Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison and Evaluation of Methods for a Predict+Optimize Problem in Renewable Energy

Algorithms that involve both forecasting and optimization are at the core of solutions to many difficult real-world problems, such as in supply chains (inventory optimization), traffic, and in the transition towards carbon-free energy generation in battery/load/production scheduling in sustainable energy systems. Typically, in these scenarios we want to solve an optimization problem that depends on unknown future values, which therefore need to be forecast. As both forecasting and optimization are difficult problems in their own right, relatively few research has been done in this area. This paper presents the findings of the ``IEEE-CIS Technical Challenge on Predict+Optimize for Renewable Energy Scheduling,"held in 2021. We present a comparison and evaluation of the seven highest-ranked solutions in the competition, to provide researchers with a benchmark problem and to establish the state of the art for this benchmark, with the aim to foster and facilitate research in this area. The competition used data from the Monash Microgrid, as well as weather data and energy market data. It then focused on two main challenges: forecasting renewable energy production and demand, and obtaining an optimal schedule for the activities (lectures) and on-site batteries that lead to the lowest cost of energy. The most accurate forecasts were obtained by gradient-boosted tree and random forest models, and optimization was mostly performed using mixed integer linear and quadratic programming. The winning method predicted different scenarios and optimized over all scenarios jointly using a sample average approximation method.
Page time and the order parameter for a consciousness state

In this short note using the analogy with the recent resolution of the black hole information paradox we conjecture the order parameter for the state of consciousness based on the notion of the Page curve and the Page time. The entanglement between the state of the brain and time series of neuronal firing as well as the non-orthogonality of the functional connectomes play a key role.
RobTL: A Temporal Logic for the Robustness of Cyber-Physical Systems

We propose the Robustness Temporal Logic ( RobTL ), a novel temporal logic for the specification and analysis of distances between the behaviours of Cyber-Physical Systems (CPSs) over a finite time horizon. Differently from classical temporal logic expressing properties on the behaviour of a system, we can use RobTL specifications to measure the differences in the behaviours of systems with respect to various objectives and temporal constraints, and to study how those differences evolve in time . Since the behaviour of CPSs is inevitably subject to uncertainties and approximations, we show how the unique features of RobTL allow us to specify property of robustness of systems against perturbations , i.e., their capability to function correctly even under the effect of perturbations. Given the probabilistic nature of CPSs, our model checking algorithm for RobTL specifications is based on statistical inference . As an example of an application of our framework, we consider a supervised, self-coordinating engine system that is subject to attacks aimed at inflicting overstress of equipment.
Detecting Temporal shape changes with the Euler Characteristic Transform

Organoids are multi-cellular structures which are cultured in vitro from stem cells to resemble speciﬁc organs (e.g., brain, liver) in their three-dimensional composition. Dynamic changes in the shape and composition of these model systems can be used to understand the eﬀect of mutations and treatments in health and disease. In this paper, we propose a new technique in the ﬁeld of topological data analysis for DEtecting Temporal shape changes with the Euler Characteristic Transform (DETECT). DETECT is a rotationally invariant signature of dynam- ically changing shapes. We demonstrate our method on a data set of segmented videos of mouse small intestine organoid experiments and show that it outperforms classical shape descriptors. We verify our method on a synthetic organoid data set and illustrate how it generalises to 3D. We conclude that DETECT oﬀers rigorous quantiﬁcation of organoids and opens up computationally scalable methods for distinguishing diﬀerent growth regimes and assessing treatment eﬀects.
Solutions of Maxwell’s equations for electric and magnetic fields in arbitrary media

Solutions of Maxwell’s equations for fields in arbitrary media are derived in terms of charge density ρ, current density J, polarization P, and magnetization M. The solutions express E, D, H, and B as integrals of retarded ρ, J, P, M, and their spatial and temporal derivatives.
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction

When a human communicates with a machine using natural language on the web and online, how can it understand the human’s intention and semantic context of their talk? This is an important AI task as it enables the machine to construct a sensible answer or perform a useful action for the human. Meaning is represented at the sentence level, identification of which is known as intent detection, and at the word level, a labelling task called slot filling. This dual-level joint task requires innovative thinking about natural language and deep learning network design, and as a result, many approaches and models have been proposed and applied. This tutorial will discuss how the joint task is set up and introduce Spoken Language Understanding/Natural Language Understanding (SLU/NLU) with Deep Learning techniques. We will cover the datasets, experiments and metrics used in the field. We will describe how the machine uses the latest NLP and Deep Learning techniques to address the joint task, including recurrent and attention-based Transformer networks and pre-trained models (e.g. BERT). We will then look in detail at a network that allows the two levels of the task, intent classification and slot filling, to interact to boost performance explicitly. We will do a code demonstration of a Python notebook for this model and attendees will have an op-portunity to watch coding demo tasks on this joint NLU to further their understanding.
A new near-linear time algorithm for k-nearest neighbor search using a compressed cover tree

