Miracle-dz/UniFAR-FTUs huggingface dataset:
CMAR_A_302274 3209..3218

Department of Thoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China Objective: To evaluate the correlation between systemic inflammation markers and sarcopenia in elderly patients with esophageal squamous cell carcinoma (ESCC) and their prognostic value. Materials and Methods: The clinical data of 121 elderly patients with ESCC were collected. The skeletal muscle area at the level of the third lumbar vertebrae (L3) was measured by computed tomography (CT), and then the skeletal muscle index (SMI) was calculated. The neutrophil-to-lymphocyte ratio (NLR), prognostic nutritional index (PNI) and Geriatric Nutritional Risk Index (GNRI) were calculated according to laboratory standards. Univariate and multivariate Cox proportional hazards models were used to determine prognostic factors for overall survival (OS). Results: A total of 121 elderly ESCC patients were enrolled. Among them, 65 patients had sarcopenia. NLR, PNI and GNRI are significantly related to sarcopenia. The OS of ESCC patients with sarcopenia and/or NLR>2.24 was significantly worse. Conclusion: PNI, GNRI, NLR and sarcopenia were significantly related. Sarcopenia and NLR are independent prognostic factors for elderly ESCC, and when combined have better prognostic value.
Academic Mindsets and Behaviors, Prior Achievement, and the Transition to Middle School

An ever-growing body of evidence suggests that students’ academic mindsets can have a powerful impact on student engagement and outcomes in school. Scholars have argued there is a recursive cycle in which students’ academic mindsets affect their academic behaviors. These academic behaviors affect their success in school, which in turn affects students’ academic mindsets, restarting the cycle. 1 This cycle can be positive and lead to improved academic outcomes, or can be negative and undermine students’ engagement and success.
Virtual team working in the european automotive industry: user requirements and a case study approach

The globalization of the automotive industry and increasing competition from U.S. and Asian-Pacific firms are forcing the European automotive industry to introduce information technology and telecommunications that enable more efficient collaboration along the supply chain, and hence a reduction in the time to market for new vehicles. This paper illustrates the nature of the automotive supply chain and summarizes current barriers to collaborative working between European automotive manufacturers and suppliers. To achieve a greater degree of concurrent engineering, organizational and user requirements are outlined, drawn from a series of collaborative European automotive projects. Finally, a case study is presented, based on the development, demonstration, and evaluation of a heterogeneous computer-supported cooperative working platform to support working between virtual teams during the product introduction process. © 2000 John Wiley & Sons, Inc.
Effects of Maternal Alcohol Consumption on the Haematological Parameters in the Pups of Albino Rats

It has been observed that some pregnant women especially in riverine areas indulge in heavy alcohol consumption. Alcohol ingestion during pregnancy can result in fetal disturbances in their off-springs. This study was undertaken prospectively to determine the possible effect of maternal alcohol consumption on the haematological parameters in the pups of albino rats. Three groups of the animals A, B and C (10 albino rats in each group) were used in this study. Group A was the control while groups B and C served as test groups. Group B (prenatal group) was exposed to 1ml of 20% alcohol per kg weight up to the delivery time, while group C (postnatal group) was administered with 1ml of 20% alcohol per kg weight up to the weaning period. A total of 120 off-springs from the three groups were used in the investigations. Eight off-springs were randomly selected every week from each group and 2mls of blood was collected by ocular puncture for a period of five weeks. All haematological parameters were evaluated using Sysmex haematology analyzer. The mean leucocytes value was significantly lower (p<0.05) in the test group B and C compared with control group A. However there was no significant change in the mean value of haemoglobin levels (p>0.05) of the off-springs compared with control. Furthermore while the mean values of haematocrit were significantly lower P<0.05) in test groups of weeks 3, 5 and 6, the MCHC showed significant differences in weeks 5 and 6 of test groups compared with control. These investigations have therefore demonstrated that maternal alcohol consumption during prenatal and postnatal periods has some deleterious effects on some haematological parameters and has revealed potential risks in their offsprings. Therefore, there is the need for controlled alcohol consumption during pregnancy.
Learning Generalizable Behavior via Visual Rewrite Rules

Though deep reinforcement learning agents have achieved unprecedented success in recent years, their learned policies can be brittle, failing to generalize to even slight modifications of their environments or unfamiliar situations. The black-box nature of the neural network learning dynamics makes it impossible to audit trained deep agents and recover from such failures. In this paper, we propose a novel representation and learning approach to capture environment dynamics without using neural networks. It originates from the observation that, in games designed for people, the effect of an action can often be perceived in the form of local changes in consecutive visual observations. Our algorithm is designed to extract such vision-based changes and condense them into a set of action-dependent descriptive rules, which we call ''visual rewrite rules'' (VRRs). We also present preliminary results from a VRR agent that can explore, expand its rule set, and solve a game via planning with its learned VRR world model. In several classical games, our non-deep agent demonstrates superior performance, extreme sample efficiency, and robust generalization ability compared with several mainstream deep agents.
More Control for Free! Image Synthesis with Semantic Diffusion Guidance

