Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-level Training and Bayesian Optimization for Economical Hyperparameter Optimization

Hyperparameters play a critical role in the performances of many machine learning methods. Determining their best settings or Hyperparameter Optimization (HPO) faces difficulties presented by the large number of hyperparameters as well as the excessive training time. In this paper, we develop an effective approach to reducing the total amount of required training time for HPO. In the initialization, the nested Latin hypercube design is used to select hyperparameter configurations for two types of training, which are, respectively, heavy training and light training. We propose a truncated additive Gaussian process model to calibrate approximate performance measurements generated by light training, using accurate performance measurements generated by heavy training. Based on the model, a sequential model-based algorithm is developed to generate the performance profile of the configuration space as well as find optimal ones. Our proposed approach demonstrates competitive performance when applied to optimize synthetic examples, support vector machines, fully connected networks and convolutional neural networks.
Modeling Nonlinear Growth Followed by Long-Memory Equilibrium with Unknown Change Point

Measurements of many biological processes are characterized by an initial growth period followed by an equilibrium period. Scientists may wish to quantify features of the growth and equilibrium periods, as well as the timing of the change point between the two. Specifically, we are motivated by problems in the study of electrical cell-substrate impedance sensing (ECIS) data. ECIS is a popular new technology which measures cell behavior non-invasively. Previous studies using ECIS data have found that different cell types can be classified by their equilibrium behavior. Likewise, it has been hypothesized that the presence of certain infections can be identified from cells' equilibrium behavior. However, it can be challenging to identify when equilibrium has been reached, and to quantify the relevant features of cells' equilibrium behavior. In this paper, we assume that measurements during the growth period are independent deviations from a smooth nonlinear function of time, and that measurements during the equilibrium period are characterized by a simple long memory model. We propose a method to simultaneously estimate the parameters of the growth and equilibrium processes and locate the change point between the two. We find that this method performs well in simulations and in practice. When applied to ECIS data, it produces estimates of change points and measures of cell equilibrium behavior which offer improved classification of infected and uninfected cells.
Global Introductions of Crayfishes: Evaluating the Impact of Species Invasions on Ecosystem Services

Impacts of nonindigenous crayfishes on ecosystem services exemplify the mixture of positive and negative effects of intentionally introduced species. Global introductions for aquaculture and ornamental purposes have begun to homogenize naturally disjunct global distributions of crayfish families. Negative impacts include the loss of provisioning (e.g., reductions in edible native species, reproductive interference or hybridization with native crayfishes), regulatory (e.g., lethal disease spread, increased costs to agriculture and water management), supporting (e.g., large changes in ecological communities), and cultural (e.g., loss of festivals celebrating native crayfish) services. Where quantification of impacts exists (e.g., Procambarus clarkii and Pacifastacus leniusculus in Europe), regulations now prohibit introduction and spread of crayfishes, indicating that losses of ecosystem services have outweighed gains. Recent research advances such as predicting invasiveness, predicting spread, improved detection and control, and bioeconomic analysis to increase cost-effectiveness of management could be employed to reduce future losses of ecosystem services.
Effects of Salinity on Seedling Biomass Production and Relative Water Content of Some Haricot Bean (Phaseolus vulgaris) Varieties

The aim of this research was to study the effects of salinity on fourteen haricot bean (Phaseolus vulgaris) varieties at 0, 2, 4, 8 and 16 dS/m. Data analysis was carried out using jmp 5 (version 5.0) and SPSS (version 12) statistical softwares. Seedling Shoot Fresh Weight (SSFW), Seedling Shoot Dry Weight (SSDW), Seedling Root Fresh Weight (SRFW) and Seedling Root Dry Weight (SRDW) were measured; and seedling Shoot Relative Water Content (SRWC) and seedling Root Relative Water Content (RRWC) were calculated. The two ways ANOVA for varieties found statistically insignificant with respect to Seedling Root Fresh Weight (SRFW), seedling Shoot Relative Water Content (SRWC) and seedling root relative water content (RRWC) (p>0.05) but it was significant with respect to Seedling Shoot Fresh Weight (SSFW), Seedling Shoot Dry Weight (SSDW) and Seedling Root Dry Weight (SRDW) (p 0.05) with respect to the entire parameters. Varieties such as Awash-1, Argene, Chore, Mexican 142 and Awash Melka were found to be salt tolerant during seedling biomass production and in Relative Water Content (RWC). On the other hand, varieties Red Kidney (DRK), Dimtu, Gofta, Cranscope and Sinkinesh were found to be salt sensitive during seedling biomass production and in RWC. The rest haricot bean varieties were intermediate in their salt tolerance. The study affirmed the presence of broad intraspecific genetic variation in haricot bean varieties for salt stress with respect to their early biomass production and Relative Water Content (RWC).
Accumulation of protein subunits and their polymers in developing grains of hexaploid wheats

