Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Chicken Feather Fiber (CFF) a Livestock Waste in Composite Material Development

Chicken feather fiber (CFF) that is a clear livestock waste is been used currently in various applications like textiles, crafting, decorations and even in biocomposite manufacturing. The reason for it is the abundance availability and compatible characteristics of the CFF. Here the reviews by various literature sited has been piled up to enhance the awareness of the researchers in the area of using CFF as a reinforcing material in developing various forms of composites. Properties like physical, chemical, mechanical, thermal, acoustical, morphological etc. that are been characterized by different researchers have been put together for the advancement of material development technology. In the past Importance is being given by most of the researchers to the various natural plant fibers. So for the change and the sustainable development the reviews are been formatted.
Multi-criteria design optimization of control system instrumentation using Principal Component Analysis (PCA) and structural modeling approaches

This article presents general approach of multi-criteria design of the control system instrumentation. The work reported here aims at defining that principal component analysis PCA can be used as method of design phase for non-linear system based on data measurements from the sensors and the available actuators for dynamical control system. The PCA consists to select inputs variables for quantifying the speed vt+δt using structural modeling, despite the environmental disturbance is the slope of the road and uncertainties in measurements from the sensors and actuators implemented in the control system instrumentation in design phase.
Polpa cítrica desidratada na dieta de borregos Suffolk e Santa Inês em confinamento

Suffolk (12) and Santa Ines (12) lambs averaging 80 to 90 days of age fed with corn silage and concentrate mixture (20% CP) with increasing levels of dehydrated citric pulp replacing maize (0, 36, 64, 100%) were utilized to evaluate performance and apparent digestibility coefficient of the diet components. The increasing levels of citric pulp did not modify weight gain, feed intake, feed conversion and the DM, OM, CP and NNE apparent digestibility coefficients, increased CF, NDF and ADF and decreased EE apparent digestibility (P<0.05). The Suffolk lambs had higher daily live-weight gain (P<0.01) than the Santa Ines. It was concluded that dehydrated citric pulp can replace maize on growing lambs diet.
Model reference adaptive control of a haptic feedback device for improving force performance

In this paper, a new adaptive control algorithm of a haptic feedback device is analyzed. Forces applied to the haptic device through human hand movements are modeled as disturbances and compensated in the force control action. A model reference adaptive control (MRAC) scheme is proposed to improve force tracking performance. A separate reference model for every DOF is selected to satisfy rising time, settling time, peak time, and overshoot requirements. General adaptive control laws are developed for tuning gains in the control transfer functions based on the reference model and the force sensor and encoder readings in real time. These control gains cover force tracking performance and compensate human hand disturbances while providing robustness to sensor noise. Stability of the control system is shown analytically. Convergence and boundedness of control gains are also shown through experiments.
The Dynamics Of User Innovation: Drivers And Impediments Of Innovation Activities

AbstractThe following sections are included:IntroductionLiterature ReviewOverview of the Moth ClassEmpirical Findings on User Innovation ActivityCharacteristics of user innovatorsSailing tinkerers and tinkering sailorsKnowledge-sharingDifferences in community cultureThe Dynamics of the Innovative Activity of UsersRevolution and adoptionThe dynamics of user innovation in the Moth classTechnology complexityTechnological maturityMarket structureCustomer satisfactionBarriers to innovationFactors driving the development of user innovation across timeSummary and Discussion of FindingsSummary of resultsGenerality of findingsImplications for managerial practiceReferences
Development of Electronic Ground System Equipment for the ASIM Project

ASIM (Atmosphere Space Interaction Monitor) is an instrument designed to monitor X-ray and gamma-ray bursts of terrestrial origin. ASIM is divided into two parts; Modular X-ray and Gamma-ray Sensor detect photons in the 15 keV to 20 MeV range, and Modular Multi-spectral Imaging Array takes images in the visible range. It will be mounted on the International Space Station (ISS) where it will be observing the earth’s atmosphere. The instrument is composed of two detector layers, each consisting of four identical units. The outermost layer is a low energy detector detecting photons ranging from 15 keV to 400 keV. This layer is made out of Cadmium Zinc Telluride (CZT) crystals and structured as a pixlated array. Each unit consists of 4096 pixels, giving the MXGS a total of 16384 pixels. The innermost layer is a high energy detector detecting photons ranging from 200 keV to 20 MeV. This layer is made out of Bismuth Germanate Oxide (BGO) crystals, and each unit consists of 3 BGO crystals and 3 photomultiplying tubes. University of Bergen (UoB) is responsible for the development of both detector layers. The project is entering the final stage where the detector units are tested. Some test equipment and computer software is required to carry out these tests. This thesis describes the development of tools needed to test the detectors. A primary objective of this work was to develop the equipment with scalability and reusability in mind. Several parts of the equipment can be adapted for later projects, thus reducing the time required to develop future testing equipment. The test equipment is divided into two parts, and a computer program commands, monitors and gathers data from the detectors. All 4096 pixels of a CZT detector can be controlled individually by using a graphical user interface or through a script. The second part of the equipment is a physical interface, allowing a computer to communicate with the detectors. This is realized with a commercial FPGA based interface and with an embedded microcontroller supplying USB protocol. The primary objective is to convert from USB signals to the xlink protocol utilized by the detectors, but it also includes a buffer for temporary data storage. The system has been subject of thorough testing, including both validation and verification tests and performance tests. It controls, monitors and acquires data from up to four detectors simultaneously. A working proof of concept existed a few weeks after project start, and the system has been used for several months.
Smad3 Signatures in Renal Inflammation and Fibrosis

