Miracle-dz/UniFAR-FTUs huggingface dataset:
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion

Distributional reinforcement learning algorithms have attempted to utilize estimated uncertainty for exploration, such as optimism in the face of uncertainty. However, using the estimated variance for optimistic exploration may cause biased data collection and hinder convergence or performance. In this paper, we present a novel distributional reinforcement learning algorithm that selects actions by randomizing risk criterion to avoid one-sided tendency on risk. We provide a perturbed distributional Bellman optimality operator by distorting the risk measure and prove the convergence and optimality of the proposed method with the weaker contraction property. Our theoretical results support that the proposed method does not fall into biased exploration and is guaranteed to converge to an optimal return. Finally, we empirically show that our method outperforms other existing distribution-based algorithms in various environments including Atari 55 games.
Identifying Reasons for Bias: An Argumentation-Based Approach

As algorithmic decision-making systems become more prevalent in society, ensuring the fairness of these systems is becoming increasingly important. Whilst there has been substantial research in building fair algorithmic decision-making systems, the majority of these methods require access to the training data, including personal characteristics, and are not transparent regarding which individuals are classified unfairly. In this paper, we propose a novel model-agnostic argumentation-based method to determine why an individual is classified differently in comparison to similar individuals. Our method uses a quantitative argumentation framework to represent attribute-value pairs of an individual and of those similar to them, and uses a well-known semantics to identify the attribute-value pairs in the individual contributing most to their different classification. We evaluate our method on two datasets commonly used in the fairness literature and illustrate its effectiveness in the identification of bias.
On Pixel-level Performance Assessment in Anomaly Detection

Anomaly detection methods have demonstrated remarkable success across various applications. However, assessing their performance, particularly at the pixel-level, presents a complex challenge due to the severe imbalance that is most commonly present between normal and abnormal samples. Commonly adopted evaluation metrics designed for pixel-level detection may not effectively capture the nuanced performance variations arising from this class imbalance. In this paper, we dissect the intricacies of this challenge, underscored by visual evidence and statistical analysis, leading to delve into the need for evaluation metrics that account for the imbalance. We offer insights into more accurate metrics, using eleven leading contemporary anomaly detection methods on twenty-one anomaly detection problems. Overall, from this extensive experimental evaluation, we can conclude that Precision-Recall-based metrics can better capture relative method performance, making them more suitable for the task.
Measuring and Inferring Demographics From Multi-Mode Communication Networks

With the proliferation of electronic modes of communication (e.g., e-mails, phone calls and short messages), a group of people can form several distinct communication networks. A communication network is essentially a graph representation of who talks (or texts) to whom among a group of individuals. In this thesis, we conduct an empirical study of communication networks acorss two modern countries and focus on four questions: 1) what are the patterns of multimodal communication across countries and over time? 2) how much information can we extract regarding the roles of users? 3) can we infer the demographic properties of certain users? 4) can we predict the phone choices for a group of users? For the first question, I study the correlation between calling and texting across two countries: China and the U.S., and the evolution of the usage of the two communications over the last five years. I propose to depend on communication channels (calling and texting) and time- slices (weekday, weekend, and holiday) to study how people in China and the U.S. contact one another. This idea is inspired by the fact that different human relationships can be indicated by communications in different time slicess. For example, texting tends to indicate a friendship, while calling on a weekday morning shows a relationship between colleagues. For the second question, I examine the effect of communication channels on title prediction. I first show the similarity and differences in calling and texting between managers and ordinary employees and then propose a ranking algorithm, called HumanRank, which infers job title with 10% higher accuracy than existing methods. I discuss how the texting graph is 10% better at title prediction than the calling graph. For the third question, I explore the effect of time slices on the prediction of age group and income level on call networks by studying the correlation between calling features and the demographic homophily and then proposing a prediction algorithm to reach an accuracy of 80% for age group and 71% for income level. Moreover, I discuss how the weekday graph is 15% better than the night-weekend graph regarding the prediction accuracy. for the fourth question, I study correlations between demographics and phone preference. With the features emerging from the correlations, I devise a solution to infer a user's phone choice. Compared with existing methods, my solution reduces the error by 1/3 and related costs by half.
Dynamic Processing Neural Network Architecture for Hearing Loss Compensation

This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech intelligibility after further processing by a person with a hearing impairment, which is modeled by a hearing loss model. We propose an interpretable model called dynamic processing network, which has a structure similar to band-wise dynamic compressor. The network is differentiable, and therefore allows to learn its parameters to maximize speech intelligibility. More generic models based on convolutional layers were tested as well. The performance of the tested architectures was assessed using spectro-temporal objective index (STOI) with hearing-threshold noise and hearing aid speech intelligibility (HASPI) metrics. The dynamic processing network gave a significant improvement of STOI and HASPI in comparison to popular compressive gain prescription rule Camfit. A large enough convolutional network could outperform the interpretable model with the cost of larger computational load. Finally, a combination of the dynamic processing network with convolutional neural network gave the best results in terms of STOI and HASPI.
Neural Potential Field for Obstacle-Aware Local Motion Planning