Technological properties of wheat flours are largely governed by the functional properties of storage proteins, comprising single chain gliadins and disulphide-bonded polymeric proteins (glutenins, triticins, HMW albumins). This study was conducted (a) to measure the rate of accumulation of different classes of proteins, (b) to monitor the rate of polymerization of the polymeric subunits, and (c) to investigate the origin of differences in the degree of polymerization arising from allelic differences at the Glu-D1 locus of HMW glutenin subunits. Developing grains from hexaploid wheat cultivars were collected at 3-d intervals between 7 d and 40 d after anthesis (DAA). The accumulation behaviour of polymeric proteins was determined by size exclusion-HPLC and of the constituent polypeptides (HMW glutenin subunits, LMW glutenin subunits, HMW albumin subunits) by a combination of SDS-PAGE, reversed phase-HPLC and immunoblotting. The relative time for peak accumulation of the albumins/gobulins, gliadins and polymeric proteins occurred in that order during grain filling. Deposition of HMW glutenin subunits, LMW glutenin subunits and HMW albumin subunits (β-amylases) began as early as 7 d after anthesis and progressed until maturity. Disulphide-linked aggregation (polymerization) of these subunits also commenced at 7 DAA. However, significant changes in size distribution of the polymers occurred only during the late stages of seed development which coincided with a rapid increase in the amounts of the glutenin subunits. The effects of HMW albumin subunits and LMW glutenin subunits on polymer size were relatively smaller than that of the HMW glutenin subunits. The wheat biotype with HMW glutenin subunits 5+10 (Glu-D1d allele) accumulated larger polymers more quickly than the biotype with allelic subunits 2+12 (Glu-D1a allele), this difference becoming apparent at 28 DAA which could be attributed to variation in the accumulation rate of HMW glutenin subunits.
Distributed Control of Charging for Electric Vehicle Fleets Under Dynamic Transformer Ratings

Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods, such as those based on dual decomposition and alternating direction method of multipliers (ADMM), are compared against the new augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method and a novel low-information, look-ahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs.
Regret Analysis of a Markov Policy Gradient Algorithm for Multi-arm Bandits

We consider a policy gradient algorithm applied to a finite-arm bandit problem with Bernoulli rewards. We allow learning rates to depend on the current state of the algorithm rather than using a deterministic time-decreasing learning rate. The state of the algorithm forms a Markov chain on the probability simplex. We apply Foster–Lyapunov techniques to analyze the stability of this Markov chain. We prove that, if learning rates are well-chosen, then the policy gradient algorithm is a transient Markov chain, and the state of the chain converges on the optimal arm with logarithmic or polylogarithmic regret.
Mechanism Design for Efficient Online and Offline Allocation of Electric Vehicles to Charging Stations

The industry related to electric vehicles (EVs) has seen a substantial increase in recent years, as such vehicles have the ability to significantly reduce total CO2 emissions and the related global warming effect. In this paper, we focus on the problem of allocating EVs to charging stations, scheduling and pricing their charging. Specifically, we developed a Mixed Integer Program (MIP) which executes offline and optimally allocates EVs to charging stations. On top, we propose two alternative mechanisms to price the electricity the EVs charge. The first mechanism is a typical fixed-price one, while the second is a variation of the Vickrey–Clark–Groves (VCG) mechanism. We also developed online solutions that incrementally call the MIP-based algorithm and solve it for branches of EVs. In all cases, the EVs’ aim is to minimize the price to pay and the impact on their driving schedule, acting as self-interested agents. We conducted a thorough empirical evaluation of our mechanisms and we observed that they had satisfactory scalability. Additionally, the VCG mechanism achieved an up to 2.2% improvement in terms of the number of vehicles that were charged compared to the fixed-price one and, in cases where the stations were congested, it calculated higher prices for the EVs and provided a higher profit for the stations, but lower utility to the EVs. However, in a theoretical evaluation, we proved that the variant of the VCG mechanism being proposed in this paper still guaranteed truthful reporting of the EVs’ preferences. In contrast, the fixed-price one was found to be vulnerable to agents’ strategic behavior as non-truthful EVs can charge instead of truthful ones. Finally, we observed the online algorithms to be, on average, at 95.6% of the offline ones in terms of the average number of serviced EVs.
Morphological Skip-Gram: Using morphological knowledge to improve word representation

