations. FLAGS aims at extract both global and local semantics from images to benefit various downstream tasks. It shows promising results under common linear evaluation protocol. We also conduct detection and segmentation on PASCAL VOC and COCO to show the representations extracted by FLAGS are transferable.
Probing the initial longitudinal density profile and electromagnetic field in ultrarelativistic heavy-ion collisions with heavy quarks

Heavy quarks are valuable probes of the electromagnetic ﬁeld and the initial condition of the quark-gluon plasma (QGP) matter produced in high-energy nuclear collisions. Within an improved Langevin model that is coupled to a (3+1)-dimensional viscous hydrodynamic model, we explore the origin of the directed ﬂow coefﬁcient ( v 1 ) of heavy mesons and their decay leptons, and its splitting ( ∆ v 1 ) between opposite charges. We ﬁnd that while the rapidity dependence of the heavy ﬂavor v 1 is mainly driven by the titled energy density proﬁle of the QGP with respect to the longitudinal direction at the RHIC energy, it is dominated by the electromagnetic ﬁeld at the LHC energy. The ∆ v 1 serves as a novel probe of the spacetime evolution proﬁle of the electromagnetic ﬁeld. Our results of D mesons and their decay electrons are consistent with the available data at RHIC and LHC, and our predictions on the heavy ﬂavor decay muons can be further tested by future measurements.
Bina-Rep Event Frames: A Simple and Effective Representation for Event-Based Cameras

This paper presents "Bina-Rep", a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. By representing multiple binary event images as a single frame of N-bit numbers, our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream. Coupled with our proposed model based on a convolutional neural network, the reported results achieve state-of-the-art performance and repeatedly outperforms other common event representation methods. Our approach also shows competitive robustness against common image corruptions, compared to other representation techniques.
MaMaDroid2.0 - The Holes of Control Flow Graphs

Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malicious samples such that those samples will be misclassified as benign. This paper fully inspects a well-known Android malware detection system, MaMaDroid, which analyzes the control flow graph of the application. Changes to the portion of benign samples in the train set and models are considered to see their effect on the classifier. The changes in the ratio between benign and malicious samples have a clear effect on each one of the models, resulting in a decrease of more than 40% in their detection rate. Moreover, adopted ML models are implemented as well, including 5-NN, Decision Tree, and Adaboost. Exploration of the six models reveals a typical behavior in different cases, of tree-based models and distance-based models. Moreover, three novel attacks that manipulate the CFG and their detection rates are described for each one of the targeted models. The attacks decrease the detection rate of most of the models to 0%, with regards to different ratios of benign to malicious apps. As a result, a new version of MaMaDroid is engineered. This model fuses the CFG of the app and static analysis of features of the app. This improved model is proved to be robust against evasion attacks targeting both CFG-based models and static analysis models, achieving a detection rate of more than 90% against each one of the attacks.
Combating the instability of mutual information-based losses via regularization

Notable progress has been made in numerous fields of machine learning based on neural network-driven mutual information (MI) bounds. However, utilizing the conventional MI-based losses is often challenging due to their practical and mathematical limitations. In this work, we first identify the symptoms behind their instability: (1) the neural network not converging even after the loss seemed to converge, and (2) saturating neural network outputs causing the loss to diverge. We mitigate both issues by adding a novel regularization term to the existing losses. We theoretically and experimentally demonstrate that added regularization stabilizes training. Finally, we present a novel benchmark that evaluates MI-based losses on both the MI estimation power and its capability on the downstream tasks, closely following the pre-existing supervised and contrastive learning settings. We evaluate six different MI-based losses and their regularized counterparts on multiple benchmarks to show that our approach is simple yet effective.
Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks

The study of multidimensional discriminator (critic) output for Generative Adversarial Networks has been underexplored in the literature. In this paper, we generalize the Wasserstein GAN framework to take advantage of multidimensional critic output and explore its properties. We also introduce a square-root velocity transformation (SRVT) block which favors training in the multidimensional setting. Proofs of properties are based on our proposed maximal p-centrality discrepancy, which is bounded above by p-Wasserstein distance and fits the Wasserstein GAN framework with multidimensional critic output n. Especially when n = 1 and p = 1, the proposed discrepancy equals 1-Wasserstein distance. Theoretical analysis and empirical evidence show that high-dimensional critic output has its advantage on distinguishing real and fake distributions, and benefits faster convergence and diversity of results.
Understanding I/O Direct Cache Access Performance for End Host Networking