Model predictive control (MPC) may provide local motion planning for mobile robotic platforms. The challenging aspect is the analytic representation of collision cost for the case when both the obstacle map and robot footprint are arbitrary. We propose a Neural Potential Field: a neural network model that returns a differentiable collision cost based on robot pose, obstacle map, and robot footprint. The differentiability of our model allows its usage within the MPC solver. It is computationally hard to solve problems with a very high number of parameters. Therefore, our architecture includes neural image encoders, which transform obstacle maps and robot footprints into embeddings, which reduce problem dimensionality by two orders of magnitude. The reference data for network training are generated based on algorithmic calculation of a signed distance function. Comparative experiments showed that the proposed approach is comparable with existing local planners: it provides trajectories with outperforming smoothness, comparable path length, and safe distance from obstacles. Experiment on Husky UGV mobile robot showed that our approach allows real-time and safe local planning. The code for our approach is presented at https://github.com/cog-isa/NPField together with demo video.
Detection of news written by the ChatGPT through authorship attribution performed by a Bidirectional LSTM model

The large language based-model chatbot ChatGPT gained a lot of popularity since its launch and has been used in a wide range of situations. This research centers around a particular situation, when the ChatGPT is used to produce news that will be consumed by the population, causing the facilitation in the production of fake news, spread of misinformation and lack of trust in news sources. Aware of these problems, this research aims to build an artificial intelligence model capable of performing authorship attribution on news articles, identifying the ones written by the ChatGPT. To achieve this goal, a dataset containing equal amounts of human and ChatGPT written news was assembled and different natural processing language techniques were used to extract features from it that were used to train, validate and test three models built with different techniques. The best performance was produced by the Bidirectional Long Short Term Memory (LSTM) Neural Network model, achiving 91.57\% accuracy when tested against the data from the testing set.
CR-COPEC: Causal Rationale of Corporate Performance Changes to Learn from Financial Reports

In this paper, we introduce CR-COPEC called Causal Rationale of Corporate Performance Changes from financial reports. This is a comprehensive large-scale domain-adaptation causal sentence dataset to detect financial performance changes of corporate. CR-COPEC contributes to two major achievements. First, it detects causal rationale from 10-K annual reports of the U.S. companies, which contain experts' causal analysis following accounting standards in a formal manner. This dataset can be widely used by both individual investors and analysts as material information resources for investing and decision making without tremendous effort to read through all the documents. Second, it carefully considers different characteristics which affect the financial performance of companies in twelve industries. As a result, CR-COPEC can distinguish causal sentences in various industries by taking unique narratives in each industry into consideration. We also provide an extensive analysis of how well CR-COPEC dataset is constructed and suited for classifying target sentences as causal ones with respect to industry characteristics. Our dataset and experimental codes are publicly available.
Assessing the overall and partial causal well-specification of nonlinear additive noise models

We propose a method to detect model misspecifications in nonlinear causal additive and potentially heteroscedastic noise models. We aim to identify predictor variables for which we can infer the causal effect even in cases of such misspecification. We develop a general framework based on knowledge of the multivariate observational data distribution. We then propose an algorithm for finite sample data, discuss its asymptotic properties, and illustrate its performance on simulated and real data.
Experimental Study on Flow around Blades of Horizontal Axis Wind Turbine in Wind Tunnel

This paper describes the measurement of the flow field around rotor blade. Three-bladed upwind rotor was tested in an open jet type wind tunnel. The rotor has a diameter of 2.4 m. Flow field around rotor blade was measured with the use of two-dimensional LDV. The flow field was measured in x-y plane and z-y plane. The circulation around the blade sections were calculated by flow vectors around the rotor blade. The velocity vectors at optimum operation show a smooth flow around the blade and the bound vortex around blade cross-section seems to be persistent. On the other hand, the velocity vectors at stall condition demonstrate significant fluctuations in the near wake and separation on the blade suction side was observed. The circulation around blade span-wise section was calculated at the certain control volume. By the observation of flow field and calculated results of circulation, it seems that the flow is separated at the blade from middle-span region to tip region at stall condition. No separation was observed at the blade root region.
Controlled butchery observations as a means for interpreting Okote member hominin carnivory at Koobi Fora, Kenya