Given a reference set $R$ of $n$ points and a query set $Q$ of $m$ points in a metric space, this paper studies an important problem of finding $k$-nearest neighbors of every point $q \in Q$ in the set $R$ in a near-linear time. In the paper at ICML 2006, Beygelzimer, Kakade, and Langford introduced a cover tree on $R$ and attempted to prove that this tree can be built in $O(n\log n)$ time while the nearest neighbor search can be done in $O(n\log m)$ time with a hidden dimensionality factor. This paper fills a substantial gap in the past proofs of time complexity by defining a simpler compressed cover tree on the reference set $R$. The first new algorithm constructs a compressed cover tree in $O(n \log n)$ time. The second new algorithm finds all $k$-nearest neighbors of all points from $Q$ using a compressed cover tree in time $O(m(k+\log n)\log k)$ with a hidden dimensionality factor depending on point distributions of the given sets $R,Q$ but not on their sizes.
Time to augment self-supervised visual representation learning

Biological vision systems are unparalleled in their ability to learn visual representations without supervision. In machine learning, self-supervised learning (SSL) has led to major advances in forming object representations in an unsupervised fashion. Such systems learn representations invariant to augmentation operations over images, like cropping or flipping. In contrast, biological vision systems exploit the temporal structure of the visual experience during natural interactions with objects. This gives access to"augmentations"not commonly used in SSL, like watching the same object from multiple viewpoints or against different backgrounds. Here, we systematically investigate and compare the potential benefits of such time-based augmentations during natural interactions for learning object categories. Our results show that time-based augmentations achieve large performance gains over state-of-the-art image augmentations. Specifically, our analyses reveal that: 1) 3-D object manipulations drastically improve the learning of object categories; 2) viewing objects against changing backgrounds is important for learning to discard background-related information from the latent representation. Overall, we conclude that time-based augmentations during natural interactions with objects can substantially improve self-supervised learning, narrowing the gap between artificial and biological vision systems.
5G Long-Term and Large-Scale Mobile Traffic Forecasting

It is crucial for the service provider to comprehend and forecast mobile traffic in large-scale cellular networks in order to govern and manage mechanisms for base station placement, load balancing, and network planning. The purpose of this article is to extract and simulate traffic patterns from more than 14,000 cells that have been installed in different metropolitan areas. To do this, we create, implement, and assess a method in which cells are first categorized by their point of interest and then clustered based on the temporal distribution of cells in each region. The proposed model has been tested using real-world 5G mobile traffic datasets collected over 31 weeks in various cities. We found that our proposed model performed well in predicting mobile traffic patterns up to 2 weeks in advance. Our model outperformed the base model in most areas of interest and generally achieved up to 15\% less prediction error compared to the na\"ive approach. This indicates that our approach is effective in predicting mobile traffic patterns in large-scale cellular networks.
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization

Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summaries. However, recent unsupervised models are extractive, which remove words from texts and thus they are less flexible than abstractive summarization. In this work, we devise an abstractive model based on reinforcement learning without ground-truth summaries. We formulate the unsupervised summarization based on the Markov decision process with rewards representing the summary quality. To further enhance the summary quality, we develop a multi-summary learning mechanism that generates multiple summaries with varying lengths for a given text, while making the summaries mutually enhance each other. Experimental results show that the proposed model substantially outperforms both abstractive and extractive models, yet frequently generating new words not contained in input texts.
Surface Waves in Piezoelectrics

The effect of various electromagnetic boundary conditions on the propagation of surface waves in piezo‐electrics is considered. Basic for the analysis is the introduction of an electric ``surface impedance'' which relates the electric potential to the normal component of electric displacement in the surface. An analytic expression for this impedance is found in the case of weak piezoelectric coupling which permits the calculation of the phase velocity for arbitrary values of the impedance. It is also shown that within the weak coupling approximation the surface impedance contains the information on material parameters necessary for calculating the power transferred to surface waves from an impressed electric current.
A Generalized Approach to Impedance Control Design for Robotic Minimally Invasive Surgery

Energy based control methods are at the core of modern robotic control algorithms. In this paper we present a general approach to virtual model/mechanism control, which is a powerful design tool to create energy based controllers. We present two novel virtual-mechanisms designed for robotic minimally invasive surgery, which control the position of a surgical instrument while passing through an incision. To these virtual mechanisms we apply the parameter tuning method of Larby and Forni 2022, which optimizes for local performance while ensuring global stability.
DeepGlow: An efficient neural network emulator of physical afterglow models for gamma-ray bursts and gravitational-wave events

