Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-sensitive Tensor-based Dual-stage Attention LSTM with Feature Selection for Data Center Server Power Forecasting

Power forecasting has a guiding effect on power-aware scheduling strategies to reduce unnecessary power consumption in data centers. Many metrics related to power consumption can be collected in physical servers, such as the status of CPU, memory, and other components. However, most existing methods empirically exploit a small number of metrics to forecast power consumption. To this end, this article uses feature selection based on causality to explore the metrics that strongly influence the power consumption of different tasks. Moreover, we propose a tensor-based dual-stage attention LSTM to forecast the non-linear and non-periodic power consumption. In the proposed model, a multi-way delay embedding transform is utilized to convert the time series into tensors along the temporal direction. The LSTM combines with the tensor technique and the attention mechanism to capture the temporal pattern effectively. In addition, we adopt the cost-sensitive loss function to optimize the specific power forecasting problem in data centers. The experimental results demonstrate that our method can achieve up to 1.4% to 4.3% forecasting accuracy improvement compared with the state-of-the-art models.
Joint design of beamforming and trajectory for integrated sensing and communication drone networks

Drone-aided network has drawn a significant attention due to the high flexibility and reliability it can bring to the wireless communications. In this paper, we design an integrated sensing and communication (ISAC) scheme for a drone-aided network to achieve mutual benefit of communication and sensing, where the drone detects several ground targets while communicating with a large number of ground users during the flight. We jointly optimize the beamforming and trajectory of the drone for achieving high beam gain subject to the signal to interference plus noise ratio (SINR), the transmit power and the flight speed constraints. To solve the formulated multivariate non-convex optimization problem, we propose an alternating optimization algorithm based on the Riemannian conjugate gradient (RCG). Numerical results show that the proposed method can effectively improve the sensing performance while ensuring the communication quality, and has a low computational complexity.
Towards improving Disfluency Detection from Speech using Shifted Delta Cepstral Coefficients

In this paper, we investigate the use of shifted delta cepstral (SDC) coefficients for detecting disfluencies in two types of speech - stuttered speech and spontaneous lecture-mode speech. SDC features capture temporal variations in the speech signal effectively across several frames. The UCLASS stuttered speech dataset and IIITH-IED dataset are used here to develop frame-level automatic disfluency detection systems for four types of disfluencies and the effect of SDC features on the detection of each disfluency type is examined using MFCC and SFFCC cepstral representations. Overall, it is found that using MFCC+SDC features gives an absolute improvement of 2.98% and 6.02% for stuttered and spontaneous speech disfluency detection respectively over the static MFCC features, while SFFCC+SDC features give an absolute improvement of 4.62% for stutter disfluencies and 7.28% for spontaneous speech disfluencies over the static SFFCC features, showing the importance of considering temporal variations for disfluency detection.
TCP-INT: lightweight network telemetry with TCP transport

In-band Network Telemetry (INT) provides visibility into the state of the network and can be used for monitoring and debugging. However, existing implementations do not make telemetry available to end-hosts. In this demonstration we present TCP-INT, which delivers network telemetry directly to the hosts and show the advantages of correlating end-host state with the state of the network fabric.
CiraaS: cloud computing with programmable logic

Cloud computing reduces provider and user costs by multiplexing workloads. Advantages of cloud computing include high utilization by temporal and spatial sharing computing resources and a subscription model that charges only for the resources and time used[1]. As recent cloud computing has evolved to maximize these advantages, microservices [11], function-as-a-service (FaaS) [10], and other more granular and short-lived cloud services have emerged. Today's FaaS are oriented towards fast provisioning, fine-grained billing times, tight memory constraints, stateless processing, and real-time processing [15]. However, since the context switching in CPUs is the bottleneck [8], their processing demands are no longer satisfactorily met. Therefore, a shift is expected toward a more efficient system architecture for cloud computing [10].
Data Analysis of Agriculture using Data Mining Techniques

Data mining is a method of uncovering hidden patterns in vast, complex databases. It's crucial while dealing with complex agricultural challenges. To detect trends in the influence of a range of factors on crop yield, data visualization is necessary. The planned prediction system is critical in addressing India's food security challenges, as well as advising government agencies on how to deal with over-or under-production situations. In nonlinear complicated settings, deep learning techniques are crucial for properly predicting the agricultural output. The state-of-the-art pattern recognition of time-sequence data has been achieved using the recurrent Neural Network (RNN) technique. Long Short–Term Memory is the most extensively utilized strategy in RNN models (LSTM). In order to forecast crop production in India, the suggested approach is used to extract information from previous agricultural data collections. Python Jupiter is used to carrying out the simulation. The RMSE, MAE, and correlation coefficient are the performance measures employed. Long Short–Term Memory (LSTM), Agriculture Data Mining, and Deep Learning are all used to analyze the data.
The Potentials of Dynamic Platform Modelling in Public Administration