Natural language processing models have attracted much interest in the deep learning community. This branch of study is composed of some applications such as machine translation, sentiment analysis, named entity recognition, question and answer, and others. Word embeddings are continuous word representations, they are an essential module for those applications and are generally used as input word representation to the deep learning models. Word2Vec and GloVe are two popular methods to learn word embeddings. They achieve good word representations, however, they learn representations with limited information because they ignore the morphological information of the words and consider only one representation vector for each word. This approach implies that Word2Vec and GloVe are unaware of the word inner structure. To mitigate this problem, the FastText model represents each word as a bag of characters n-grams. Hence, each n-gram has a continuous vector representation, and the final word representation is the sum of its characters n-grams vectors. Nevertheless, the use of all n-grams character of a word is a poor approach since some n-grams have no semantic relation with their words and increase the amount of potentially useless information. This approach also increases the training phase time. In this work, we propose a new method for training word embeddings, and its goal is to replace the FastText bag of character n-grams for a bag of word morphemes through the morphological analysis of the word. Thus, words with similar context and morphemes are represented by vectors close to each other. To evaluate our new approach, we performed intrinsic evaluations considering 15 different tasks, and the results show a competitive performance compared to FastText.
Active Deception using Factored Interactive POMDPs to Recognize Cyber Attacker's Intent

This paper presents an intelligent and adaptive agent that employs deception to recognize a cyber adversary's intent. Unlike previous approaches to cyber deception, which mainly focus on delaying or confusing the attackers, we focus on engaging with them to learn their intent. We model cyber deception as a sequential decision-making problem in a two-agent context. We introduce factored finitely nested interactive POMDPs (I-POMDPx) and use this framework to model the problem with multiple attacker types. Our approach models cyber attacks on a single honeypot host across multiple phases from the attacker's initial entry to reaching its adversarial objective. The defending I-POMDPx-based agent uses decoys to engage with the attacker at multiple phases to form increasingly accurate predictions of the attacker's behavior and intent. The use of I-POMDPs also enables us to model the adversary's mental state and investigate how deception affects their beliefs. Our experiments in both simulation and on a real host show that the I-POMDPx-based agent performs significantly better at intent recognition than commonly used deception strategies on honeypots.
Human-like Energy Management Based on Deep Reinforcement Learning and Historical Driving Experiences

Development of hybrid electric vehicles depends on an advanced and efficient energy management strategy (EMS). With online and real-time requirements in mind, this article presents a human-like energy management framework for hybrid electric vehicles according to deep reinforcement learning methods and collected historical driving data. The hybrid powertrain studied has a series-parallel topology, and its control-oriented modeling is founded first. Then, the distinctive deep reinforcement learning (DRL) algorithm, named deep deterministic policy gradient (DDPG), is introduced. To enhance the derived power split controls in the DRL framework, the global optimal control trajectories obtained from dynamic programming (DP) are regarded as expert knowledge to train the DDPG model. This operation guarantees the optimality of the proposed control architecture. Moreover, the collected historical driving data based on experienced drivers are employed to replace the DP-based controls, and thus construct the human-like EMSs. Finally, different categories of experiments are executed to estimate the optimality and adaptability of the proposed human-like EMS. Improvements in fuel economy and convergence rate indicate the effectiveness of the constructed control structure.
Declines in the abundance of Chaetodon butterflyfishes following extensive coral depletion

This study documented temporal variation in the abundance of butterﬂyﬁshes (Chaetodontidae) at Trunk Reef, on the central Great Barrier Reef, Australia, from May 2000 to March 2005. During this period, live coral cover declined by >90%, mostly due to severe coral bleaching. There were no short-term changes (within 4 months) in the abundance of butterﬂyﬁshes following initial declines in live coral cover. Surveys conducted in 2005, however, revealed signiﬁcant declines in the abundance of Chaetodon baronessa, Chaetodon lunulatus, Chaetodon trifascialis, Chaetodon plebeius and Chaetodon rainfordi, all of which are obligate hard-coral feeders. In contrast, there was no signiﬁcant change in the abundance of Chaetodon auriga, Chaetodon aureofasciatus, Chaetodon citrinellus, Chaetodon melannotus or Chaetodon vagabundus, which are much less reliant on scleractinian coral for food. Clearly, extensive coral depletion, such as that caused by severe coral bleaching, can have a major effect on the abundance of butterﬂyﬁshes. Speciﬁc responses of butterﬂyﬁshes varied according to their reliance on hard corals for food and their ability to utilize alternate prey types.
Dynamical Ising model of spatially-coupled ecological oscillators

