Miracle-dz/UniFAR-FTUs huggingface dataset:
Chicago's urban forest ecosystem: Results of the Chicago Urban Forest Climate Project. (Includes executive summary). Forest Service general technical report (Final)

Results of the 3-year Chicago Urban Forest Climate Project indicate that there are an estimated 50.8 million trees in the Chicago area of Cook and DuPage Counties; 66 percent of these trees rated in good or excellent condition. During 1991, trees in the Chicago area removed an estimated 6,145 tons of air pollutants, providing air cleansing valued at $9.2 million dollars. These trees also sequester approximately 155,000 tons of carbon per year, and provide residential heating and cooling energy savings that, in turn, reduce carbon emissions from power plants by about 12,600 tons annually. Shade, lower summer air temperatures, and a reduction in windspeed associated with increasing tree cover by 10 percent can lower total heating and cooling energy use by 5 to 10 percent annually ($50 to $90 per dwelling unit). The projected net present value of investment in planting and care of 95,000 trees in Chicago is $38 million ($402 per planted tree), indicating that the long-term benefits of trees are more than twice their costs.
Automatic Control of Reactive Brain Computer Interfaces

This article discusses practical and theoretical aspects of real-time brain computer interface control methods based on Bayesian statistics. We investigate and improve the performance of automatic control and feedback algorithms of a reactive brain computer interface based on a visual oddball paradigm for faster statistical convergence. We introduce transfer learning using Gaussian mixture models, enabling a ready-to-use setup.
Ultima: Robust and Tail-Optimal AllReduce for Distributed Deep Learning in the Cloud

We present Ultima, a new collective-communication system for the cloud with bounded, predictable completion times for deep-learning jobs in the presence of varying computation (stragglers) and communication (congestion and gradient drops) variabilities. Ultima exploits the inherent resiliency and the stochastic nature of distributed deep-learning (DDL) training to work with approximated gradients, and provides an efficient balance between (tail) performance and the resulting accuracy of the trained models. Exploiting this domain-specific characteristic of DDL, Ultima introduces (1) mechanisms (e.g., Transpose AllReduce, unreliable connection-oriented transport, and adaptive timeout) to improve the DDL jobs' tail execution time, and (2) strategies (e.g., Hadamard Transform) to mitigate the impact of gradient drops on model accuracy. Our evaluation shows that Ultima achieves 60% faster time-to-accuracy (TTA), on average, when operating in shared environments (e.g., public cloud), and is on par with existing algorithms (e.g., Ring-AllReduce) in dedicated environments (like HPC).
A Unified Algorithmic Framework for Dynamic Compressive Sensing

We propose a unified dynamic tracking algorithmic framework (PLAY-CS) to reconstruct signal sequences with their intrinsic structured dynamic sparsity. By capitalizing on specific statistical assumptions concerning the dynamic filter of the signal sequences, the proposed framework exhibits versatility by encompassing various existing dynamic compressive sensing (DCS) algorithms. This is achieved through the incorporation of a newly proposed Partial-Laplacian filtering sparsity model, tailored to capture a more sophisticated dynamic sparsity. In practical scenarios such as dynamic channel tracking in wireless communications, the framework demonstrates enhanced performance compared to existing DCS algorithms.
Boosting Learning for LDPC Codes to Improve the Error-Floor Performance

Low-density parity-check (LDPC) codes have been successfully commercialized in communication systems due to their strong error correction capabilities and simple decoding process. However, the error-floor phenomenon of LDPC codes, in which the error rate stops decreasing rapidly at a certain level, presents challenges for achieving extremely low error rates and deploying LDPC codes in scenarios demanding ultra-high reliability. In this work, we propose training methods for neural min-sum (NMS) decoders to eliminate the error-floor effect. First, by leveraging the boosting learning technique of ensemble networks, we divide the decoding network into two neural decoders and train the post decoder to be specialized for uncorrected words that the first decoder fails to correct. Secondly, to address the vanishing gradient issue in training, we introduce a block-wise training schedule that locally trains a block of weights while retraining the preceding block. Lastly, we show that assigning different weights to unsatisfied check nodes effectively lowers the error-floor with a minimal number of weights. By applying these training methods to standard LDPC codes, we achieve the best error-floor performance compared to other decoding methods. The proposed NMS decoder, optimized solely through novel training methods without additional modules, can be integrated into existing LDPC decoders without incurring extra hardware costs. The source code is available at https://github.com/ghy1228/LDPC_Error_Floor .
Human-Centered Evaluation of XAI Methods

