Miracle-dz/UniFAR-FTUs huggingface dataset:
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data

Detecting out-of-distribution (OOD) data is a task that is receiving an increasing amount of research attention in the domain of deep learning for computer vision. However, the performance of detection methods is generally evaluated on the task in isolation, rather than also considering potential downstream tasks in tandem. In this work, we examine selective classification in the presence of OOD data (SCOD). That is to say, the motivation for detecting OOD samples is to reject them so their impact on the quality of predictions is reduced. We show under this task specification, that existing post-hoc methods perform quite differently compared to when evaluated only on OOD detection. This is because it is no longer an issue to conflate in-distribution (ID) data with OOD data if the ID data is going to be misclassified. However, the conflation within ID data of correct and incorrect predictions becomes undesirable. We also propose a novel method for SCOD, Softmax Information Retaining Combination (SIRC), that augments softmax-based confidence scores with feature-agnostic information such that their ability to identify OOD samples is improved without sacrificing separation between correct and incorrect ID predictions. Experiments on a wide variety of ImageNet-scale datasets and convolutional neural network architectures show that SIRC is able to consistently match or outperform the baseline for SCOD, whilst existing OOD detection methods fail to do so.
Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning

Understanding non-linear relationships among financial instru-ments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require hand-crafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such hand-crafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management.
Does Twitter know your political views? POLiTweets dataset and semi-automatic method for political leaning discovery

Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users’ personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 10 posts addressing important or controversial topics for society are enough to predict one’s political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.
Surround with Depth on First-Order Beam-Controlling Loudspeakers

Surround systems are typically based on fixed-directivity loudspeakers pointing towards the listener. Laitinen et al. showed for a variable-directivity loudspeaker that directivity control can be used to influence the distance impression of the reproduced sound. As we have shown in a listening experiment, using beam-controlling loudspeakers, stable auditory events at directions additional to the loudspeaker positions can be created by exciting specific wall reflections. We use these two effects to enable distance control and increase the number of effective surround directions in two different surround setups. We present IIR filter design derived from a physical model, which achieves low frequency beam-control for our novel cube-shaped 4-channel loudspeakers.
Quantitative Stock Investment by Routing Uncertainty-Aware Trading Experts: A Multi-Task Learning Approach

Quantitative investment is a fundamental financial task that highly relies on accurate stock prediction and profitable investment decision making. Despite recent advances in deep learning (DL) have shown stellar performance on capturing trading opportunities in the stochastic stock market, we observe that the performance of existing DL methods is sensitive to random seeds and network initialization. To design more profitable DL methods, we analyze this phenomenon and find two major limitations of existing works. First, there is a noticeable gap between accurate financial predictions and profitable investment strategies. Second, investment decisions are made based on only one individual predictor without consideration of model uncertainty, which is inconsistent with the workflow in real-world trading firms. To tackle these two limitations, we first reformulate quantitative investment as a multi-task learning problem. Later on, we propose AlphaMix, a novel two-stage mixture-of-experts (MoE) framework for quantitative investment to mimic the efficient bottom-up trading strategy design workflow of successful trading firms. In Stage one, multiple independent trading experts are jointly optimized with an individual uncertainty-aware loss function. In Stage two, we train neural routers (corresponding to the role of a portfolio manager) to dynamically deploy these experts on an as-needed basis. AlphaMix is also a universal framework that is applicable to various backbone network architectures with consistent performance gains. Through extensive experiments on long-term real-world data spanning over five years on two of the most influential financial markets (US and China), we demonstrate that AlphaMix significantly outperforms many state-of-the-art baselines in terms of four financial criteria.
RESECT-SEG: Open access annotations of intra-operative brain tumor ultrasound images

Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images play an essential role in surgical planning and resection of brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique annotation dataset of tumor tissues and resection cavities from the previously published RESECT dataset [1] to encourage a more rigorous assessments of image processing techniques. Acquisition and validation methods: The RESECT database [1] consists of MR and intraoperative US (iUS) images of 23 patients who underwent resection surgeries. The proposed dataset contains tumor tissues and resection cavity annotations of the iUS images. The quality of annotations were validated by two highly experienced neu-rosurgeons
Holographic Meissner effect