Long-range synchrony from short-range interactions is a familiar pattern in biological and physical systems, many of which share a common set of “universal” properties at the point of synchronization. Common biological systems of coupled oscillators have been shown to be members of the Ising universality class, meaning that the very simple Ising model replicates certain spatial statistics of these systems at stationarity. This observation is useful because it reveals which aspects of spatial pattern arise independently of the details governing local dynamics, resulting in both deeper understanding of and a simpler baseline model for biological synchrony. However, in many situations a system’s dynamics are of greater interest than their static spatial properties. Here, we ask whether a dynamical Ising model can replicate universal and non-universal features of ecological systems, using noisy coupled metapopulation models with two-cycle dynamics as a case study. The standard Ising model makes unrealistic dynamical predictions, but the Ising model with memory corrects this by using an additional parameter to reflect the tendency for local dynamics to maintain their phase of oscillation. By fitting the two parameters of the Ising model with memory to simulated ecological dynamics, we assess the correspondence between the Ising and ecological models in several of their features (location of the critical boundary in parameter space between synchronous and asynchronous dynamics, probability of local phase changes, and ability to predict future dynamics). We find that the Ising model with memory is reasonably good at representing these properties of ecological metapopulations. The correspondence between these models creates the potential for the simple and well-known Ising class of models to become a valuable tool for understanding complex biological systems.
Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

The usage of convolutional neural networks (CNNs) for unsupervised image segmentation was investigated in this study. Similar to supervised image segmentation, the proposed CNN assigns labels to pixels that denote the cluster to which the pixel belongs. In unsupervised image segmentation, however, no training images or ground truth labels of pixels are specified beforehand. Therefore, once a target image is input, the pixel labels and feature representations are jointly optimized, and their parameters are updated by the gradient descent. In the proposed approach, label prediction and network parameter learning are alternately iterated to meet the following criteria: (a) pixels of similar features should be assigned the same label, (b) spatially continuous pixels should be assigned the same label, and (c) the number of unique labels should be large. Although these criteria are incompatible, the proposed approach minimizes the combination of similarity loss and spatial continuity loss to find a plausible solution of label assignment that balances the aforementioned criteria well. The contributions of this study are four-fold. First, we propose a novel end-to-end network of unsupervised image segmentation that consists of normalization and an argmax function for differentiable clustering. Second, we introduce a spatial continuity loss function that mitigates the limitations of fixed segment boundaries possessed by previous work. Third, we present an extension of the proposed method for segmentation with scribbles as user input, which showed better accuracy than existing methods while maintaining efficiency. Finally, we introduce another extension of the proposed method: unseen image segmentation by using networks pre-trained with a few reference images without re-training the networks. The effectiveness of the proposed approach was examined on several benchmark datasets of image segmentation.
Modernizing the HPC System Software Stack

Through the 1990s, HPC centers at national laboratories, universities, and other large sites designed distributed system architectures and software stacks that enabled extreme-scale computing. By the 2010s, these centers were eclipsed by the scale of web-scale and cloud computing architectures, and today even upcoming exascale HPC systems are magnitudes of scale smaller than those of datacenters employed by large web companies. Meanwhile, the HPC community has allowed system software designs to stagnate, relying on incremental changes to tried-and-true designs to move between generations of systems. We contend that a modern system software stack that focuses on manageability, scalability, security, and modern methods will benefit the entire HPC community. In this paper, we break down the logical parts of a typical HPC system software stack, look at more modern ways to meet their needs, and make recommendations of future work that would help the community move in that direction.
Program algebra for random access machine programs

This paper presents an algebraic theory of instruction sequences with instructions for a random access machine (RAM) as basic instructions, the behaviours produced by the instruction sequences concerned under execution, and the interaction between such behaviours and RAM memories. This theory provides a setting for the development of theory in areas such as computational complexity and analysis of algorithms that distinguishes itself by offering the possibility of equational reasoning to establish whether an instruction sequence computes a given function and being more general than the setting provided by any known version of the RAM model of computation. In this setting, a semi-realistic version of the RAM model of computation and a bit-oriented time complexity measure for this version are introduced. Under the time measure concerned, semi-realistic RAMs can be simulated by multi-tape Turing machines with quadratic time overhead.