Direct Cache Access (DCA) enables a network interface card (NIC) to load and store data directly on the processor cache, as conventional Direct Memory Access (DMA) is no longer suitable as the bridge between NIC and CPU in the era of 100 Gigabit Ethernet. As numerous I/O devices and cores compete for scarce cache resources, making the most of DCA for networking applications with varied objectives and constraints is a challenge, especially given the increasing complexity of modern cache hardware and I/O stacks. In this paper, we reverse engineer details of one commercial implementation of DCA, Intel's Data Direct I/O (DDIO), to explicate the importance of hardware-level investigation into DCA. Based on the learned knowledge of DCA and network I/O stacks, we (1) develop an analytical framework to predict the effectiveness of DCA (i.e., its hit rate) under certain hardware specifications, system configurations, and application properties; (2) measure penalties of the ineffective use of DCA (i.e., its miss penalty) to characterize its benefits; and (3) show that our reverse engineering, measurement, and model contribute to a deeper understanding of DCA, which in turn helps diagnose, optimize, and design end-host networking.
Amplitude and wavelength scaling of sinusoidal roughness effects in turbulent channel flow at fixed $\boldsymbol {{Re}_\tau = 720}$

Abstract Direct numerical simulations are performed for incompressible, turbulent channel flow over a smooth wall and different sinusoidal wall roughness configurations at a constant $Re_\tau = 720$. Sinusoidal walls are used to study the effects of well-defined geometric features of roughness-amplitude, $a$, and wavelength, $\lambda$, on the flow. The flow in the near-wall region is strongly influenced by both $a$ and $\lambda$. Establishing appropriate scaling laws will aid in understanding the effects of roughness and identifying the relevant physical mechanisms. Using inner variables and the roughness function to scale the flow quantities provides support for Townsend's hypothesis, but inner scaling is unable to capture the flow physics in the near-wall region. We provide modified scaling relations considering the dynamics of the shear layer and its interaction with the roughness. Although not a particularly surprising observation, this study provides clear evidence of the dependence of flow features on both $a$ and $\lambda$. With these relations, we are able to collapse and/or align peaks for some flow quantities and, thus, capture the effects of surface roughness on turbulent flows even in the near-wall region. The shear-layer scaling supports the hypothesis that the physical mechanisms responsible for turbulent kinetic energy production in turbulent flows over rough walls are greatly influenced by the shear layer and its interaction with the roughness elements. Finally, a semiempirical model is developed to predict the contribution of pressure and skin friction drag on the roughness element based purely on its geometric parameters and the corresponding shear-layer velocity scale.
Potentials and challenges of analyzing use phase data in product planning of manufacturing companies

Abstract The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.
Phospho-proteomic analysis of CAR-T cell signaling following activation by antigen-presenting cancer cells

Chimeric antigen receptors (CARs) are synthetic biomolecules comprised of an extracellular antigen recognition domain and intracellular signaling domains. When expressed in immune cells, CARs direct their host cells to kill diseased cells expressing the antigen recognized by the CAR. Although signaling pathways downstream of CAR activation control the cytotoxic function of CAR-expressing cells, phospho-proteomic studies of CAR signaling have been limited. Most approaches have used antibodies or soluble ligands, rather than cell-displayed antigens, to activate CAR signaling. Here, we demonstrate an efficient and cost-effective label-free phospho-proteomic approach to analyze CAR signaling in immune cells stimulated with antigen-presenting cancer cells. Following co-culture of CAR-T cells with cancer cells, we first preserve phospho-signaling by cross-linking proteins with formalin. Then, we use magnet-activated cell sorting (MACS) to isolate CAR-T cells from the co-culture. Validation experiments demonstrated that formalin fixation did not alter the phospho-proteome and that MACS achieved >90% CAR-T cell purity. Next, we compared the phospho-proteome in CAR-T cells stimulated with either CD19-expressing or non-CD19-expressing SKOV3 ovarian cancer cells. This analysis revealed that CAR signaling activated known pathways including the mitogen- activated protein kinases (MAPKs) ERK1/2. Bioinformatic approaches further showed that CAR activation induced other signaling pathways including the MAPK p38α, protein kinase A, and checkpoint kinase 1 (CHK1). Taken together, this work presents an easy and inexpensive method to better understand CAR immunotherapy by label-free phospho-proteomic analysis of CAR signaling in immune cells stimulated by antigen- presenting cancer cells.
Genetic deletion of Kiaa1109 in mice leads to developmental defects at the neuromuscular junction

