Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemical Behavior of Reactively Sputtered Iron‐Doped Nickel Oxide

Iron-doped nickel oxide films were deposited by reactive sputtering from elemental and alloy targets in a 20% oxygen/argon atmosphere and were characterized for use as oxygen evolution catalysts. The incorporation of iron reduced the overpotential required for oxygen evolution by as much as 300 mV at a current density of 100 mA/cm 2 compared to undoped nickel oxide deposited under similar conditions. Tafel slopes were reduced from 95 mV/dec in undoped NiO x films to less than 40 mV/dec for films containing 1.6 to 5.6 mole percent (m/o) iron, indicating a change in the rate-limiting step from the primary discharge of OH ions to the recombination of oxygen radicals. Resistivity, structural, and compositional measurements indicate that high oxygen content is necessary to gain the full benefit of the iron dopant. Initial tests in KOH indicate excellent long-term stability. A film deposited from the FeNi alloy target, which exhibited low oxygen overpotentials and a Tafel slope of 35 mV/dec, had not degraded appreciably following more than 7000 h of operation at an anodic current density of 20 mA/cm 2 . Taken together, the low oxygen evolution reaction overpotentials, the excellent stability in KOH, and the relative insensitivity to iron content indicate that reactively sputtered iron-doped nickel oxide is promising as an oxygen catalyst.
Life‐cycle assessment of photovoltaic systems: results of Swiss studies on energy chains

The methodology used and results obtained for grid-connected photovoltaic (PV) plants in recent Swiss life-cycle assessment (LCA) studies on current and future energy systems are discussed. Mono- and polycrystalline silicon cell technologies utilized in current panels as well as monocrystalline and amorphous cells for future applications were analysed for Swiss conditions. The environmental inventories of slanted-roof solar panels and large plants are presented. Greenhouse gas emissions from present and future electricity systems are compared. The high electricity requirements for manufacturing determine most of the environmental burdens associated with current photovoltaics. However, due to increasing efficiency of production processes and cells, the environmental performance of PV systems is likely to improve substantially in the future. © 1998 John Wiley & Sons, Ltd.
tf.data: A Machine Learning Data Processing Framework

Training machine learning models requires feeding input data for models to ingest. Input pipelines for machine learning jobs are often challenging to implement efficiently as they require reading large volumes of data, applying complex transformations, and transferring data to hardware accelerators while overlapping computation and communication to achieve optimal performance. We present tf.data, a framework for building and executing efficient input pipelines for machine learning jobs. The tf.data API provides operators which can be parameterized with user-defined computation, composed, and reused across different machine learning domains. These abstractions allow users to focus on the application logic of data processing, while tf.data's runtime ensures that pipelines run efficiently. We demonstrate that input pipeline performance is critical to the end-to-end training time of state-of-the-art machine learning models. tf.data delivers the high performance required, while avoiding the need for manual tuning of performance knobs. We show that tf.data features, such as parallelism, caching, static optimizations, and non-deterministic execution are essential for high performance. Finally, we characterize machine learning input pipelines for millions of jobs that ran in Google's fleet, showing that input data processing is highly diverse and consumes a significant fraction of job resources. Our analysis motivates future research directions, such as sharing computation across jobs and pushing data projection to the storage layer.
Reinforcement Learning based Per-antenna Discrete Power Control for Massive MIMO Systems

Power consumption is one of the major issues in massive MIMO (multiple input multiple output) systems, causing increased long-term operational cost and overheating issues. In this paper, we consider per-antenna power allocation with a given finite set of power levels towards maximizing the long-term energy efficiency of the multi-user systems, while satisfying the QoS (quality of service) constraints at the end users in terms of required SINRs (signal-to-interference-plus-noise ratio), which depends on channel information. Assuming channel states to vary as a Markov process, the constraint problem is modeled as an unconstraint problem, followed by the power allocation based on Q-learning algorithm. Simulation results are presented to demonstrate the successful minimization of power consumption while achieving the SINR threshold at users.
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization

Federated learning (FL), as a type of distributed machine learning frameworks, is vulnerable to external attacks on FL models during parameters transmissions. An attacker in FL may control a number of participant clients, and purposely craft the uploaded model parameters to manipulate system outputs, namely, model poisoning (MP). In this paper, we aim to propose effective MP algorithms to combat state-of-the-art defensive aggregation mechanisms (e.g., Krum and Trimmed mean) implemented at the server without being noticed, i.e., covert MP (CMP). Specifically, we first formulate the MP as an optimization problem by minimizing the Euclidean distance between the manipulated model and designated one, constrained by a defensive aggregation rule. Then, we develop CMP algorithms against different defensive mechanisms based on the solutions of their corresponding optimization problems. Furthermore, to reduce the optimization complexity, we propose low complexity CMP algorithms with a slight performance degradation. In the case that the attacker does not know the defensive aggregation mechanism, we design a blind CMP algorithm, in which the manipulated model will be adjusted properly according to the aggregated model generated by the unknown defensive aggregation. Our experimental results demonstrate that the proposed CMP algorithms are effective and substantially outperform existing attack mechanisms.
Effects of NaCl salinity on nitrate uptake and partitioning of N and C in Festuca rubra L. in relation to growth rate

Summary RUBINIGG M. , ELZENGA J. T. M. & STULEN I. 2002. Effects of NaCl salinity on nitrate uptake and partitioning of N and C in Festuca rubra L. in relation to growth rate. - Phyton (Horn, Austria) 42 (2): 251-267, with 6 figures. - English with German summary. The effect of salinity on nitrate net uptake rate was studied in the moderately salt tolerant halophyte Festuca rubra L. , in relation to changes in relative growth rate, root weight ratio and nitrogen and carbon partitioning. Plants were grown for 21 days on nutrient solution containing 50, 100 and 200 mol m"3 sodium chloride. Control plants received no additional sodium chloride. Relative growth rate of Festuca rubra was reduced by exposure to a sodium chloride concentration as low as 50 mol rrf3, resulting in a lower nitrogen 'demand' for plant growth and consequently in a lower nitrate net uptake rate. However, the accumulation of reduced nitrogen containing osmotic solutes increased the plant's nitrogen 'demand' slightly. The decreased root weight ratio, caused by the stronger inhibition of root growth as compared to shoot growth upon elevated salinity levels complicated the interpretation of the effects of different salinity treatments on nitrate net uptake rates. Utilization of soluble sugars for osmotic adjustment did not divert significant amounts of carbo
Exact densities of loops in O(1) dense loop model and of clusters in critical percolation on a cylinder: II. Rotated lattice

This work continues the study started in Povolotsky (2021 J. Phys. A: Math. Theor. 54 22LT01), where the exact densities of loops in the O(1) dense loop model on an infinite strip of the square lattice with periodic boundary conditions were obtained. These densities are also equal to the densities of critical percolation clusters on the 45∘ rotated square lattice rolled into a cylinder. Here, we extend those results to the square lattice with a tilt. This in particular allows us to obtain the densities of critical percolation clusters on the cylinder of the square lattice of standard orientation extensively studied before. We obtain exact densities of contractible and non-contractible loops or equivalently the densities of critical percolation clusters, which do not and do wrap around the cylinder, respectively. The solution uses the mapping of O(1) dense loop model to the six-vertex model in the Razumov–Stroganov point, while the effective tilt is introduced via the inhomogeneous transfer matrix proposed by Fujimoto. The further solution is based on the Bethe ansatz and Fridkin–Stroganov–Zagier’s solution of Baxter’s T–Q equation. The results are represented in terms of the solution of two explicit systems of linear algebraic equations, which can be performed either analytically for small circumferences of the cylinder or numerically for larger ones. We present exact rational values of the densities on the cylinders of small circumferences and several lattice orientations and use the results of high precision numerical calculations to study the finite-size corrections to the densities, in particular their dependence on the tilt of the lattice.
Metastability for the Ising model on the hexagonal lattice

We consider the Ising model on the hexagonal lattice evolving according to Metropolis dynamics. We study its metastable behavior in the limit of vanishing temperature when the system is immersed in a small external magnetic field. We determine the asymptotic properties of the transition time from the metastable to the stable state up to a multiplicative factor and study the mixing time and the spectral gap of the Markov process. We give a geometrical description of the critical configurations and show how not only their size but their shape varies depending on the thermodynamical parameters. Finally we provide some results concerning polyiamonds of maximal area and minimal perimeter.
Counting regions of the boxed threshold arrangement

In this paper we consider the hyperplane arrangement in R whose hyperplanes are {xi + xj = 1 | 1 ≤ i < j ≤ n} ∪ {xi = 0, 1 | 1 ≤ i ≤ n}. We call it the boxed threshold arrangement since we show that the bounded regions of this arrangement are contained in an n-cube and are in one-to-one correspondence with the labeled threshold graphs on n vertices. The problem of counting regions of this arrangement was studied earlier by Joungmin Song. He determined the characteristic polynomial of this arrangement by relating its coefficients to the count of certain graphs. Here, we provide bijective arguments to determine the number of regions. In particular, we construct certain signed partitions of the set {−n, . , n} \ {0} and also construct colored threshold graphs on n vertices and show that both these objects are in bijection with the regions of the boxed threshold arrangement. We independently count these objects and provide a closed form formula for the number of regions.
Reducing ReLU Count for Privacy-Preserving CNN Speedup