The holographic superconductor is the holographic dual of superconductivity, but there is no Meissner eﬀect in the standard holographic superconductor. This is because the boundary Maxwell ﬁeld is added as an external source and is not dynamical. We show the Meissner eﬀect analytically by imposing the semiclassical Maxwell equation on the AdS boundary. Unlike the Ginzburg-Landau (GL) theory, the extreme Type I limit cannot be reached even in the e → ∞ limit where e is the U (1) coupling of the boundary Maxwell ﬁeld. This is due to the bound current which is present even in the pure bulk Maxwell theory. In the bulk 5-dimensional case, the GL parameter and the dual GL theory are obtained analytically for the order parameter of scaling dimension 2.
OASYS: Domain-Agnostic Automated System for Constructing Knowledge Base from Unstructured Text

In recent years, creating and managing knowledge bases have become crucial to the retail product and enterprise domains. We present an automatic knowledge base construction system that mines data from documents. This system can generate training data during the training process without human intervention. Therefore, it is domain-agnostic trainable using only the target domain text corpus and a pre-defined knowledge base. This system is called OASYS and is the first system built with the Korean language in mind. In addition, we also have constructed a new human-annotated benchmark dataset of the Korean Wikipedia corpus paired with a Korean DBpedia to aid system evaluation. The system performance results on human-annotated benchmark test dataset are meaningful and show that the generated knowledge base from OASYS trained on only auto-generated data is useful. We provide both a human-annotated test dataset and an auto-generated dataset.
Photometric survey of the red giant V449 Cygni over half a century (1974 to 2022). A semi-regular variable star with a period of ~54 days

: This study sums up a major part of the red giant V449 Cygni measurements for the years 1974 through 2022, obtained by visual and CCD GEOS observers, the Unione Astrofili Italiani, and various automatized telescopes. It appears that the light variations of V449 Cyg in the years 1970-1990 corresponded more to an L-type star according to the GCVS classification, with irregular amplitude variations, sometimes marked by a quasi-periodicity of about 100 days. Since the mid-1990s, these variations have become more regular, with an amplitude of about 0.7 magnitude in V, and a period of about 54±1 days, as shown by various period search routines applied to the best recent CCD series. A longer period of about 2000 days is possible to describe the long term variation of V449 Cyg. Therefore, V449 Cyg appears to be an SRB-type star.
Metal to Wigner-Mott insulator transition in two-leg ladders

We study theoretically the quantum phase transition from a metal to a Wigner-Mott insulator at fractional commensurate filling on a two-leg ladder. We show that a continuous transition out of a symmetry-preserving Luttinger liquid metal is possible where the onset of insulating behavior is accompanied by the breaking of the lattice translation symmetry. At fillings $\nu = 1/m$ per spin per unit cell, we find that the spin degrees of freedom also acquire a gap at the Wigner-Mott transition for odd integer $m$. In contrast for even integer $m$, the spin sector remains gapless and the resulting insulator is a ladder analog of the two-dimensional spinon surface state. In both cases, a charge neutral spinless mode remains gapless across the Wigner-Mott transition. We discuss physical properties of these transitions, and comment on insights obtained for thinking about continuous Wigner-Mott transitions in two-dimensional systems which are being studied in moire materials.
First-order logic with self-reference

We consider an extension of ﬁrst-order logic with a recursion operator that corresponds to allowing formulas to refer to themselves. We investigate the obtained language under two diﬀerent systems of semantics, thereby obtaining two closely related but diﬀerent logics. We provide a natural deduction system that is complete for validities for both of these logics, and we also investigate a range of related basic decision problems. For example, the validity problems of the two-variable fragments of the logics are shown coNexpTime-complete, which is in stark contrast with the high undecidability of two-variable logic extended with least ﬁxed points. We also argue for the naturalness and beneﬁts of the investigated approach to recursion and self-reference by, for example, relating the new logics to Lindstr¨om’s Second Theorem.
Sequestration of carbon in the deep Atlantic during the last glaciation