This set of experiments investigates the stone tool characteristics and contextual butchery factors that influence cut mark width and depth, measured from cross-sections of bone surface molds, to test whether cut mark size is useful in inferring tool identity. A pastoralist experienced in stone tool butchery used replicated Oldowan flake and bifacial core tools in experimental trials that isolated skinning, bulk and scrap muscle defleshing and element disarticulation cut marks on goat and cow skeletons. This large experimental sample explores the cut mark traces generated under realistic butchery scenarios and suggests the following results: core tools were significantly heavier and had more obtuse edges than flake tools, but both tool classes were equally efficient at completing all butchery tasks in goats and cows. Distributions of flake and core cut mark width and depth in the total sample could not be distinguished, but skinning and disarticulation produced significantly wider and deeper marks than defleshing. Cut marks on cows tended to be wider and deeper than those on goats. When the sample of experimental cut marks was stratified to remove the confounding influence of butchery action, carcass size and bone portion on cut mark cross section, tool weight had a strong, positive correlation with average defleshing cut mark width only in the cow sample. Cut mark size is similar on elements butchered early and late in the butchery sequence. Bone portion density is negatively associated with cut mark width per portion in both size classes. Because the numerous butchery contingencies affecting cut mark size on archaeological specimens
Needle terpenoid composition may affect the infestation of the European larch by the larch wooly adelgid

The larch wooly adelgid Adelges laricis Vallot (Hemiptera Adelgidae) infestation has a damaging effect on the European larch Larix decidua Miller. In the present study, we found significant differences in A. laricis population number between individual larch trees. On the scarcely infested tree T1, few A. laricis exulis progrediens sessile nymphs/adult females and winged sexuparae oc-curred incidentally in mid-May and at the end of June. On the heavily infested tree T2, we found all developmental stages of A. laricis typical of the secondary host: the exulis sistens sessile nymphs/females, exulis progrediens sessile nymphs/females, L1 crawlers, and winged sexuparae . The population of A. laricis on T2 developed from the beginning of April, the maximum number occurred at the end of May, and the decline - in the end of July, which coincided with the migration of winged sexuparae to the primary host, the spruce Picea abies (L.) H. Karsten. We studied the qualitative and quantitative content of mono-and sesquiterpe-noids in the needles of T1 and T2, as these allelochemicals play a major role in conifer defence against herbivores. Thirty mono-and sesquiterpenoids were identified using GC-MS. The amounts of verbenone, isobornyl acetate, and camphene were 42, 10, and 5 times higher in T1 than in T2, respectively. While the relative contents of verbenone and camphene were 3.4% vs. 0.0% and 6.0% vs. 1.8% in T1 vs. T2, respectively, the content of isobornyl acetate was 28% vs. 4.0%. It is likely that the relatively high content of isobornyl acetate in the needles is an important component of antixenosis resistance in L. decidua against A. laricis . The results of this study may contribute to future investigations on herbivore resistance in conifers and may appear helpful in designing sustainable control strategies in forests and in urban environments.
Modelling nitrogen and phosphorus export by the Pearl River in China 1970-2050

China is a country with a rapid economic development and fast population growth. This is causing environmental pollution. Eutrophication has been a problem in the coastal waters of southern China since the 1970s. This is a result of increased nitrogen (N) and phosphorus (P) inputs from rivers such as the Pearl River (Zhujiang). The Pearl River is the third largest river in China. It serves as an important water supplier for irrigation and human water consumption. Human activities on land, however, have increased the nutrient content of the Pearl River water considerably. The objective of our study is to quantify the relative contribution of the anthropogenic and non-anthropogenic sources of dissolved inorganic N and P export by the Pearl River to coastal seas. We describe a method to model dissolved N and P export at the sub-basin scale. Results indicate that in 2000 about 90% of the dissolved N and P inputs exported by the Pearl River to the coastal waters were from anthropogenic sources (e.g. agriculture, sewage, and biological N2-fixation by agricultural crops). In the future the relative share of anthropogenic sources may increase. We show that nutrient inputs from land to rivers vary among sub-basins. We conclude that modelling and scenario analyses may support the formulation of effective strategies to reduce N and P export by the Pearl River, and thus to effective management of coastal eutrophication in the coming decades.
METTL3 promotes choriocarcinoma progression by activating the miR‐935/GJA1 pathway in an m6A‐dependent manner