Privacy-Preserving Machine Learning algorithms must balance classification accuracy with data privacy. This can be done using a combination of cryptographic and machine learning tools such as Convolutional Neural Networks (CNN). CNNs typically consist of two types of operations: a convolutional or linear layer, followed by a non-linear function such as ReLU. Each of these types can be implemented efficiently using a different cryptographic tool. But these tools require different representations and switching between them is time-consuming and expensive. Recent research suggests that ReLU is responsible for most of the communication bandwidth. ReLU is usually applied at each pixel (or activation) location, which is quite expensive. We propose to share ReLU operations. Specifically, the ReLU decision of one activation can be used by others, and we explore different ways to group activations and different ways to determine the ReLU for such a group of activations. Experiments on several datasets reveal that we can cut the number of ReLU operations by up to three orders of magnitude and, as a result, cut the communication bandwidth by more than 50%.
A transformer based approach for fighting COVID-19 fake news

The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work. After extensive experimentation with numerous architectures and techniques, we use eight different transformer-based pre-trained models with additional layers to construct a stacking ensemble classifier and fine-tuned them for our purpose. We achieved 0.979906542 accuracy, 0.979913119 precision, 0.979906542 recall, and 0.979907901 f1-score on the test dataset of the competition.
Playable Video Generation

This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github. io/playable-video-generation-website.
Adversarial Machine Learning Attacks on Condition-Based Maintenance Capabilities

Condition-based maintenance (CBM) strategies exploit machine learning models to assess the health status of systems based on the collected data from the physical environment, while machine learning models are vulnerable to adversarial attacks. A malicious adversary can manipulate the collected data to deceive the machine learning model and affect the CBM system's performance. Adversarial machine learning techniques introduced in the computer vision domain can be used to make stealthy attacks on CBM systems by adding perturbation to data to confuse trained models. The stealthy nature causes difficulty and delay in detection of the attacks. In this paper, adversarial machine learning in the domain of CBM is introduced. A case study shows how adversarial machine learning can be used to attack CBM capabilities. Adversarial samples are crafted using the Fast Gradient Sign method, and the performance of a CBM system under attack is investigated. The obtained results reveal that CBM systems are vulnerable to adversarial machine learning attacks and defense strategies need to be considered.
Low-Density Neutron Matter and the Unitary Limit

We review the properties of neutron matter in the low-density regime. In particular, we revise its ground state energy and the superfluid neutron pairing gap and analyze their evolution from the weak to the strong coupling regime. The calculations of the energy and the pairing gap are performed, respectively, within the Brueckner–Hartree–Fock (BHF) approach of nuclear matter and the Bardeen–Cooper–Schrieffer (BCS) theory using the chiral nucleon-nucleon interaction of Entem and Machleidt at N3LO and the Argonne V18 phenomenological potential. Results for the energy are also shown for a simple Gaussian potential with a strength and range adjusted to reproduce the 1S0 neutron-neutron scattering length and effective range. Our results are compared with those of quantum Monte Carlo (QMC) calculations for neutron matter and cold atoms. The Tan contact parameter in neutron matter is also calculated, finding a reasonable agreement with experimental data from ultra-cold atoms only at very low densities. We find that low-density neutron matter exhibits a behavior close to that of a Fermi gas at the unitary limit, although, this limit is actually never reached. We also review the properties (energy, effective mass, and quasiparticle residue) of a spin-down neutron impurity immersed in a low-density free Fermi gas of spin-up neutrons already studied by the author in a recent work where it was shown that these properties are very close to those of an attractive Fermi polaron in the unitary limit.
Beyond Equi-joins: Ranking, Enumeration and Factorization