Atmospheric CO2 concentrations declined markedly about 70,000 years ago, when the Earth's climate descended into the last glaciation. Much of the carbon removed from the atmosphere has been suspected to have entered the deep oceans, but evidence for increased carbon storage remains elusive. Here we use the B/Ca ratios of benthic foraminifera from several sites across the Atlantic Ocean to reconstruct changes in the carbonate ion concentration and hence the carbon inventory of the deep Atlantic across this transition. We find that deep Atlantic carbonate ion concentration declined by around 25 mu mol kg(1) between similar to 80,000 and 65,000 years ago. This drop implies that the deep Atlantic carbon inventory increased by at least 50 Gt around the same time as the amount of atmospheric carbon dropped by about 60 Gt. From a comparison with proxy records of deep circulation and climate model simulations, we infer that the carbon sequestration coincided with a shoaling of the Atlantic meridional overturning circulation. We thus conclude that changes in the Atlantic Ocean circulation may have played an important role in reductions of atmospheric CO2 concentrations during the last glaciation, by increasing the carbon storage in the deep Atlantic. 2 this transition. We find that deep Atlantic carbonate ion concentration declined by around 30 25 mol kg-1 between ~80,000 and 65,000 years ago. This drop implies that the deep 31 Atlantic carbon inventory increased by at leas t 50 Gt around the same time as the amount 32 of atmospheric carbon dropped by about 60 Gt . From a comparison with proxy records of 33 deep circulation and climate model simulations 5, we infer that the carbon sequestration 34 coincided with a shoaling of the Atlantic meridional overturning circulation. We thus 35 conclude that changes in the Atlantic ocean circulation may have played an important role 36 in reductions of atmospheric CO2 concentrations during the last glaciation, by increasing 37 the carbon storage in the deep Atlantic. 38
Foreign Direct Investments and Individual Income in Central Africa

This article employs a panel of double ordinary least squares to verify the sensitivity of income per capita to the influx of foreign direct investment (FDI) in the Economic and Monetary Community of Central Africa (EMCCA). Equally, it uses the granger causality test based on the auto-regressive model, most especially the one recently developed by Toda and Yamamoto (1995) to establish the causality links between FDI and per capita income in the Economic and Monetary Community of Central Africa. Results obtained show that there exist a direct effect between FDI and income per capita in the entirety of EMCCA. The granger causality test shows contrarily that there is no link between FDI and income per capita in the EMCCA countries whereas the Toda-Yamamoto finds a symmetric link among these variables in Equatorial Guinea, a unidirectional link of FDI towards income per capita in Congo, Gabon and Chad; and no link between the variables in the Republic of Central Africa and Cameroon.
Characterization of the CGRP receptor antagonist telcagepant (MK-0974) in human isolated coronary arteries

The sensory neuropeptide calcitonin gene-related peptide (CGRP) plays a role in primary headaches and CGRP receptor antagonists are effective in migraine treatment. CGRP is a potent vasodilator raising the possibility that antagonism of its receptor could have cardiovascular effects. We therefore investigated the effects of the anti-migraine CGRP receptor antagonist telcagepant (MK-0974) on human isolated coronary arteries. Arteries with different internal diameters (ID) were studied to assess the potential for differential effects across the coronary vascular bed. The concentration-dependent relaxation responses to human α CGRP were greater in distal (ID: 600-1000 µm, E max =83 ± 7%) than proximal coronary arteries (ID: 2-3 mm, E max =23 ± 9%), coronary arteries from explanted hearts (ID 3-5 mm, E max =11 ± 3%) and coronary arterioles (ID: 200-300 µm, E max =15 ± 7%). Telcagepant alone did not induce contraction or relaxation of these coronary blood vessels. Pre-treatment with telcagepant (10 nM to 1 µM) antagonized α CGRP-induced relaxation competitively in distal coronary arteries (pA 2 =8.43 ± 0.24), proximal coronary arteries and coronary arterioles (telcagepant 1 µM giving pK B =7.89 ± 0.13 and 7.78 ± 0.16 respectively). α CGRP significantly increased cAMP levels in distal, but not proximal coronary arteries and this was abolished by pretreatment with telcagepant. Immunohistochemistry revealed the expression and co-localization of the CGRP1 receptor elements calcitonin like receptor (CLR) and receptor activity modifying protein 1 (RAMP1) in the smooth muscle cells in the media layer of human coronary arteries. These findings in vitro support the cardiovascular safety of CGRP receptor antagonists and suggest that telcagepant is unlikely to induce coronary side effects under normal cardiovascular conditions.
TCP Vegas-L An Adaptive End-to-End Congestion Control Algorithm over Satellite Communications