The objective of this paper is to show how system dynamics modelling can be applied to simulate the behavior of information communication technology based platforms to formulate and validate strategies or development policies of these platforms in the public sector. We show three typical yet very different public administration platforms; the Hungarian clientgate, an Italian Government-As-A-Platform example, and a Finnish mobility-as-a-service concept as illustrations. We then develop a general dynamic model based on classic two-sided market economics and extended management models, and theoretically show how cross-group and within-group externalities determine the behavior of platforms. Through the introduction of a conceptual system dynamics model we finally show how the behavior of public administration platforms can be simulated and what type of conclusions can be drawn from these models. Further research and practical implications are also shown by potential developments and refinements.
Effect of Digitalisation on Business Performance of Companies in the Manufacturing Industry in Hungary

The manufacturing industry in Hungary is responsible for 24,3% of the GDP. In overall Hungarian companies’ labour productivity reaches only 70% of the EU average. This study shows that digitalisation may contribute to higher labour productivity. In this study a new index to measure digital business process integration (DBII) is introduced based on Porter's Value Chain model. Historic data from a questionnaire is used to categorize companies based on DBII. The association between DBII, organization IT capabilities and business performance indicators (labour productivity and profit per employee) is analysed with the help of cross tabulation and SPSS. It is also shown that higher DBII can be associated with higher labour productivity. Positive association between organization IT capabilities and labour productivity is also presented. A weaker but significant association between DBII and profit per employee suggests, that organizations still need to work out ways how digitalisation can help to harvest the gains from increased labour productivity.
The Composite Structure of Hologram and Optical Waveguide

Optical holography is an excellent technology which can be applied in many fields, such as 3D-displays, information storage, products packaging, interferometer survey, optical connection and computer and so on. The conventional holography include reference wave, object wave and diffracting wave. These optical waves are spatial light and the whole hologram set up is fair size. The optical waveguides which are set up in optical routs can be integrated into a mini-chip. A new class of holography is proposed by many researchers [Suhara, et al., 1976; Putilin, et al., 1991; Singher & Shamir, 1994], in which the evanescent part of a guided wave or the guided wave in a waveguide is utilized as the reference wave or the illuminating one. This kind of hologram is defined as “waveguide hologram”, and it has significant advantages in integration, wide field of view, compactness and other special functions. In this chapter, three sections are used to introduce the new development of waveguide hologram in the optical memory, optical connection and see-through planar displays fields.
Explicable Policy Search

Human teammates often form conscious and subconscious expectations of each other during interaction. Teaming success is contingent on whether such expectations can be met. Similarly, for an intelligent agent to operate beside a human, it must consider the human’s expectation of its behavior. Disregarding such expectations can lead to the loss of trust and degraded team performance. A key challenge here is that the human’s expectation may not align with the agent’s optimal behavior, e.g., due to the human’s partial or inaccurate understanding of the task domain. Prior work on explicable planning described the ability of agents to respect their human teammate’s expectations by trading off task performance for more expected or “ explicable ” behaviors. In this paper, we introduce Explicable Policy Search (EPS) to significantly extend such an ability to stochastic domains in a reinforcement learning (RL) setting with continuous state and action spaces. Furthermore, in contrast to the traditional RL methods, EPS must at the same time infer the human’s hidden expectations. Such inferences require information about the human’s belief about the domain dynamics and her reward model but directly querying them is impractical. We demonstrate that such information can be necessarily and sufficiently encoded by a surrogate reward function for EPS, which can be learned based on the human’s feedback on the agent’s behavior. The surrogate reward function is then used to reshape the agent’s reward function, which is shown to be equivalent to searching for an explicable policy. We evaluate EPS in a set of navigation domains with synthetic human models and in an autonomous driving domain with a user study. The results suggest that our method can generate explicable behaviors that reconcile task performance with human expectations intelligently and has real-world relevance in human-agent teaming domains.
CEG_A_293272 333..342

