Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible structure learning under uncertainty

Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments. Here, we ask whether uncertainty in dynamic environments affects our ability to learn predictive structures. We exposed participants to sequences of symbols determined by first-order Markov models and asked them to indicate which symbol they expected to follow each sequence. We introduced uncertainty in this prediction task by manipulating the: (a) probability of symbol co-occurrence, (b) stimulus presentation rate. Further, we manipulated feedback, as it is known to play a key role in resolving uncertainty. Our results demonstrate that increasing the similarity in the probabilities of symbol co-occurrence impaired performance on the prediction task. In contrast, increasing uncertainty in stimulus presentation rate by introducing temporal jitter resulted in participants adopting a strategy closer to probability maximization than matching and improving in the prediction tasks. Next, we show that feedback plays a key role in learning predictive statistics. Trial-by-trial feedback yielded stronger improvement than block feedback or no feedback; that is, participants adopted a strategy closer to probability maximization and showed stronger improvement when trained with trial-by-trial feedback. Further, correlating individual strategy with learning performance showed better performance in structure learning for observers who adopted a strategy closer to maximization. Our results indicate that executive cognitive functions (i.e., selective attention) may account for this individual variability in strategy and structure learning ability. Taken together, our results provide evidence for flexible structure learning; individuals adapt their decision strategy closer to probability maximization, reducing uncertainty in temporal sequences and improving their ability to learn predictive statistics in variable environments.
Sovereign Spreads, Central Bank Collateral Frameworks, and Peri‐ phery Premia in the Eurozone

This paper studies the emergence of sovereign bond yield spreads in the Eurozone prior to the financial crisis. While spreads were close to zero in European government debt markets until the mid-2000s, they have persistently widened since then in many member states. We employ a diffe-rence-in-differences approach to analyze this phenomenon. We find that the Eurosystem’s move from unconditional to conditional collateral eligibility of sovereign bonds, as part of the 2005 Single List reform, was the institutional change triggering the emergence of sovereign spreads in the Euro Area. Conditional eligibility becomes effective predominantly through a periphery premium: higher yields have been demanded from countries whose business cycles deviate most from the average Eurozone cycle. In contrast, spreads did not arise in response to adverse macroeconomic and fiscal fundamentals.
Ideals and Commutators of Operators

Subideals. A subideal of operators is an ideal of J (called a J -ideal) for J an arbitrary ideal of B(H). Necessary and sufficient conditions are determined for a finitely generated subideal to be also an ideal of B(H) and then these conditions are exploited to characterize all finitely generated subideals. This generalizes to arbitrary ideals the 1983 work of Fong and Radjavi who determined which principal (i.e., singly generated) ideals of the ideal of compact operators are also ideals of B(H). Then a necessary and sufficient condition is found for a countably generated subideal and a subideal generated by sets of cardinality strictly less than the continuum to be also an ideal of B(H). This is based on the Hamel dimension of a related quotient space. Then this condition is used to characterize these subideals and settle additional general questions about subideals. The condition is a generalization of the notion of soft-edged ideals discovered in 2007 by Kaftal and Weiss. Examples of subideals reveal some striking differences between subideals and ideals of B(H). Also this work intersects the study of subideals with the study of elementary operators with coefficient constraints. Commutators of compact operators. The 1971 commutator problem asked by Pearcy and Topping is investigated: Is every compact operator a single commutator of compact operators? And a 1976 test question arising from work of Pearcy, Topping, Anderson and Weiss: Are any strictly positive compact operators a single commutator of compact operators? An affirmative answer to this test question for a whole class of strictly positive compact operators is obtained.
Multi-Resolution and Asymmetric Implementation of Attention in Transformers