In the ever-evolving field of Artificial Intelligence, a critical challenge has been to decipher the decision-making processes within the so-called “black boxes” in deep learning. Over recent years, a plethora of methods have emerged, dedicated to explaining decisions across diverse tasks. Particularly in tasks like image classification, these methods typically identify and emphasize the pivotal pixels that most influence a classifier’s prediction. Interestingly, this approach mirrors human behavior: when asked to explain our rationale for classifying an image, we often point to the most salient features or aspects. Capitalizing on this parallel, our research embarked on a user-centric study. We sought to objectively measure the interpretability of three leading explanation methods: (1) Prototypical Part Network, (2) Occlusion, and (3) Layer-wise Relevance Propagation. Intriguingly, our results highlight that while the regions spotlighted by these methods can vary widely, they all offer humans a nearly equivalent depth of understanding. This enables users to discern and categorize images efficiently, reinforcing the value of these methods in enhancing AI transparency.
Time, Through the Life Course, in the Family

Time, through the Lifecourse, in the Family This paper discusses the way that individuals’ time budgets are influenced by changes in their family status and circumstances. We sometimes associate life course changes in time use patterns, in an unconsidered manner, with chronological ageing. But is it really age itself, or the changing material (particularly family) circumstances associated with ageing, that cause these changes? The ideal approach to answering this question would be a household panel study large enough to provide sufficient instances of the various relevant changes in material circumstances, to model their temporal consequences. However the only available time-diary panel study is too small for this purpose. So this study “fuses” the time diary evidence with evidence from a much larger and long-running national household panel study, using a number of questionnaire items highly correlated with time allocation, and present in both data sets. It uses the combined data set to show how time use is affected by changes in family statuses through the life course. It demonstrates in particular that successive stages in the “family cycle “ have strong effects in increasing gendered specialisation in the distribution of paid and unpaid work. NON-TECHNICAL SUMMARY Time diaries are the preferred method for collecting evidence of time use patterns. But it is not practicable (because of both financial cost and respondent burden) to collect the large time diary samples needed to investigate the effects of family transitions, on a panel survey basis. The alternative is data fusion. We have a time-diary dataset—the Home on-Line (HoL) study—which shares the same questionnaire-derived time-use predictor variables with a large long-term panel study (the BHPS). So we use regression of the predictors on the time diary data in HoL to impute time use in BHPS by multiplying the resulting regression coefficients with the same predictor variables in the BHPS. What emerges is in effect a calibrated index of time-use patterns based on BHPS questionnaire items. We have thus been able to use real “longitudinal” evidence—repeated measurements of the same subjects—to estimate how time-use patterns change with family transitions such as partnership formation, childbirth and divorce. It emerges that: • there is relatively slow change in time allocation patterns if family status is unchanged; • the largest changes seem to occur during the two years surrounding a family transition; • and family transition effects are in general larger for younger people than for older. Gender differences increase through the family cycle, with small differences in paid work before partnership formation, and increasing specialisation into “traditional” roles—men in paid work, women in unpaid—after childbirth. Within broad age-categories, what might appear as continuous age-related changes, can alternatively be interpreted as consequences of family status changes.
Many-body entanglement and spectral clusters in the extended hard-core bosonic Hatano-Nelson model

We study the many-body entanglement and spectra of the extended bosonic Hatano-Nelson model in the hard-core limit. We first show the quantum entanglement entropy can describe phase transitions for both the ground state and the first excited state. The phase transition of the ground state is second-order with the central charge $c=1$. While the phase transition of the first excited state is the first-order $\mathcal{PT}$ transition associated with a sudden change of the entanglement entropy. Secondly, we show that all energy spectral clusters would form ellipses in strong nearest-neighbor interactions, for which we establish a universal scaling law. The lengths of the major and minor axes are shown to obey power laws with respect to the nearest-neighbor interaction. Finally, we derive exact expressions for the numbers of energy levels on the outermost elliptic ring of each clusters.
Certainty and uncertainty in understanding global warming

Global warming has greatly concerned the whole world. Owing to the limitation we currently have, it is still difficult to completely understand the mechanism and physical science of climate change. Now both certainty and uncertainty coexist in the understanding of climate warming. This paper aims to summarize certainties and uncertainties in climate-warming studies, which focus on seven key problems related to human activities, namely, global warming, atmospheric concentrations of greenhouse gases, relationship between greenhouse gas emissions and climate warming, climate models, future climate change, 2°C warming threshold and tipping point in the Earth’s system. We should comprehensively take into account the level of certainty and uncertainty in our understanding of climate change while adapting to and mitigating global warming and adjusting our industrial structures accordingly. This would allow us to respond to change with certainty, while avoiding the risks associated with uncertainty.
Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images

MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these therapies include lack of access to validated testing, such as standardized immunohistochemistry (IHC) assessment, and consumption of valuable tissue for a single gene/protein assay. Development of pre-screening algorithms using routinely available digitized hematoxylin and eosin (H&E)-stained slides to predict MET overexpression could promote testing for those who will benefit most. While assessment of MET expression using IHC is currently not routinely performed in NSCLC, next-generation sequencing is common and in some cases includes RNA expression panel testing. In this work, we leveraged a large database of matched H&E slides and RNA expression data to train a weakly supervised model to predict MET RNA overexpression directly from H&E images. This model was evaluated on an independent holdout test set of 300 over-expressed and 289 normal patients, demonstrating an ROC-AUC of 0.70 (95th percentile interval: 0.66 - 0.74) with stable performance characteristics across different patient clinical variables and robust to synthetic noise on the test set. These results suggest that H&E-based predictive models could be useful to prioritize patients for confirmatory testing of MET protein or MET gene expression status.
Classi(cid:12)cation of Dysarthria based on the Levels of Severity. A Systematic Review

Dysarthria is a neurological speech disorder that can significantly impact affected individuals’ communication abilities and overall quality of life. The accurate and objective classification of dysarthria and the determination of its severity are crucial for effective therapeutic intervention. While traditional assessments by speech-language pathologists (SLPs) are common, they are often subjective, time-consuming, and can vary between practitioners. Emerging machine learning-based models have shown the potential to provide a more objective dysarthria assessment, enhancing diagnostic accuracy and reliability. This systematic review aims to comprehensively analyze current methodologies for classifying dysarthria based on severity levels. Specifically, this review will focus on determining the most effective set and type of features that can be used for automatic patient classification and evaluating the best AI techniques for this purpose. We will systematically review the literature on the automatic classification of dysarthria severity levels. Sources of information will include electronic databases and grey literature. Selection criteria will be established based on relevance to the research questions. Data extraction will include methodologies used, the type of features extracted for classification, and AI techniques employed. The findings of this systematic review will contribute to the current understanding of dysarthria classification, inform future research, and support the development of improved diagnostic tools. The implications of these findings could be significant in advancing patient care and improving therapeutic outcomes for individuals affected by dysarthria.
Comparing Styles across Languages

Understanding how styles differ across languages is advantageous for training both humans and computers to generate culturally appropriate text. We introduce an explanation framework to extract stylistic differences from multilingual LMs and compare styles across languages. Our framework (1) generates comprehensive style lexica in any language and (2) consolidates feature importances from LMs into comparable lexical categories. We apply this framework to compare politeness, creating the first holistic multilingual politeness dataset and exploring how politeness varies across four languages. Our approach enables an effective evaluation of how distinct linguistic categories contribute to stylistic variations and provides interpretable insights into how people communicate differently around the world.
RLaGA: A Reinforcement Learning Augmented Genetic Algorithm For Searching Real and Diverse Marker-Based Landing Violations

Automated landing for Unmanned Aerial Vehicles (UAVs), like multirotor drones, requires intricate software encompassing control algorithms, obstacle avoidance, and machine vision, especially when landing markers assist. Failed landings can lead to significant costs from damaged drones or payloads and the time spent seeking alternative landing solutions. Therefore, it's important to fully test auto-landing systems through simulations before deploying them in the real-world to ensure safety. This paper proposes RLaGA, a reinforcement learning (RL) augmented search-based testing framework, which constructs diverse and real marker-based landing cases that involve safety violations. Specifically, RLaGA introduces a genetic algorithm (GA) to conservatively search for diverse static environment configurations offline and RL to aggressively manipulate dynamic objects' trajectories online to find potential vulnerabilities in the target deployment environment. Quantitative results reveal that our method generates up to 22.19% more violation cases and nearly doubles the diversity of generated violation cases compared to baseline methods. Qualitatively, our method can discover those corner cases which would be missed by state-of-the-art algorithms. We demonstrate that select types of these corner cases can be confirmed via real-world testing with drones in the field.
On k-vertex-edge domination of graph