1Division of Gastroenterology and Hepatology, Stanford University School of Medicine, Stanford, CA, USA; 2Stanford University School of Medicine, Stanford, CA, USA; 3Immunology Program, Stanford University School of Medicine, Stanford, CA, USA Abstract: Leukocyte trafficking to the gastrointestinal tract is recognized to play a role in the pathogenesis of inflammatory bowel disease (IBD). Integrins are expressed on immune cells and interact with cell adhesion molecules (CAM) to mediate leukocyte trafficking. Blockade of the gut-tropic integrin α4β7 and its subunits has been exploited as a therapeutic target in IBD. Natalizumab (anti-α4) is approved for moderate to severe Crohn’s disease (CD), but its use is limited due to potential risk of progressive multifocal leukoencephalopathy. Vedolizumab (anti-α4β7) is approved for the treatment of ulcerative colitis (UC) and CD. It is the most widely used anti-integrin therapy in IBD and has been shown to be effective in both induction and maintenance therapy, with a favorable safety profile. Several models incorporating clinical, genetic, immune, gut microbial, and vitamin D markers to predict response to vedolizumab in IBD have been developed. Etrolizumab (anti-β7) blocks leukocyte trafficking via α4β7 and cell adhesion via αEβ7 integrins. Large phase 3 clinical trials evaluating efficacy of etrolizumab in the induction and maintenance of patients with IBD are underway. Other investigational anti-integrin therapies include abrilumab (anti-α4β7 IgG2), PN-943 (orally administered and gut-restricted α4β7 antagonist peptide), AJM300 (orally active small molecule inhibitor of α4), and ontamalimab (anti-MAdCAM-1 IgG).
Characterization of Obtuse Triangular Defects on 4H-SiC 4° off-Axis Epitaxial Wafers

We investigate the triangular defects with different structural features on 4H-SiC epilayers by a Nomarski microscope, a Candela optical surface analyzer and ultraviolet photoluminescence (UV-PL) imaging. Both the foreign particles and the substrate scratches can cause the formation of the obtuse triangular defects. The central area of some obtuse triangular defects can have the spatially confined core, in which the in-grown stacking faults can be observed under the UV-PL imaging. In contrast, the obtuse triangular defects induced by the scratches appear in the form of band-like defects, of which the width depends on the scratch direction and reaches the maximum when the scratch direction is parallel to the step flow direction. The formation mechanisms of these obtuse triangular defects are discussed.
DISENTANGLED GENERATIVE MODELS AND THEIR APPLICATIONS

OF THE DISSERTATION Disentangled Generative Models and their Applications By Bingchen Liu Dissertation Director: Ahmed Elgammal Generative models, such as Auto-Encoders, Generative Adversarial Networks, Generative Flows, and Diffusion Models, are fascinating for their ability to synthesis versatile visual and audio information from merely noise. This generative process usually requires a model to perceive and compress high-dimensional data into a compact, low-dimensional latent space, where each dimension encodes some valuable semantic variations in the original data space. How much we know about the latent space is vital because it determines how we can take advantage of the corresponding generative model. Imagine a GAN trained on human faces. If we know which dimension in its latent vector controls the concept “hair shape”, we can synthesize multiple images for the same face to try different hairstyles without changing other facial attributes. Disentangling generative models makes them more fun to play with, which is the topic of this thesis. This thesis studies the unsupervised disentangling of the latent space in GANs focused on the image domain and extended to multi-modalities (image captioning and text-to-image synthesis). The proposed methods in this thesis enable the GAN model to disentangle its latent space automatically, thus sparing the expensive effort of collecting semantic labeling for the training data. Derived from disentanglement, this thesis also
Stochastic convex optimization from a continuous dynamical system perspective

Résumé – Afin de résoudre des problèmes d’optimisation non contraints avec des objectives différentiables et convexes, et où le gradient est soumis à des erreurs, nous analysons le comportement des flots de type gradient sous des perturbations stochastiques. Plus précisément, nous étudions une équation différentielle stochastique où le terme de dérivé est l’opposé du gradient de la fonction objective, et le terme de diffusion est borné ou carré-intégrable. Dans ce contexte, sous des conditions de Lipschitz continuité du gradient, en plus d’assurer l’existence standard et l’unicité d’une solution, un premier résultat principal montre la convergence presque sûre de l’objectif et de la solution/processus vers un minimiseur. Nous menons ensuite une étude de complexité et établissons des taux de convergence ponctuels et ergodiques en espérance lorsque l’objective est convexe, fortement convexe ou vérifie (localement) l’inégalité de Polyak-Łojasiewicz. Cette dernière, qui implique une analyse locale, nécessite des arguments fins en théorie de la mesure. Abstract – In order to solve differentiable and convex unconstrained optimization problems with a noisy (inexact) gradient input, we analyze the behavior of gradient-like flows under stochastic errors. More precisely, we study a stochastic differential equation where the drift term is minus the gradient of our objective function and the diffusion term is bounded or square-integrable. In this context, under Lipschitz continuity of the gradient, beside ensuring standard existence and uniqueness of a solution, our first main result shows almost sure convergence of the objective and the solution/process to a minimizer of the objective function. We also provide a comprehensive complexity analysis by establishing several new pointwise and ergodic convergence rates in expectation for the convex, strongly convex and local Polyak-Łojasiewicz case. The latter, which involves a local analysis, is very challenging and necessitates non-trivial arguments from measure theory.
Estimating entanglement in a class of N-qudit states