Controllable image synthesis models allow creation of diverse images based on text instructions or guidance from a reference image. Recently, denoising diffusion probabilistic models have been shown to generate more realistic imagery than prior methods, and have been successfully demonstrated in unconditional and class-conditional settings. We investigate fine-grained, continuous control of this model class, and introduce a novel unified framework for semantic diffusion guidance, which allows either language or image guidance, or both. Guidance is injected into a pretrained unconditional diffusion model using the gradient of image-text or image matching scores, without re-training the diffusion model. We explore CLIP-based language guidance as well as both content and style-based image guidance in a unified framework. Our text-guided synthesis approach can be applied to datasets without associated text annotations. We conduct experiments on FFHQ and LSUN datasets, and show results on fine-grained text-guided image synthesis, synthesis of images related to a style or content reference image, and examples with both textual and image guidance.1
Energy-efficient Data-intensive Computing with a Fast Array of Wimpy Nodes

Abstract : Large-scale data-intensive computing systems have become a critical foundation for Internet-scale services. Their widespread growth during the past decade has raised datacenter energy demand and created an increasingly large financial burden and scaling challenge: Peak energy requirements today are a significant cost of provisioning and operating datacenters. In this thesis we propose to reduce the peak energy consumption of datacenters by using a FAWN: A Fast Array of Wimpy Nodes. FAWN is an approach to building datacenter server clusters using low-cost, low-power servers that are individually optimized for energy efficiency rather than raw performance alone. FAWN systems, however, have a different set of resource constraints than traditional systems that can prevent existing software from reaping the improved energy efficiency benefits FAWN systems can provide. This dissertation describes the principles behind FAWN and the software techniques necessary to unlock its energy efficiency potential. First, we present a deep study into building FAWN-KV, a distributed, log-structured key-value storage system designed for an early FAWN prototype. Second, we present a broader classification and workload analysis showing when FAWN can be more energy-efficient and under what workload conditions a FAWN cluster would perform poorly in comparison to a smaller number of high-speed systems. Last, we describe modern trends that portend a narrowing gap between CPU and I/O capability and highlight the challenges endemic to all future balanced systems. Using FAWN as an early example, we demonstrate that pervasive use of vector interfaces throughout distributed storage systems can improve throughput by an order of magnitude and eliminate the redundant work found in many data-intensive workloads.
Defending Label Inference and Backdoor Attacks in Vertical Federated Learning

Since there are multiple parties in collaborative learning settings like federated learning, curious parities might be honest but are attempted to infer other parties’ private data through inference attacks while malicious parties might manipulate the learning process for their own purposes through backdoor attacks. However, most existing works only consider the federated learning scenario where data are partitioned by samples (HFL). The feature-partitioned federated learning (VFL) can be another important scenario in many real-world applications. Attacks and defenses in such scenarios are especially challenging when the attackers and the defenders are not able to access the features or model parameters of other participants. Previous work have only shown that private labels can be reconstructed from communication of per-sample gradients. In this paper, we ﬁrst show that private labels can be reconstructed even when only batch-averaged gradients instead of sample-level gradients are revealed. It is a common presumption that batch-averaged information is safe to share, therefore batch label inference attack is a severe challenge to VFL. In addition, we show that a passive party (a party without labels) in VFL can even replace its corresponding labels in the active party with a target label through a gradient-replacement attack. To defend against batch label inference attack, we introduce a novel technique termed confusional autoencoder (CoAE), based on autoencoder and entropy regularization. We demonstrate that label inference attacks can be successfully blocked by this technique without hurting as much main task accuracy as compared to existing meth-ods. Our CoAE technique is also effective in defending the gradient-replacement backdoor attack, making it an universal and practical defense strategy with no change to the original VFL protocol. We demonstrate the effectiveness of our approaches under both two-party and multi-party VFL settings. To the best of our knowledge, this is the ﬁrst systematic study to deal with label inference and backdoor attacks in the feature-partitioned federated learning framework.
Quantile Spectral Beta: A Tale of Tail Risks, Investment Horizons, and Asset Prices