Renal inflammation and fibrosis are key pathological features of acute kidney injury (AKI) and chronic kidney disease (CKD). Smad3 is a critical mediator of TGF-β signaling and plays a pathogenic role in both renal inflammation and fibrosis. Smad3 can be activated not only by TGF-β1 but also by many stress molecules including angiotensin II (Ang II), advanced end products (AGEs), and C-reactive protein (CRP) under disease conditions. In addition, Smad3 can interact with other signaling pathways, such as the ERK/p38 MAPK and NF-κB pathways, to mediate renal inflammation and fibrosis. Mechanistically, Smad3 transcriptionally regulates many downstream target genes including microRNAs and long non-coding RNAs to cause cell death, inflammation, and fibrosis. Thus, targeting Smad3 or its downstream genes specifically related to renal inflammation and fibrosis should provide a novel therapeutic strategy to combat kidney diseases.
Formulations of the max k-cut problem on classical and quantum computers

Recent claims on “solving” combinatorial optimization problems via quantum computers have attracted researchers to work on quantum algorithms. The max k-cut problem is a challenging combinatorial optimization problem with multiple notorious mixed integer linear optimization formulations. In this paper, we revisit the binary quadratic optimization formulation of Carlson and Nemhauser (Operations Research, 1966) and provide theoretical and computational comparisons between different mixed integer optimization formulations of the max k-cut problem. While no claim on “quantum advantage” is provided, we introduce quadratic unconstrained binary optimization (QUBO) formulations with tight penalty coefficients. For at least 60% of test instances, the computational experiments show the superiority of the tight models over näıve ones in terms of the quality of feasible solutions in the quantum context. We also design quantum circuits to find feasible solutions using a quantum approximate optimization algorithm (QAOA). A preprocessing procedure is proposed to make instances manageable for quantum computers. In comparison to the existing fixing procedures, our proposed preprocessing algorithm reduces the number of vertices and edges of some existing benchmark instances up to 30% and 25%, respectively. Finally, we conduct an extensive set of experiments on classical and quantum-inspired formulations. Our codes and data are available on GitHub.
Resiliency of Nonlinear Control Systems to Stealthy Sensor Attacks

In this work, we focus on analyzing vulnerability of nonlinear dynamical control systems to stealthy sensor attacks. We define the notion of stealthy attacks in the most general form by leveraging Neyman-Pearson lemma. Specifically, an attack is considered to be stealthy if it is stealthy from (i.e., undetected by) any intrusion detector – i.e., the probability of the detection is not better than a random guess. We then provide a sufficient condition under which a nonlinear control system is vulnerable to stealthy attacks, in terms of moving the system to an unsafe region due to the attacks. In particular, we show that if the closed-loop system is incrementally exponentially stable while the open-loop plant is incrementally unstable, then the system is vulnerable to stealthy yet impactful attacks on sensors. Finally, we illustrate our results on a case study.
Finiteness properties of some groups of piecewise projective homeomorphisms

The Lodha-Moore group G ﬁrst arose as a ﬁnitely presented counterexample to von Neumann’s conjecture. The group G acts on the unit interval via piecewise projective homemorphisms. A result of Lodha shows that G in fact has type F 8 . Here we will describe G as a group that is “locally determined” by an inverse semigroup S 2 , in the sense of the author’s joint work with Hughes. The semigroup S 2 is generated by three linear fractional transformations A , B , and C 2 , where A and B are elliptical transformations of the hyperbolic plane and C 2 is a hyperbolic translation. Following a general procedure delineated by Farley and Hughes, we oﬀer a new proof that G has type F 8 . Our proof simultaneously shows that various groups acting on the line, the circle, and the Cantor set have type F 8 . We also prove analogous results for the groups that are locally determined by an inverse semigroup S 3 , which shares the generators A and B with S 2 , but replaces C 2 with a diﬀerent hyperbolic translation C 3 .
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents

Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control and optimization, and reinforcement learning. Motivated by this line of work, we consider a multi-agent min-max learning problem, and focus on the emerging challenge of contending with worst-case Byzantine adversarial agents in such a setup. By drawing on recent results from robust statistics, we design a robust distributed variant of the extragradient algorithm - a popular algorithmic approach for min-max optimization. Our main contribution is to provide a crisp analysis of the proposed robust extra-gradient algorithm for smooth convex-concave and smooth strongly convex-strongly concave functions. Specifically, we establish statistical rates of convergence to approximate saddle points. Our rates are near-optimal, and reveal both the effect of adversarial corruption and the benefit of collaboration among the non-faulty agents. Notably, this is the first paper to provide formal theoretical guarantees for large-scale distributed min-max learning in the presence of adversarial agents.
Constraints on TESS albedos for five hot Jupiters

Photometric observations of occultations of transiting exoplanets can place important constraints on the thermal emission and albedos of their atmospheres. We analyse photometric measurements and derive geometric albedo (Ag) constraints for five hot Jupiters observed with TESS in the optical: WASP-18 b, WASP-36 b, WASP-43 b, WASP-50 b and WASP-51 b. For WASP-43 b, our results are complemented by a VLT/HAWK-I observation in the near-infrared at 2.09 μm. We derive the first geometric albedo constraints for WASP-50 b and WASP-51 b: Ag < 0.445 and Ag < 0.368, respectively. We find that WASP-43 b and WASP-18 b are both consistent with low geometric albedos (Ag < 0.16) even though they lie at opposite ends of the hot Jupiter temperature range with equilibrium temperatures of ∼1400 K and ∼2500 K, respectively. We report self-consistent atmospheric models which explain broadband observations for both planets from TESS, HST, Spitzer and VLT/HAWK-I. We find that the data of both hot Jupiters can be explained by thermal emission alone and inefficient day-night energy redistribution. The data do not require optical scattering from clouds/hazes, consistent with the low geometric albedos observed.
Forecasting lifetime and performance of a novel NVM last-level cache with compression

Non-volatile memory (NVM) technologies are interesting alternatives for building the on-chip Last-Level Cache (LLC). Their advantages, compared to SRAM memory, are higher density and lower static power, but each write operation slightly wears out the bitcell, to the point of losing its storage capacity. In this context, this paper proposes a novel NV-LLC organization leveraging data compression. Data compression reduces the size of the blocks and, together with wear-leveling mechanisms, can defer the degradation of such a NV-LLC. Moreover, as capacity is reduced by write wear, data compression enables degraded cache frames to allocate blocks whose compressed size is adequate. From a methodological point of view, although different approaches are used in the literature to analyze the degradation of a NV-LLC, none of them allows to study in detail its temporal evolution. In this sense, this work proposes a forecasting procedure that combines detailed simulation and prediction, enabling an accurate analysis of different cache content mechanisms (replacement, wear leveling, compression, etc.) on the temporal evolution of the indices of interest, such as the effective capacity of the NV-LLC or the system IPC. The proposed NV-LLC organization has a small added cost compared to that of a baseline NV-LLC without compression in terms of area, latency and energy consumption, and increases more than 6 times the time a NV-LLC takes to reach 50% effective capacity.
Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories

We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a series of instructions, to a sequence of movements to be executed by a robot. In the first stage, we perceive and preprocess the given inputs, isolating individual commands from the complete instruction provided by a human user. In the second stage we encode the individual commands into a multimodal latent space, employing a deep generative model. Finally, in the third stage we convert the latent samples into individual trajectories and combine them into a single dynamic movement primitive, allowing its execution by a robotic manipulator. We evaluate our pipeline in the context of a novel robotic handwriting task, where the robot receives as input a word through different perceptual modalities (e.g., image, sound), and generates the corresponding motion trajectory to write it, creating coherent and high-quality handwritten words.
Advancing Data Justice Research and Practice: An Integrated Literature Review