The logarithmic derivative (or, quantum score) of a positive definite density matrix appearing in the quantum Fisher information is discussed, and its exact expression is presented. Then, the problem of estimating the parameters in a class of the Werner-type N -qudit states is studied in the context of the quantum Cramér-Rao inequality. The largest value of the lower bound to the error of estimate by the quantum Fisher information is shown to coincide with the separability point only in the case of two qubits. It is found, on the other hand, that such largest values give rise to the universal fidelity that is independent of the system size.
Rate-Integrating Gyroscope Operation in The Non-Linear Regime

In Coriolis vibratory Rate-Integrating Gyroscopes (RIG), a higher electrical Signal-to-Noise Ratio (SNR) provides an improved resolution for measuring the orientation of the oscillation pattern. Therefore, precession due to a smaller angular rotation can be detected by maximizing the vibration amplitude. On the other hand, the contribution of non-linear mechanisms to the resonator dynamics, the measurements, and the control grows in complexity with the oscillation amplitude. This paper studies the trade-offs between achieving a higher SNR through increasing the oscillation amplitude and the adverse effects of non-linear mechanisms in RIG. As part of our study, we derived analytical equations to demonstrate that by operating a RIG in the non-linear regime, electrostatically-induced amplitude-frequency coupling results in an angle-dependent anisoelasticity. Anisoelasticity was shown to reduce angular gain and introduce angle-dependent biases in measurements despite utilizing a quadrature control for suppressing ellipticity. We proposed a modified Whole Angle (WA) control, which utilizes a non-linear feedback loop to compensate for the amplitude-frequency coupling along the X and Y axes. By implementing the control with a Micro-Electro-Mechanical System (MEMS) Dual Foucault Pendulum (DFP) gyroscope, we were able to operate the RIG with a vibration amplitude equal to 15% of the capacitive gap size and reduce ellipticity by 100 times. The magnitude of control voltages for compensation of nonlinearity was shown to depend on the biasing voltages, which were intentionally applied for mode-matching. Based on our observations, we conjectured that impractically large control voltages coupled with phase drifts in electronics degrade the stability of RIG when operated in the non-linear regime. Our results highlight the limitations of feedback compensation methods in RIG and the need for fabrication of structurally symmetric micro-gyroscopes for high-resolution and high-accuracy angle measurements.
GPPS­TC­2022­0038 Modelling the Influence of Roughness Skewness on Wall Bounded Flows by Systematic Roughness Variation

In recent years, the research on roughness has focused on various roughness features, rather than the roughness height only, to improve the understanding of roughness effects on wall­bounded flows. A special focus is placed on the skewness of the roughness height profile. The skewness measures whether the height profile is dominated by negative or positive roughness elements. Surfaces with both features can be found on worn blades: On the leading edge, roughness is caused by the impact of particles resulting in a negative skewness. Rough surfaces around the trailing edge, however, develop due to depositions leading to a positive skewness. In this paper, rough surfaces taken from a compressor blade of an aero­engine are systematically varied to investigate the isolated effect of skewness on aerodynamic losses. By direct numerical simulations of a periodic flow channel. The results show that the skewness has a major influence on loss generation. Based on these results, an existing model which essentially uses the shape­and­density parameter is extended by a skewness factor. The modified correlation predicts the influence of the rough surfaces investigated well.
Exploring User Reactions and Mental Models Towards Perceptual Manipulation Attacks in Mixed Reality

Perceptual Manipulation Attacks (PMA) involve manipulating users’ multi-sensory (e.g., visual, auditory, haptic) perceptions of the world through Mixed Reality (MR) content, in order to influence users’ judgments and following actions. For example, a MR driving application that is expected to show safety-critical output might also (maliciously or unintentionally) overlay the wrong signal on a traffic sign, misleading the user into slamming on the brake. While current MR technology is sufficient to create such attacks, little research has been done to understand how users perceive, react to, and defend against such potential manipulations. To provide a foundation for understanding and addressing PMA in MR, we conducted an in-person study with 21 participants. We developed three PMA in which we focused on attacking three different perceptions: visual, auditory, and situational awareness. Our study first investigates how user reactions are affected by evaluating their performance on “microbenchmark” tasks under benchmark and different attack conditions. We observe both primary and secondary impacts from attacks, later impacting participants’ performance even under non-attack conditions. We follow up with interviews, surfacing a range of user reactions and interpretations of PMA. Through qualitative data analysis of our observations and interviews, we identify various defensive strategies participants developed, and we observe how these strategies sometimes backfire. We derive recommendations for future investigation and defensive directions based on our findings.