—Satellite communications is one of the wireless communication technologies, which is widely spread all over the world. Vegas is a kind of the Transmission Control Protocols (TCPs) over satellite communications. However, there are some shortcomings with this protocol, such as being less aggressive and unfair. These would penalize its performance over satellite communications to a certain extent by taking into account the long round trip time and high packet error rates. In this paper, an adaptive end-to-end congestion control algorithm (Vegas-L) is proposed to overcome these issues. Vegas-L can take full advantage of the historical information of network to divide network conditions into more detailed states, then carry out some adaptive and reasonable adjustments according to the speciﬁc network state. Simulation results show that Vegas-L is not only able to improve the aggressiveness to gain high throughput but also has the ability to enhance its fairness over satellite networks.
The consequences of balanced harvesting of fish communities

Balanced harvesting, where species or individuals are exploited in accordance with their productivity, has been proposed as a way to minimize the effects of fishing on marine fish communities and ecosystems. This calls for a thorough examination of the consequences balanced harvesting has on fish community structure and yield. We use a size- and trait-based model that resolves individual interactions through competition and predation to compare balanced harvesting with traditional selective harvesting, which protects juvenile fish from fishing. Four different exploitation patterns, generated by combining selective or unselective harvesting with balanced or unbalanced fishing, are compared. We find that unselective balanced fishing, where individuals are exploited in proportion to their productivity, produces a slightly larger total maximum sustainable yield than the other exploitation patterns and, for a given yield, the least change in the relative biomass composition of the fish community. Because fishing reduces competition, predation and cannibalism within the community, the total maximum sustainable yield is achieved at high exploitation rates. The yield from unselective balanced fishing is dominated by small individuals, whereas selective fishing produces a
The Relationship between Analytic and Holistic Styles of Thinking and Forgiveness

This study examined the relationship between styles of thinking (analytic and holistic) and willingness to forgive. Previous research has demonstrated that individuals attend to their worlds in qualitatively different ways: analytic thinkers focus on objects and their attributes while holistic thinkers focus on the context as a whole, in which objects are viewed in relation to each other. Consistent with these differences in attention, analytic thinkers may be more likely to make internal attributions for other people’s transgressions while holistic thinkers may tend to perceive external attributions for those transgressions (Nisbett, Peng, Choi, & Norenzayan, 2001). Research has shown that people who attribute transgressions to external factors report higher levels of forgiveness (Fincham, Paleari, & Regalia, 2002). Thus, analytic and holistic styles of thinking may be related to a person’s willingness to forgive. We therefore hypothesized that people who think more holistically would be more forgiving because they view others’ behaviors as more strongly influenced by situational factors. On the other hand, people who think more analytically would be less likely to forgive because they are more likely to understand others’ transgressions as a result of their underlying dispositions. Study participants answered online surveys regarding their styles of thinking (analytic or holistic), their attributional style (situational or dispositional) and their willingness to forgive. Holistic thinkers were found to be more likely to forgive others than analytic thinkers. However, this relationship was not mediated by attributional style. Alternative explanations for the findings are discussed. subsamples in significant p . 001. No differences significant.
STRICTLY PROTECTED SPECIES OF HOVERFLIES IN IN THE FACE OF CLIMATE CHANGE

: Climate change is happening. Due to a spectrum of possible conse- quences, numerous studies examine the effects of global warming on species distribution. This study examines the effects of changing climate on distribution of selected strictly protected species of hoverflies in Serbia, by using species distribution modelling. Ten species were included in the analysis. Three species were predicted to lose a part of their range across time, while for seven species the range expansion was predicted. Both in the present time and in the future, mountainous regions have the highest species richness, such as Golija, Kopaonik, and Prokletije in the western Serbia, and mountains Stara Planina, Besna Kobi-la, Suva Planina, and Dukat in the southeastern part of the country. However, beside climate change, there are several other factors that might influence the distribution of strictly protected hoverflies in Serbia, such as intensive land use and degradation of habitats. Addition- ally, global warming also affects flowering plants that syrphids are dependent on, which could present another obstacle to their future range expansions. These results can contribute to planning future steps for the conservation of strictly protected hoverfly species.