KIAA1109 (4932438A13Rik) is a novel gene linked to Alkuraya-Kucinska Syndrome, an autosomal recessive disorder with severe brain malformations and arthrogryposis in humans. The role of KIAA1109 in mammalian development and function is unknown. Here, we characterize mutant mice deficient in Kiaa1109 (Kiaa1109). We report that Kiaa1109 mice died during perinatal stages. These Kiaa1109 embryos exhibited impaired intramuscular nerve growth and reduced sizes of the neuromuscular junction (NMJ) compared with their littermate controls. Electrophysiological analysis further revealed defects in neuromuscular synaptic transmission in Kiaa1109 embryos. Notably, the frequency of spontaneous neurotransmitter release was markedly increased, whereas evoked neurotransmitter release and quantal content were reduced. Furthermore, neuromuscular synapses in Kiaa1109 embryos failed to respond to a repetitive, low frequency stimulation (10Hz). These results demonstrate that KIAA1109 is required for survival in mice and for proper development and function of the NMJ.
The circ_0002538/miR-138-5p/plasmolipin axis regulates Schwann cell migration and myelination in diabetic peripheral neuropathy

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes, but the underlying molecular pathogenesis remains unclear. Accumulating evidence indicates that circular RNAs (circRNAs) play vital roles in DPN, while their expressions and functions in Schwann cell (SC) are rarely reported. Here, we performed protein profiling and circRNA sequencing on the peripheral nerves of patients with or without DPN. A total of 265 differentially expressed proteins were identified in DPN by protein profiling, which mainly enrich in myelination according to Gene Ontology analysis. Further, 15637 circRNAs were identified by circRNA sequencing, of which 11 were verified to be dysregulated. Among them, circ_0002538 was found to be downregulated in DPN and chosen for further investigation. Functional experiments revealed that circ_0002538 overexpression promoted SC migration and relieved demyelination in DPN. Mechanistic studies revealed that circ_0002538 could promote PLLP expression by sponging miR-138-5p, while lack of circ_0002538 led to PLLP deficiency, which further suppressed SC migration and caused demyelination. The present research provided novel insights into the pathogenesis of DPN, in which the circ_0002538/miR-138-5p/PLLP axis was suppressed in SCs and hence caused demyelination. These findings expanded the role of circRNAs in DPN and provided potential therapeutic targets for DPN.
Domestication and lowland adaptation of coastal preceramic maize from Paredones, Peru

Archaeological cobs from Paredones and Huaca Prieta (Peru) are phenotypically indistinguishable from modern maize. This contrasts with the earliest Mexican macro-specimens from Guila Naquitz and San Marcos, which are phenotypically intermediate even though they date more recently in time. These observations suggest at least two alternative scenarios, one in which maize was domesticated earlier than previously thought in the lowland Mesoamerica, followed by rapid lowland dispersal to Peru, and another in which maize was independently domesticated in South America and subsequently lost, as current evidence supports a single origin for all modern maize. To gain insights into the origins of ancient Peruvian maize, we sequenced DNA from three Paredones specimens dating 6775 to 5000 calibrated years before present (BP) and conducted comparative analyses with two teosinte subspecies (Zea mays ssp. mexicana and parviglumis) and extant maize, including highland and lowland landraces from Mesoamerica and South America. We show that Paredones maize originated from the same domestication event as Mexican maize and was domesticated by 6775 BP, implying rapid dispersal followed by improvement. Paredones maize show minimal levels of gene flow from mexicana, smaller than those observed in teosinte parviglumis. It also harbors significantly fewer alleles previously found to be adaptive to highlands, but not of alleles adaptive to lowlands, supporting a lowland migration route. Our overall results imply that Paredones maize originated in Mesoamerica, arrived in Peru without mexicana introgression through a rapid lowland migration route, and underwent improvements in both Mesoamerica and South America. Significance Statement The coastal Peruvian preceramic sites of Paredones and Huaca Prieta provide the earliest known maize macro-remains. Found more than 3,800 km away from the maize center of origin and presenting a phenotypically modern cob constitution relative to their antiquity, these specimens represent a paradox for understanding maize evolution and dispersal. We show that Paredones maize originated in Mesoamerica, like all known maize, and arrived in South America without introgression from the teosinte mexicana. Since modern maize has substantial contributions from mexicana, it raises the question of when mexicana introgression spread to South America. Paredones maize preferentially shares adaptive allelic diversity with lowland Mesoamerican samples, suggesting a migration route probably associated with a coastal corridor previously identified with archeological findings.
Western diet unmasks transient low-level vinyl chloride exposure-induced tumorigenesis; potential role of the (epi-)transcriptome