Transformers are the state-of-the-art for machine translation and grammar error correction. One of the most important components of transformers are the attention layers, but they require significant computational power. We suggest a new way of looking at the “mixing” mechanisms of tokens by doing a multi-resolution implementation of attention, which maintains inference results while also improving training and inference speed, thus getting the best of both worlds. This approximation can be applied in symmtrical and asymmetrical manner within and across attention layers. We also suggest an interesting alternative for the softmax layer in attention. We also analyzed some other hyperparameters in detail. For example, our experiments indicate that we can have asymmetry among the attention layers w.r. t. number of heads, while still achieving similar results. In many cases, reducing the number of heads improves inference results. We also explored the role of weighting matrices for query, key, and value vectors; and show that in case of selfattention, absence of these matrices results in the collapse of the attention layers to an identity matrix.
Technical note: Determining Arctic Ocean cold halocline and cold halostad layer depths based on vertical stability

The Arctic Ocean cold halocline layer (CHL) separates the cold surface mixed layer (SML) from the underlying warm Atlantic water, and thus provides a precondition for sea ice formation. Here, we introduce a new method in which the CHL base depth is diagnosed from vertical stability and compare it to two existing methods. Vertical stability directly affects vertical mixing and heat exchange. When applied to measurements from ice-tethered proﬁlers, ships, and moorings, the new method for estimating the CHL base depth provides robust results with few artifacts. Comparatively large differences between 5 our new method and two existing methods for detecting the CHL base depth were found in regions which are most prone to a CHL retreat in a warming climate. CHL base depth exhibits a seasonal cycle with a maximum depth in winter and also spring, when the SML depth is also at its maximum, but the amplitude of the CHL base depth’s seasonal cycle is lower than for the SML for all three methods as expected. We also propose a novel method for detecting the cold halostad layer and study the seasonal cycle employing conservative assumptions to avoid a misclassiﬁcation (including a lower bound of 50 m for the 10 thickness). Detection of a cold halostad layer was largely conﬁned to the Canada Basin and to the regions off the eastern coast of Greenland and also Svalbard.
Parental Ethnotheories Early Childhood

: Although a plausible link between parental ethnotheories and cognitive development have been suggested, studies addressing specifically parental ethnotheories about learning and early cognitive development in Sub direct relationship between parents’ cultural ideas about learning and children’s cognitive development. A mixed-method design (Case study and Correlational) was used for th Participants included 222 parents of children aged five to six years old selected using the purposive sampling technique. Questionnaires and interviews were used for data collection. Parental ethnotheories about learning was associated with indivi (p=0.00) but not associated with social cognition or cognition in service of social ends (p=0.169). Therefore, parental ethnotheories about learning may influence cognitive competence in intellectual tasks such as in school or an on social competence emphasizing social responsibilities and relatedness to the family and peers.
ϕ Meson Spin Alignment and the Azimuthal Angle Dependence of Λ (Λ) Polarization in Au+Au collisions at RHIC

Initial large global angular momentum in non-central relativistic heavy-ion collisions can produce strong vorticity, and through the spin-orbit coupling, causes the spin of particles to align with the system’s global angular momentum. We present the azimuthal angle dependent (relative to the reaction plane) polarization for Λ and Λ in mid-central Au+Au collisions at sNN = 200 GeV. We also present the ϕ meson spin alignment parameter, ρ 00 in Au+Au collisions at sNN = 19.6, 27, 39, 62.4 and 200 GeV. The implications of the results are discussed.
Cross Sections for Electron Collisions with H 2 O

Electron collision cross section data for the water molecule are compiled from the literature. Cross sections are collected and reviewed for total scattering, elastic scattering, momentum transfer, excitations of rotational and vibrational states, electronic excitation, dissociation, ionization, and dissociative attachment. For each of these processes, the recommended values of the cross sections are presented. The literature has been surveyed up to the end of 2019.
Temporal Attention Signatures for Interpretable Time-Series Prediction