This paper investigates how two important sources of risk – market tail risk and extreme market volatility risk – are priced into the cross-section of asset returns across various investment horizons. To identify such risks, we propose a quantile spectral beta representation of risk based on the decomposition of covariance between indicator functions that capture fluctuations over various frequencies. We study the asymptotic behavior of the proposed estimators of such risk. Empirically, we find that tail risk is a short-term phenomenon, whereas extreme volatility risk is priced by investors in the long term when pricing a cross-section of individual stocks. In addition, we study popular industry, size and value, profit, investment or book-to-market portfolios, as well as portfolios constructed from various asset classes, portfolios sorted on cash flow duration and other strategies. These results reveal that tail-dependent and horizon-specific risks are priced heterogeneously across datasets and are important sources of risk for investors.
Determination of Some Concentrated Atmospheric Pollutants Such As Heavy Metal ( Pb , Mn , Cd , Cr And Zn ) In Lichens Found In Urban And Peri-Urban Areas of Kaduna State

Atmospheric deposition of heavy metals like (Pb, Mn, Cd, Cr and Zn) was investigated using lichens as suction points. The lichens samples were collected from four study areas namely; ABU Botanical garden, PZ area, (Sabon Gari Local Government) Buruku and Rido village (Chikun Local Government). Samples were collected in two seasons, that is three months in Dry season and three months in Wet season, the sample were collected 300m away from the main road. The representative samples were wet digested in Hydrogen trioxonitrate (HNO 3 ) and Hydrochloric acid (HCL). Heavy metals content were measured in the extract using Atomic Absorption Spectrometry (AAS); Quantitative analysis of the samples was carried out using Sequential Atomic Absorption Spectrometry (Varian AAS 240FS). Data obtained was analyzed statistically using Analyses of variance (ANOVA), Duncan’s multiple range test (DMRT) was used to separate the means where there is significant differences. The species of lichens collected are; Phaeophyscia species, Xanthoparmelia spp, Flavopermelia capirata, Dictyonema glabratum, and Physcia species. There is significant difference among the lichens in the accumulation of all the pollutants and Flavopermelia capirata has higher accumulation of the most pollutants. The results from all the locations shows that, PZ area is significantly higher in Pb, Zn and SO 2 , Buruku is significantly higher in Mn and Cd. Cr show no significant difference among the locations. Also, for the seasonal variation dry season (Jan., Feb., Mar.) shows high content of Zn (0.221), Cr (0.286ppm). The high content of Pb (0.183ppm) and Mn (2.146ppm) recorded during wet season (Jul., Aug., Sept.) while the concentration of Cd is uniformly recorded in both dry and wet seasons. The accumulation of heavy metals by lichens species ranged as follows; Physcia pecies (1.69 – 0.01ppm), Phaeophyscia species (1.46 – 0.01ppm), Dictyonema glabratum (0.64 – 0.02ppm), Xanthopermelia caperata (2.12 – 0.02ppm), Flavopermelia caperata (3.84 – 0.02ppm), Manganese (3.84) has the higher concentration in the lichens species and is significantly higher than the metal with lower deposition which is Cadmium (0.02ppm). Data Obtained reveal the important contributions towards understanding of heavy metal deposition patterns and provide baseline data, that can be used for identification of areas potential at risk from atmospheric pollutants contamination in the areas and seasons. The use of epiphytic lichens can provide a cost–effective approach for monitoring atmospheric pollutants contamination and may be effectively used in large scale air pollution monitoring programmes.
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs

We propose a framework which makes it feasible to directly train deep neural networks with respect to popular families of task-specific non-decomposable performance measures such as AUC, multi-class AUC, F-measure and others. A feature of the optimization model that emerges from these tasks is that it involves solving a Linear Programs (LP) during training where representations learned by upstream layers characterize the constraints or the feasible set. The constraint matrix is not only large but the constraints are also modified at each iteration. We show how adopting a set of ingenious ideas proposed by Mangasarian for 1-norm SVMs - which advocates for solving LPs with a generalized Newton method - provides a simple and effective solution that can be run on the GPU. In particular, this strategy needs little unrolling, which makes it more efficient during the backward pass. Further, even when the constraint matrix is too large to fit on the GPU memory (say large minibatch settings), we show that running the Newton method in a lower dimensional space yields accurate gradients for training, by utilizing a statistical concept called sufficient dimension reduction. While a number of specialized algorithms have been proposed for the models that we describe here, our module turns out to be applicable without any specific adjustments or relaxations. We describe each use case, study its properties and demonstrate the efficacy of the approach over alternatives which use surrogate lower bounds and often, specialized optimization schemes. Frequently, we achieve superior computational behavior and performance improvements on common datasets used in the literature.
Building energy and comfort management through occupant behaviour pattern detection based on a large-scale environmental sensor network

Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security. However, the ability to discern the actual number of people in a room is beyond the scope of most current sensing techniques. To address this issue, a complex environmental sensor network is deployed in the Robert L. Preger Intelligent Workplace (IW) at Carnegie Mellon University. The results indicate that there are significant correlations between measured environmental conditions and occupancy status. It is shown that an average of 83% accuracy on the occupancy number detection was achieved by Gaussian Mixture Model based Hidden Markov Models during testing periods. To illustrate the consequent energy impact based on the occupant behaviour detection (i.e. number and duration of occupancy) in the space, an EnergyPlus model of the IW with an assumed standard variable air volume (VAV) system is created. Simulations are conducted to compare the energy consumption consequences between a prescribed occupancy schedule according to the ASHRAE 90.1 base case with the predicted occupancy behaviour. The results show that energy saving of 18.5% can be achieved in the IW while maintaining indoor thermal comfort.
Edge-aware Guidance Fusion Network for RGB Thermal Scene Parsing

RGB–thermal scene parsing has recently attracted increasing research interest in the field of computer vision. However, most existing methods fail to perform good boundary extraction for prediction maps and cannot fully use high-level features. In addition, these methods simply fuse the features from RGB and thermal modalities but are unable to obtain comprehensive fused features. To address these problems, we propose an edge-aware guidance fusion network (EGFNet) for RGB–thermal scene parsing. First, we introduce a prior edge map generated using the RGB and thermal images to capture detailed information in the prediction map and then embed the prior edge information in the feature maps. To effectively fuse the RGB and thermal information, we propose a multimodal fusion module that guarantees adequate cross-modal fusion. Considering the importance of high-level semantic information, we propose a global information module and a semantic information module to extract rich semantic information from the high-level features. For decoding, we use simple elementwise addition for cascaded feature fusion. Finally, to improve the parsing accuracy, we apply multitask deep supervision to the semantic and boundary maps. Extensive experiments were performed on benchmark datasets to demonstrate the effectiveness of the proposed EGFNet and its superior performance compared with state-of-the-art methods. The code and results can be found at https://github.com/ShaohuaDong2021/EGFNet.
Realized GARCH, CBOE VIX, and the Volatility Risk Premium

We show that the Realized GARCH model yields close-form expression for both the Volatility Index (VIX) and the volatility risk premium (VRP). The Realized GARCH model is driven by two shocks, a return shock and a volatility shock, and these are natural state variables in the stochastic discount factor (SDF). The volatility shock endows the exponentially affine SDF with a compensation for volatility risk. This leads to dissimilar dynamic properties under the physical and risk-neutral measures that can explain time-variation in the VRP. In an empirical application with the S&P 500 returns, the VIX, and the VRP, we find that the Realized GARCH model significantly outperforms conventional GARCH models.
Boosting Active Learning via Improving Test Performance

Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless, it remains unclear how selected data impacts the test performance of the task model used in AL. In this work, we explore such an impact by theoretically proving that selecting unlabeled data of higher gradient norm leads to a lower upper-bound of test loss, resulting in a better test performance. However, due to the lack of label information, directly computing gradient norm for unlabeled data is infeasible. To address this challenge, we propose two schemes, namely expected-gradnorm and entropy-gradnorm. The former computes the gradient norm by constructing an expected empirical loss while the latter constructs an unsupervised loss with entropy. Furthermore, we integrate the two schemes in a universal AL framework. We evaluate our method on classical image classification and semantic segmentation tasks. To demonstrate its competency in domain applications and its robustness to noise, we also validate our method on a cellular imaging analysis task, namely cryo-Electron Tomography subtomogram classification. Results demonstrate that our method achieves superior performance against the state of the art. We refer readers to https://arxiv.org/pdf/2112.05683. pdf for the full version of this paper which includes the appendix and source code link.
Self-attention Does Not Need $O(n^2)$ Memory

We present a very simple algorithm for attention that requires $O(1)$ memory with respect to sequence length and an extension to self-attention that requires $O(\log n)$ memory. This is in contrast with the frequently stated belief that self-attention requires $O(n^2)$ memory. While the time complexity is still $O(n^2)$, device memory rather than compute capability is often the limiting factor on modern accelerators. Thus, reducing the memory requirements of attention allows processing of longer sequences than might otherwise be feasible. We provide a practical implementation for accelerators that requires $O(\sqrt{n})$ memory, is numerically stable, and is within a few percent of the runtime of the standard implementation of attention. We also demonstrate how to differentiate the function while remaining memory-efficient. For sequence length 16384, the memory overhead of self-attention is reduced by 59X for inference and by 32X for differentiation.