Background & Aims Vinyl chloride (VC) monomer is a volatile organic compound commonly used in industry to produce polyvinyl chloride (PVC). At high exposure levels, VC causes liver cancer and toxicant-associated steatohepatitis. However, lower exposure levels (i.e., < regulatory exposure limits) that do not directly damage the liver, enhance injury caused by Western diet (WD). Although these lower exposure levels are considered ‘safe,’ it is unknown if the long-term impact of transient low-concentration VC enhances the risk of liver cancer development. Low-concentration VC is especially a concern given that fatty liver disease is in and of itself a risk factor for the development of liver cancer. Methods C57Bl/6J mice were fed WD or control diet (CD) for 1 year. During the first 12 weeks of feeding only, mice were also exposed to VC via inhalation at sub-regulatory limit concentrations (<1 ppm) or air for 6 hours/day, 5 days/week. Results Feeding WD for 1 year caused significant hepatic injury, including steatohepatitis and moderate fibrosis, which was exacerbated by VC. Additionally, VC increased the number of tumors which ranged from moderately to poorly differentiated hepatocellular carcinoma (HCC). Transcriptomic analysis demonstrated VC-induced changes in metabolic but also ribosomal processes. Epitranscriptomic analysis showed a VC-induced shift of the modification pattern that has been associated with metabolic disease, mitochondrial dysfunction, and cancer. Conclusions These data indicate that VC sensitizes the liver to other stressors (e.g., WD), resulting in enhanced tumorigenesis. These data raise concerns about a potential interaction between VC exposure and WD. Furthermore, it also emphasizes that current safety restrictions may be insufficient to account for other factors that can influence hepatotoxicity.
Development of duplex real‐time PCR for quick detection of cryptosporidiosis in goats

Cryptosporidium spp. is the most important foodborne and waterborne pathogens and a leading cause of mortality from foodborne and waterborne gastrointestinal diseases. In neonates of domestic animals, it is associated with consistent diarrhea and dehydration. Cryptosporidium infection begins with the ingestion of sporulated oocytes disseminated by carrier animals that consistently contaminate the environment. Many diagnostic tests are available including microscopy and antigen trap‐ELISA, but none of the diagnostic tests available currently cannot differentiate between active and passive infection in the host. In the current study, to address this challenge an mRNA‐based duplex TaqMan® probe PCR was developed to target the Cryptosporidium oocyst wall protein gene and 18SSU rRNA gene in a single tube that can detect metabolically active cryptosporidial oocysts. The mRNA transcripts are the direct indicator of any actively replicating cell and they will help decipher the active stages of its lifecycle in a host. This diagnostic assay was standardized by computing transcript copy number‐based limit of detection (LOD). For COWP and 18SSU rRNA genes, the LOD was 7.08 × 1004 and 5.95 × 1005, respectively. During active infections, the oocyst wall protein will be active and so its COWP gene transcripts will act as a marker for active infection. While transcripts for 18SSU rRNA are constitutively expressed in cryptosporidial life cycle. This current diagnostic assay will be a quantitative marker that will help assess the active stages of Cryptosporidium infection in neonates. The disease dynamics will help better understand to formulate the control strategies and contain infection among healthy animals.