Abstract Gamma-ray bursts (GRBs) and double neutron star merger gravitational-wave events are followed by afterglows that shine from X-rays to radio, and these broadband transients are generally interpreted using analytical models. Such models are relatively fast to execute, and thus easily allow estimates of the energy and geometry parameters of the blast wave, through many trial-and-error model calculations. One problem, however, is that such analytical models do not capture the underlying physical processes as well as more realistic relativistic numerical hydrodynamic (RHD) simulations do. Ideally, those simulations are used for parameter estimation instead, but their computational cost makes this intractable. To this end, we present DeepGlow, a highly efficient neural network architecture trained to emulate a computationally costly RHD-based model of GRB afterglows, to within a few percent accuracy. As a first scientific application, we compare both the emulator and a different analytical model calibrated to RHD simulations, to estimate the parameters of a broadband GRB afterglow. We find consistent results between these two models, and also give further evidence for a stellar wind progenitor environment around this GRB source. DeepGlow fuses simulations that are otherwise too complex to execute over all parameters, to real broadband data of current and future GRB afterglows.
Ontologically Faithful Generation of Non-Player Character Dialogues

We introduce a language generation task grounded in a popular video game environment. KNUDGE (KNowledge Constrained User-NPC Dialogue GEneration) requires models to produce trees of dialogue between video game characters that accurately reflect quest and entity specifications stated in natural language. KNUDGE is constructed from side quest dialogues drawn directly from game data of Obsidian Entertainment's The Outer Worlds, leading to real-world complexities in generation: (1) dialogues are branching trees as opposed to linear chains of utterances; (2) utterances must remain faithful to the game lore- character personas, backstories, and entity relationships; and (3) a dialogue must accurately reveal new quest details to the human player. We report results for a set of neural generation models using supervised and in-context learning techniques; we find competent performance but room for future work addressing the challenges of creating realistic, game-quality dialogues.
Parameter Estimation of a Rigorous Rate Base Model for Distillation in Packed Columns

Abstract This papers presents the formulation, analysis and parameter estimation of a steady state Rate Base Model (RBM) for a pilot-scale distillation column. A packed column is considered, using non-equilibrium stages to represent the packing segments and accounting for the special structure such that sparsity can be exploited in the non-linear parameter estimation problem. Model parameters are tuned to fit the model with experimental data from multiple experiments. A sensitivity analysis with respect to parameters to estimate is performed in order to properly select the parameters that can be estimated from the available temperature measurements. The tuned model is validated with experimental data.
Forensic Anthropology and Archaeology in Denmark

Abstract In this paper, we provide a brief overview of the status of forensic anthropology and forensic archeology in Denmark, as well as related information about education, research, and skeletal collections. Forensic anthropologists mainly deal with the examination of unidentified skeletal remains. Some special tasks include cranial trauma analysis of the recently deceased, advanced 3D visualization from CT scanning of homicide cases, and stature estimation of perpetrators using surveillance videos. Forensic anthropologists are employed at one of Denmark’s three departments of forensic medicine (in Copenhagen, Odense, and Aarhus) and have access to advanced imaging equipment (e.g., CT and MR scanning, surface scanners, and 3D printers) for use in both their requisitioned work and their research. Extensive research is conducted on different topics, such as the health and diseases of past populations, age estimation, and human morphology. Research is based on skeletal material from the archeological collections housed in Copenhagen and Odense or on CT data from the recently deceased. There is no full degree in forensic anthropology in Denmark, but elective courses and lectures are offered to students at different levels and to people from different professional backgrounds. Forensic archaeology is a relatively new field of expertise in Denmark, and relevant cases are rare, with only one or two cases per year. No forensic archeologists are officially employed in any of the departments of forensic medicine. Until recently, the Special Crime Unit of the police handled crime scene investigations involving excavations, but with the option of enlisting the help of outside specialists, such as archaeologists, anthropologists, and pathologists. An official excavation work group was established in 2015 under the lead of the Special Crime Unit of the police with the aim of refining the methods and procedures used in relevant criminal investigations. The group is represented by five police officers from the Special Crime Scene Unit, a police officer from the National Police Dog Training center, the two archaeologists from Moesgaard Museum, a forensic anthropologist from the Department of Forensic Medicine (University of Copenhagen), and a forensic pathologist from the Department of Forensic Medicine (University of Aarhus).
Complete Genome Sequence of Entomomonas sp. Strain E2T0, Isolated from the Darkling Beetle Zophobas morio Larvae