The Advancing Data Justice Research and Practice (ADJRP) project aims to widen the lens of current thinking around data justice and to provide actionable resources that will help policymakers, practitioners, and impacted communities gain a broader understanding of what equitable, freedom-promoting, and rights-sustaining data collection, governance, and use should look like in increasingly dynamic and global data innovation ecosystems. In this integrated literature review we hope to lay the conceptual groundwork needed to support this aspiration. The introduction motivates the broadening of data justice that is undertaken by the literature review which follows. First, we address how certain limitations of the current study of data justice drive the need for a re-location of data justice research and practice. We map out the strengths and shortcomings of the contemporary state of the art and then elaborate on the challenges faced by our own effort to broaden the data justice perspective in the decolonial context. The body of the literature review covers seven thematic areas. For each theme, the ADJRP team has systematically collected and analysed key texts in order to tell the critical empirical story of how existing social structures and power dynamics present challenges to data justice and related justice fields. In each case, this critical empirical story is also supplemented by the transformational story of how activists, policymakers, and academics are challenging longstanding structures of inequity to advance social justice in data innovation ecosystems and adjacent areas of technological practice.
Density functional approach to quark matter with confinement and color superconductivity

We present a novel relativistic density-functional approach to modeling quark matter with a mechanism to mimic conﬁnement. The quasiparticle treatment of quarks provides their suppression due to a large quark selfenergy already at the mean-ﬁeld level. We demonstrate that our approach is equivalent to a chiral quark model with medium-dependent couplings. The dynamical restoration of the chiral symmetry is ensured by construction of the density functional. Supplemented with the vector repulsion and diquark pairing the model is applied to construct a hybrid quark-hadron EoS of cold compact-star matter. We study the connection of such a hybrid EoS with the stellar mass-radius relation and tidal deformability. The model results are compared to various observational constraints including the NICER radius measurement of PSR J0740 + 6620 and the tidal deformability constraint from GW170817. The model is shown to be consistent with the constraints, still allowing for further improvement by adjusting the vector repulsion and diquark pairing couplings.
Low complexity joint position and channel estimation at millimeter wave based on multidimensional orthogonal matching pursuit

Compressive approaches provide a means of obtaining high resolution channel estimates in millimeter wave MIMO systems, despite the use of analog and hybrid architectures. Such estimates can also be used as part of a joint channel estimation and localization solution. Achieving good localization performance, though, requires high resolution channel estimates and better methods to exploit those channels. In this paper, we propose a low complexity multidimensional orthogonal matching pursuit strategy for compressive channel estimation based by operating with a product of independent dictionaries for the angular and delay domains, instead of a global large dictionary. This leads to higher quality channel estimates but with lower complexity than generalizations of conventional solutions. We couple this new algorithm with a novel localization formulation that does not rely on the absolute time of arrival of the LoS path and exploits the structure of reflections in indoor channels. We show how the new approach is able to operate in realistic 3D scenarios to estimate the communication channel and locate devices in an indoor simulation setting.
Nonlinear and ultrafast all-dielectric metasurfaces at the center for integrated nanotechnologies

Metasurfaces control optical wavefronts via arrays of nanoscale resonators laid out across a surface. When combined with III–V semiconductors with strong optical nonlinearities, a variety of nonlinear effects such as harmonic generation and all-optical modulation can be enabled and enhanced at the nanoscale. This review presents our research on engineering and boosting nonlinear effects in ultrafast and nonlinear semiconductor metasurfaces fabricated at the Center for Integrated Nanotechnologies. We cover our recent works on parametric generation of harmonic light via direct and cascaded processes in GaAs-metasurfaces using Mie-like optical resonances or symmetric-protected bound state in the continuum, and then describe the recent advances on harmonic generation in all-dielectric metasurfaces coupled to intersubband transitions in III–V semiconductor heterostructures. The review concludes on the potential of metasurfaces to serve as the next platform for on-chip quantum light generation.
Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning

The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward function via inverse reinforcement learning (IRL) followed by maximizing this reward function via reinforcement learning (RL). The policies learned via these approaches are however very brittle in practice and deteriorate quickly even with small test-time perturbations due to compounding errors. We propose Imitation with Planning at Test-time (IMPLANT), a new meta-algorithm for imitation learning that utilizes decision-time planning to correct for compounding errors of any base imitation policy. In contrast to existing approaches, we retain both the imitation policy and the rewards model at decision-time, thereby benefiting from the learning signal of the two components. Empirically, we demonstrate that IMPLANT significantly outperforms benchmark imitation learning approaches on standard control environments and excels at zero-shot generalization when subject to challenging perturbations in test-time dynamics.
Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients

Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose model is still a classifier for predicting class labels, if the class-prior probabilities are shifted while the class-conditional distributions are shared among the unlabeled data owned by the clients. We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model. FedUL is a very general solution to unsupervised FL: it is compatible with many supervised FL methods, and the recovery of the wanted model can be theoretically guaranteed as if the data have been labeled. Experiments on benchmark and real-world datasets demonstrate the effectiveness of FedUL. Code is available at https://github.com/lunanbit/FedUL.