Deep neural networks have become a staple in time-series prediction due to their remarkable accuracy. However, their internal workings often remain elusive. Significant advancements have been made in the interpretability of these networks, with attention mechanisms and feature maps being notably effective for image classification by highlighting the crucial data points. While human observers can readily confirm the significance of features in image classification, the interpretability of time-series data and its modeling remains challenging. To address this, we put forth an innovative approach that unifies temporal attention and visualization as a blend of recurrent neural networks, self-attention, and general attention. This synergy results in the generation of temporal attention signatures , akin to image attention heat maps. Temporal attention not only enhances prediction accuracy beyond that of recurrent networks alone but also demonstrates that varying label classes yield distinct attention signatures. This observation indicates that neural networks focus on different sections of time-series sequences contingent on the prediction target. We conclude with a discussion on the practical implications of this novel approach, including its applicability to model interpretation, sequence length selection, and model validation. This leads to more accurate, robust, and interpretable models, instilling greater confidence in their results.
Does Mutual Knowledge Affect Virtual Team Performance? Does Mutual Knowledge Affect Virtual Team Performance? Theoretical Analysis and Anecdotal Evidence Theoretical Analysis and Anecdotal Evidence

Alanah Davis is pursuing her PhD in IT in the College of Information Science and Technology at the University of Nebraska at Omaha. Her current research interests include virtual teams, group support systems and e-commerce. Her research work has been presented at various peer-reviewed conferences and workshops. She earned a MS in e-commerce from Creighton University prior to joining the PhD program. Deepak Khazanchi is the Associate Dean and Professor in College of Information Science & Technology at the University of Nebraska in Omaha. Some of his current research interests include virtual project management, virtual teams, and B2B assurance services. His research has been published in journals such as DSS , Information Systems Management , ACM's DATA BASE for Advances in Information Systems , and Information Processing and Management .
An Efficient Algorithm for the Detection of Exposed and Hidden Wormhole Attack

MANETs (Mobile Ad Hoc Networks) are slowly integrating into our everyday lives, their most prominent uses are visible in the disaster and war struck areas where physical infrastructure is almost impossible or very hard to build. MANETs like other networks are facing the threat of malicious users and their activities. A number of attacks have been identified but the most severe of them is the wormhole attack which has the ability to succeed even in case of encrypted traffic and secure networks. Once wormhole is launched successfully, the severity increases by the fact that attackers can launch other attacks too. This paper presents a comprehensive algorithm for the detection of exposed as well as hidden wormhole attack while keeping the detection rate to maximum and at the same reducing false alarms. The algorithm does not require any extra hardware, time synchronization or any special type of nodes. The architecture consists of the combination of Routing Table, RTT (Round Trip Time) and RSSI (Received Signal Strength Indicator) for comprehensive detection of wormhole attack. The proposed technique is robust, light weight, has low resource requirements and provides real-time detection against the wormhole attack. Simulation results show that the algorithm is able to provide a higher detection rate, packet delivery ratio, negligible false alarms and is also better in terms of Ease of Implementation, Detection Accuracy/ Speed and processing overhead.
Matching on the line admits no 𝑜 ( (cid:112) log 𝑛 ) -competitive algorithm

We present a simple proof that no randomized online matching algorithm for the line can be ( (cid:112) log 2 ( 𝑛 + 1 )/ 15 ) -competitive against an oblivious adversary for any 𝑛 = 2 𝑖 − 1 : 𝑖 ∈ N . This is the first super-constant lower bound for the problem
On the low-dimensional structure of Bayesian inference