We study theta-joins in general and join predicates with conjunctions and disjunctions of inequalities in particular, focusing on ranked enumeration where the answers are returned incrementally in an order dictated by a given ranking function. Our approach achieves strong time and space complexity properties: with n denoting the number of tuples in the database, we guarantee for acyclic full join queries with inequality conditions that for every value of k, the k top-ranked answers are returned in O(npolylogn+klogk) time. This is within a polylogarithmic factor of O(n+klogk), i.e., the best known complexity for equi-joins, and even of O(n+k), i.e., the time it takes to look at the input and return k answers in any order. Our guarantees extend to join queries with selections and many types of projections (namely those called “free-connex” queries and those that use bag semantics). Remarkably, they hold even when the number of join results is nℓ for a join of ℓ relations. The key ingredient is a novel O(npolylogn)-size factorized representation of the query output, which is constructed on-the-fly for a given query and database. In addition to providing the first non-trivial theoretical guarantees beyond equi-joins, we show in an experimental study that our ranked-enumeration approach is also memory-efficient and fast in practice, beating the running time of state-of-the-art database systems by orders of magnitude.
Online Capacity Scaling Augmented With Unreliable Machine Learning Predictions

Modern data centers suffer from immense power consumption. As a result, data center operators have heavily invested in capacity scaling solutions, which dynamically deactivate servers if the demand is low and activate them again when the workload increases. We analyze a continuous-time model for capacity scaling, where the goal is to minimize the weighted sum of flow-time, switching cost, and power consumption in an online fashion. We propose a novel algorithm, called Adaptive Balanced Capacity Scaling (ABCS), that has access to black-box machine learning predictions. ABCS aims to adapt to the predictions and is also robust against unpredictable surges in the workload. In particular, we prove that ABCS is $(1+\varepsilon)$-competitive if the predictions are accurate, and yet, it has a uniformly bounded competitive ratio even if the predictions are completely inaccurate. Finally, we investigate the performance of this algorithm on a real-world dataset and carry out extensive numerical experiments, which positively support the theoretical results.
Responses of Laying Chickens to Diets Formulated by Following Different Feeding Standards

Responses of laying chickens to diets based on formulations following different feeding standards were investigated during March to July months under Bangladesh conditions. One hundred twenty Shaver 579 layers of 35-week old were distributed randomly into 5 diet groups with 3 replicates, each of 8 birds. The birds were reared in laying batteries with 2 birds in each cage and 8 birds in four adjacent cages constituted a replicate. The feeding standards that constituted different dietary treatments were: Shaver 579 (1997), recommended by the breeders of the birds considered in this study; NRC (National Research Council, 1994); INSP (International Nutrition Standards for Poultry, 1983) recommended for Asiatic regions; ISI (Indian Standard Institute, 1992) and BSTI (Bangladesh Standard Testing Institute, 1988). Other cares and management were identical for birds of all diet groups. The feeding trial was conducted for 15 weeks and the data on laying performance and egg quality were evaluated. Evaluation of results revealed that responses of laying birds fed diet based on formulation following the recommendation for Shaver 579 (control) were at least equal to or better than those of other standards during March to July months under Bangladesh conditions.
Bounds for the b-chromatic number of powers of hypercubes

The b-chromatic number $b(G)$ of a graph $G$ is the maximum $k$ for which $G$ has a proper vertex coloring using $k$ colors such that each color class contains at least one vertex adjacent to a vertex of every other color class. In this paper, we mainly investigate on one of the open problems given in [P. Francis, S. Francis Raj, On b-coloring of powers of hypercubes, Discrete Appl. Math. 225 (2017) 74-86. ]. As a consequence, we have obtained an upper bound for the b-chromatic number of some powers of hypercubes. This turns out to be an improvement of the already existing bound in [P. Francis, S. Francis Raj, On b-coloring of powers of hypercubes, Discrete Appl. Math. 225 (2017) 74-86. ]. Further, we have determined a lower bound for the b-chromatic number of some powers of the Hamming graph, a generalization of the hypercube.
The Black Hole Spin in GRS 1915+105, Revisited

We estimate the black hole spin parameter in GRS 1915+105 using the continuum-fitting method with revised mass and inclination constraints based on the very long baseline interferometric parallax measurement of the distance to this source. We fit Rossi X-ray Timing Explorer observations selected to be accretion-disk-dominated spectral states as described in McClintock et al. (2006) and Middleton et al. (2006), which previously gave discrepant spin estimates with this method. We find that, using the new system parameters, the spin in both data sets increased, providing a best-fit spin of a * = 0.86 for the Middleton et al. data and a poor fit for the McClintock et al. data set, which becomes pegged at the BHSPEC model limit of a * = 0.99. We explore the impact of the uncertainties in the system parameters, showing that the best-fit spin ranges from a * = 0.4 to 0.99 for the Middleton et al. data set and allows reasonable fits to the McClintock et al. data set with near-maximal spin for system distances greater than ∼10 kpc. We discuss the uncertainties and implications of these estimates.