Let $G=(V,E)$ be a simple undirected graph. The open neighbourhood of a vertex $v$ in $G$ is defined as $N_G(v)=\{u\in V~|~ uv\in E\}$; whereas the closed neighbourhood is defined as $N_G[v]= N_G(v)\cup \{v\}$. For an integer $k$, a subset $D\subseteq V$ is called a $k$-vertex-edge dominating set of $G$ if for every edge $uv\in E$, $|(N_G[u]\cup N_G[v]) \cap D|\geq k$. In $k$-vertex-edge domination problem, our goal is to find a $k$-vertex-edge dominating set of minimum cardinality of an input graph $G$. In this paper, we first prove that the decision version of $k$-vertex-edge domination problem is NP-complete for chordal graphs. On the positive side, we design a linear time algorithm for finding a minimum $k$-vertex-edge dominating set of tree. We also prove that there is a $O(\log(\Delta(G)))$-approximation algorithm for this problem in general graph $G$, where $\Delta(G)$ is the maximum degree of $G$. Then we show that for a graph $G$ with $n$ vertices, this problem cannot be approximated within a factor of $(1-\epsilon) \ln n$ for any $\epsilon>0$ unless $NP\subseteq DTIME(|V|^{O(\log\log|V|)})$. Finally, we prove that it is APX-complete for graphs with bounded degree $k+3$.
What can knowledge graph alignment gain with Neuro-Symbolic learning approaches?

Knowledge Graphs (KG) are the backbone of many data-intensive applications since they can represent data coupled with its meaning and context. Aligning KGs across different domains and providers is necessary to afford a fuller and integrated representation. A severe limitation of current KG alignment (KGA) algorithms is that they fail to articulate logical thinking and reasoning with lexical, structural, and semantic data learning. Deep learning models are increasingly popular for KGA inspired by their good performance in other tasks, but they suffer from limitations in explainability, reasoning, and data efficiency. Hybrid neurosymbolic learning models hold the promise of integrating logical and data perspectives to produce high-quality alignments that are explainable and support validation through human-centric approaches. This paper examines the current state of the art in KGA and explores the potential for neurosymbolic integration, highlighting promising research directions for combining these fields.
Multidimensional Hopfield Networks for clustering

We present the Multidimensional Hopfield Network (DHN), a natural generalisation of the Hopfield Network. In our theoretical investigations we focus on DHNs with a certain activation function and provide energy functions for them. We conclude that these DHNs are convergent in finite time, and are equivalent to greedy methods that aim to find graph clusterings of locally minimal cuts. We also show that the general framework of DHNs encapsulates several previously known algorithms used for generating graph embeddings and clusterings. Namely, the Cleora graph embedding algorithm, the Louvain method, and the Newmans method can be cast as DHNs with appropriate activation function and update rule. Motivated by these findings we provide a generalisation of Newmans method to the multidimensional case.
Iterative solution of relativistic Boltzmann equation in curved spacetime with application to kinetic coefficients

Under relaxation time approximation, we obtain an iterative solution to the relativistic Boltzmann equation in generic stationary spacetime. This solution provides a scheme to study non-equilibrium system order by order. As a specific example, we analytically calculated the covariant expressions of the particle flow and the energy momentum tensor up to the first order in relaxation time. Finally and most importantly, we present all 14 kinetic coefficients for a neutral system, which are verified to satisfy the Onsager reciprocal relation and guarantee a non-negative entropy production.
The impact when neural min-sum variant meets ordered statistics decoding of LDPC codes

This paper introduces three key initiatives in the pursuit of a hybrid decoding framework characterized by superior decoding performance, high throughput, low complexity, and independence from channel noise variance. Firstly, adopting a graphical neural network perspective, we propose a design methodology for a family of neural min-sum variants. Our exploration delves into the frame error rates associated with different decoding variants and the consequential impact of decoding failures on subsequent ordered statistics decoding. Notably, these neural min-sum variants exhibit generally indistinguishable performance, hence the simplest member is chosen as the constituent of the hybrid decoding. Secondly, to address computational complexities arising from exhaustive searches for authentic error patterns in cases of decoding failure, two alternatives for ordered statistics decoding implementation are proposed. The first approach involves uniformly grouping test error patterns, while the second scheme dynamically generates qualified searching test error patterns with varied sizes for each group. In both methods, group priorities are determined empirically. Thirdly, iteration diversity is highlighted in the case of LDPC codes requiring high maximum iterations of decoding. This is achieved by segmenting the long iterative decoding trajectory of a decoding failure into shorter segments, which are then independently fed to small models to enhance the chances of acquiring the authentic error pattern. These ideas are substantiated through extensive simulation results covering the codes with block lengths ranging from one hundred to several hundreds.