The Bayesian approach to inference characterizes model parameters and predictions through the exploration of their posterior distributions, i.e., their distributions conditioned on available data. The Bayesian paradigm provides a flexible, principled framework for quantifying uncertainty, wherein heterogeneous and incomplete sources of information (e.g., prior knowledge, noisy observations, imperfect models) can be properly rationalized. Yet a major obstacle to deploying Bayesian inference in realistic applications is computational: characterizing the associated high-dimensional and non-Gaussian posterior distributions remains a challenging task. While the Bayesian formulation is quite general, essential features of a statistical model can bring additional structure to the Bayesian update. For instance, the prior distribution often encodes some kind of regularity in the parameters; observations might be sparse and corrupted by noise; observations might also be indirect, related to the parameters by a forward operator that filters out some information; the posterior distribution might satisfy conditional independence assumptions that reflect local probabilistic interactions; and in some cases we might be uninterested in the posterior distribution per se, but rather in specific prediction goals. In this thesis we: (1) provide a rigorous mathematical characterization of lowdimensional structures that enable efficient Bayesian inference in high-dimensional and continuous parameter spaces; and (2) exploit this characterization to devise new structure-exploiting and computationally efficient inference algorithms. Our contributions encompass multiple related topics. First we characterize optimal low-rank approximations of linear–Gaussian Bayesian inverse problems, and of their goal-oriented extensions. Then we turn to inference in the nonlinear non-Gaussian setting—analyzing the sparsity, decomposability, and low-rank structure of deterministic couplings between distributions. These couplings facilitate efficient computation of posterior expectations in generically non-Gaussian settings. Based on this analysis, we introduce a number of approaches for representing non-Gaussian Markov random fields and for exploiting their conditional independence structure in computation by means of sparse nonlinear transport maps. We also develop new variational
Difference between absolute and relative muscle strength according to resistance exercise proficiency

In this study, the absolute and relative strength of six upper extremity resistance exercises were measured by classifying resistance exercise experts and non-experts. As a result, the skilled group showed higher absolute and relative muscle strength than the unskilled group in the 6 upper extremity resistance exercises. These results are judged to be the hypertrophy of fast-twith muscles, the mobilization of motor units, and the increase in the speed of nerve conduction while the skilled person consistently performs resistance exercise. Experts use intermuscular coordination efficiently to stably perform the load according to the movement and exercise intensity performed during exercise, whereas the inexperienced person uses relatively large muscle groups rather than efficiently using intermuscular coordination. It is considered that exercise motion and load were performed by mobilizing. In addition, as a result of comparing the absolute and relative muscle strength between the 6 types of upper limb resistance exercises, there was a difference between the 6 types of upper limb resistance exercises in the two groups. It can be judged that greater muscle strength and endurance were created through liver coordination.
Agreement and disagreement in a non-classical world

The Agreement Theorem Aumann (1976 Ann. Stat. 4, 1236–1239. (doi:10.1214/aos/1176343654)) states that if two Bayesian agents start with a common prior, then they cannot have common knowledge that they hold different posterior probabilities of some underlying event of interest. In short, the two agents cannot ‘agree to disagree’. This result applies in the classical domain where classical probability theory applies. But in non-classical domains, such as the quantum world, classical probability theory does not apply. Inspired principally by their use in quantum mechanics, we employ signed probabilities to investigate the epistemics of the non-classical world. We find that here, too, it cannot be common knowledge that two agents assign different probabilities to an event of interest. However, in a non-classical domain, unlike the classical case, it can be common certainty that two agents assign different probabilities to an event of interest. Finally, in a non-classical domain, it cannot be common certainty that two agents assign different probabilities, if communication of their common certainty is possible—even if communication does not take place. This article is part of the theme issue ‘Quantum contextuality, causality and freedom of choice’.
Unintended Consequences of Organizational Error Culture: Does the Strive for Perfection Cause Employees to Put on a Façade and Consider Leaving?