Here, we present the complete genome sequence of Entomomonas sp. E2T0, a strain isolated from larvae of the darkling beetle Zophobas morio. The isolate was fully resistant to aztreonam and possessed a novel class D β-lactamase gene. The 3,325,929-bp genome consists of a chromosome and a 9,996-bp plasmid. ABSTRACT Here, we present the complete genome sequence of Entomomonas sp. E2T0, a strain isolated from larvae of the darkling beetle Zophobas morio. The isolate was fully resistant to aztreonam and possessed a novel class D β-lactamase gene. The 3,325,929-bp genome consists of a chromosome and a 9,996-bp plasmid.
Mitochondrial Genomes of the American Dog Tick (Dermacentor variabilis) Isolated from Horses in the Midwestern United States

Here, we report two complete and three partial mitochondrial genome sequences of Dermacentor variabilis specimens collected from horses in the United States. The complete genomes are 14,837 bp long and contain 13 protein-coding genes, 2 rRNA genes, and 22 tRNA genes. The sequences have been deposited under GenBank accession numbers ON052120 to ON052124. ABSTRACT Here, we report two complete and three partial mitochondrial genome sequences of Dermacentor variabilis specimens collected from horses in the United States. The complete genomes are 14,837 bp long and contain 13 protein-coding genes, 2 rRNA genes, and 22 tRNA genes. The sequences have been deposited under GenBank accession numbers ON052120 to ON052124.
Umifenovir Epigenetically Targets the IL-10 Pathway in Therapy against Coxsackievirus B4 Infection

Viral infections are major threats to human health because of their strong association with a variety of inflammation-related diseases, especially cancer. Many antiviral drugs are performing poorly in treating viral infections. ABSTRACT Umifenovir, a broad-spectrum nonnucleoside antiviral drug, has a promising efficacy against coxsackievirus B4 (CVB4) infection, but its mechanism remains unclear. CVB4 is a common human single-stranded RNA virus that belongs to the Picornaviridae family and the Enterovirus genus. Enterovirus can cause severe diseases, such as meningitis, myocarditis, pancreatitis, insulin-dependent diabetes, and several other diseases, in both adults and children. We have previously demonstrated the critical role of interleukin 10 (IL-10) in promoting CVB4 infection and the downregulation of IL-10 by umifenovir. To further explore the underlying mechanisms of umifenovir, we characterized the epigenetic regulation of IL-10 in IL-10 knockout RAW264.7 cells and a BALB/c mouse splenocyte model. Mechanistically, we found that umifenovir inhibited CVB4-activated IL-10 by enhancing the methylation level of the repressive histones H3K9me3 and H3K27me3 while reducing the acetylation level of the activating histone H3K9ac in the promoter region of the IL-10 gene. Furthermore, using a chromosome conformation capture approach, we discovered that CVB4 infection activated the IL-10 gene by forming an intrachromosomal interaction between the IL-10 gene promoter and an intronic enhancer of the downstream MK2 (mitogen-activated protein kinase [MAPK]-activated protein kinase 2 [MAPKAPK2]) gene, a critical component of the p38-MAPK signaling pathway, which is required for IL-10 gene expression. However, umifenovir treatment abolished this spatial conformation and chromatin interaction, thus reducing the continuous expression of IL-10 and subsequent CVB4 replication. In conclusion, this study reveals a novel epigenetic mechanism by which umifenovir controls CVB4 infections, thus laying a theoretical foundation for therapeutic use of umifenovir. IMPORTANCE Viral infections are major threats to human health because of their strong association with a variety of inflammation-related diseases, especially cancer. Many antiviral drugs are performing poorly in treating viral infections. This is probably due to the immunosuppressive effect of highly expressed IL-10, which is caused by viral infection. Umifenovir is a broad-spectrum antiviral drug. Our recent studies showed that umifenovir has a significant inhibitory effect on CVB4 infection and can reduce IL-10 expression caused by CVB4. However, another antiviral drug, rupintrivir, showed good antiviral activity but had no effect on the expression of IL-10. This suggests that the regulation of IL-10 expression is a key part of the antiviral mechanism of umifenovir. Therefore, due to the dual function of the inhibition of CVB4 replication and the regulation of immune antiviral pathway, the mechanism of umifenovir is of great value to study.
Teachers’ Time Use and Affect Before and After COVID-19 School Closures

Several large-scale survey efforts have attempted to understand teachers’ experiences in the early months of the pandemic. Our study complements this literature by providing direct evidence of teachers’ work prior to and after the onset of COVID-19. We leverage unique longitudinal time use and affect data on 131 teachers from one district across the 2019–2020 school year. Specifically, we provide a full accounting of teachers’ instructional activities, their reports of their positive affect and negative affect while engaged in these activities, and the extent to which teachers’ work experiences changed post-COVID. Our results suggest a large reduction in teachers’ daily instructional minutes, which were replaced with increased planning, paperwork, and interactions with colleagues and parents. Teachers’ overall positive and negative affect did not change post-COVID. But teachers’ affective responses to specific work activities did. Post-COVID, we saw increases in teachers’ positive affect when with students.