The emerging role of microRNA‐935 (miR‐935) in modulating cancer progression has been recognized. However, its role in regulating choriocarcinoma (CCA) development and progression remains unknown. The present work aims to reveal the effect of miR‐935 on CCA cell tumor properties and the related mechanism. The RNA expression of methyltransferase 3, N6‐adenosine‐methyltransferase complex catalytic subunit (METTL3), miR‐935, and gap junction protein alpha 1 (GJA1) was detected by quantitative real‐time polymerase chain reaction. Protein expression of GJA1, Ki67, and METTL3 was measured by western blotting and immunohistochemistry assays. CCK‐8 and colony formation were used to analyze cell proliferation. Transwell assays were performed to assess cell migration and invasion. Angiogenesis was investigated by tube formation assay. Xenograft mouse model assay was used to determine miR‐935‐mediated effect on tumor formation in vivo. The luciferase reporter assay and RNA pull‐down assay were used to verify the relationship between miR‐935 and GJA1. MeRIP assay was used to analyze the m6A methylation of pri‐miR‐935. MiR‐935 expression was significantly upregulated in CCA tissues and cells when compared with control groups. MiR‐935 overexpression promoted CCA cell proliferation, migration, invasion, and tube formation and tumor tumorigenesis in vitro and in vivo, but miR‐935 knockdown showed the opposite effects. In addition, miR‐935 targeted GJA1 and mediated CCA cell tumor properties by negatively regulating GJA1 expression. METTL3 promoted miR‐935 maturation by inducing m6A methylation of pri‐miR‐935, and its overexpression contributed to CCA cell tumor properties through the regulation of miR‐935. METTL3 promoted choriocarcinoma progression by m6A‐dependently activating the miR‐935/GJA1 pathway.
A Simple Eddy Potential Vorticity Flux Closure

It is shown that within the limits of quasi‐geostrophic scaling, biharmonic mixing of potential vorticity is equivalent to biharmonic lateral friction together with biharmonic isopycnal thickness mixing in primitive equations, all with the same viscosity. The problem of unbalanced zonal forces related to the planetary vorticity gradient disappears using a biharmonic instead of a harmonic closure. The former is appropriate for eddy‐permitting ocean models which are replacing now more and more the non‐eddy‐resolving model versions in coupled state‐of‐the‐art climate models. Numerical experiments demonstrate that including biharmonic thickness mixing has some benefits regarding stronger eddy production in marginally eddying ocean models, in particular when the grid size is larger than the baroclinic Rossby radius. Going to finer grids where relative vorticity dominates, however, using only biharmonic friction becomes similar to biharmonic mixing of potential vorticity, justifying the long‐standing use of biharmonic friction in reasonably well resolved eddy‐permitting ocean models.
University of Birmingham Functional diversity and community convergence of land snails in the Aegean Sea islands

Aim: Island biological communities are considered to comprise non-random assemblages from surrounding source pools, but whether they converge towards predictable structural properties remains unclear. Here, we (i) test whether insular communities of land snails converge towards similar functional and/or taxonomic properties and (ii) evaluate whether island functional diversity is determined by island biogeographical characteristics such as area and distance to the pool as well as human-related variables. Location: Sixty-six continental Aegean islands. Taxon: Land snails. Methods: We compiled a database of two morphological traits with functional significance (shell height and width) for 163 island species and 1529 species from the major species pools. We quantified inter-specific morphological dissimilarity between pairs of islands (turnover), using a modified index of the mean nearest taxon distance. We tested for functional and taxonomic convergence using null models and assessing whether overall mean turnover among islands and pairwise island-by-island turnover were lower than expected by chance. We performed multiple regression analyses to test whether functional diversity metrics scale with island biogeographical characteristics and human-related variables.
ZNF714 Supports Pro-Oncogenic Features in Lung Cancer Cells

Despite the ongoing progress in diagnosis and treatments, cancer remains a threat to more than one-third of the human population. The emerging data indicate that many Krüppel-associated box zinc finger proteins (KRAB-ZNF) belonging to a large gene family may be involved in carcinogenesis. Our previous study identified Zinc Finger Protein 714 (ZNF714), a KRAB-ZNF gene of unknown function, as being commonly overexpressed in many tumors, pointing to its hypothetical oncogenic role. Here, we harnessed The Cancer Genome Atlas (TCGA)-centered databases and performed functional studies with transcriptomic and methylomic profiling to explore ZNF714 function in cancer. Our pan-cancer analyses confirmed frequent ZNF714 overexpression in multiple tumors, possibly due to regional amplification, promoter hypomethylation, and Nuclear Transcription Factor Y Subunit Beta (NFYB) signaling. We also showed that ZNF714 expression correlates with tumor immunosuppressive features. The in vitro studies indicated that ZNF714 expression positively associates with proliferation, migration, and invasion. The transcriptomic analysis of ZNF714 knocked-down cells demonstrated deregulation of cell adhesion, migration, proliferation, apoptosis, and differentiation. Importantly, we provided evidence that ZNF714 negatively regulates the expression of several known TSGs indirectly via promoter methylation. However, as ZNF714 did not show nuclear localization in our research model, the regulatory mechanisms exerted by ZNF714 require further investigation. In conclusion, our results reveal, for the first time, that ZNF714 may support pro-oncogenic features in lung cancer cells.