Unintended Consequences of Organizational Error Culture: Does the strive for perfection cause employees to put on a façade and consider leaving? By Scott H. Crumpton March 2023 Committee Chair: Wesley Johnston Major Academic Unit: J. Mack Robinson College of Business In an increasingly digital world, and further amplified over the last two years by COVID, customer expectations have never been higher. Facing intense competition, B2B technology providers are under growing pressure to deliver a frictionless customer experience to their clients (Baliga et al., 2021). Striving for perfection, organizations naturally operationalize how they manage their customers and how they handle errors as they occur. But as organizations work through their cultural error practices, are they considering the well-being of their employees and how their error culture practices may inadvertently influence the ways that employees look at their organizations? In this study, I examined organizational error culture through the strategies of error management and error aversion cultures. I then examined how these error culture types could impact an employee’s tendency to surface internally and influence his/her turnover intentions. To gather data, I conducted an on-line survey of 270 customer-facing employees working for B2B organizations that provide technology services. Ultimately, this study’s findings suggest that both turnover intentions and the level of surface acting exhibited by employees have a direct relationship with the type of error culture that their organization employs. Further, I tested whether an employee’s organizational tenure and hierarchal role within the firm would moderate the relationships between error aversion
Upper bounds on the uniquely restricted chromatic index

Golumbic, Hirst, and Lewenstein define a matching in a simple, finite, and undirected graph G to be uniquely restricted if no other matching covers exactly the same set of vertices. We consider uniquely restricted edge‐colorings of G , defined as partitions of its edge set into uniquely restricted matchings, and study the uniquely restricted chromatic index χ ′ u r ( G ) of G , defined as the minimum number of uniquely restricted matchings required for such a partition. For every graph G , χ ′ ( G ) ≤ a ′ ( G ) ≤ χ ′ u r ( G ) ≤ χ ′ s ( G ) , where χ ′ ( G ) is the classical chromatic index, a ′ ( G ) the acyclic chromatic index, and χ ′ s ( G ) the strong chromatic index of G . While Vizing's famous theorem states that χ ′ ( G ) is either the maximum degree Δ ( G ) of G or Δ ( G ) + 1 , two famous open conjectures due to Alon, Sudakov, and Zaks, and to Erdős and Nešetřil concern upper bounds on a ′ ( G ) and χ ′ s ( G ) in terms of Δ ( G ) . Since χ ′ u r ( G ) is sandwiched between these two parameters, studying upper bounds in terms of Δ ( G ) is a natural problem. We show that χ ′ u r ( G ) ≤ Δ ( G ) 2 with equality if and only if some component of G is K Δ ( G ) , Δ ( G ) . If G is connected, bipartite, and distinct from K Δ ( G ) , Δ ( G ) , and Δ ( G ) is at least 4 , then, adapting Lovász's elegant proof of Brooks’ theorem, we show that χ ′ u r ( G ) ≤ Δ ( G ) 2 − Δ ( G ) . Our proofs are constructive and yield efficient algorithms to determine the corresponding edge‐colorings.
Investigation on the Performance of Reduced Graphene Oxide as Counter Electrode in Dye Sensitized Solar Cell Applications

In dye sensitized solar cells (DSSC), the counter electrode (CE) plays a significant role in electro catalytic reaction in redox region to achieve maximum power conversion efficiency (PCE) of the DSSC. The graphene oxide (GO) is synthesized by Hummer's method and converted to reduced graphene oxide (RGO) by thermal method. The effect of binders such as NMP, PVP, and nafion for RGO are investigated. The field emission scanning electron microscope (FESEM) of RGO shows the fine layers like structure. The surface area of the RGO is found to be 110.16 m2 g−1 by Brunauer–Emmett–Teller (BET) method and pore volume 0.5182 cm3 g−1 of RGO by Barrett–Joyner–Halenda (BJH) method. The Raman spectra is used to analyze the structural defects and changes of GO and RGO. The crystallite size of GO is 18.6 nm, and RGO is 20.9 nm calculated from the Raman spectrum. For analyzing the electro catalytic properties of the RGO/NMP/PVP, RGO/NMP, and RGO/nafion based CE materials cyclic voltammetry is performed and its maximum current density was measured. The composition of RGO/NMP/PVP based CE exhibits better electro‐catalytic properties when compared to other RGO/NMP and RGO/nafion based counter electrodes. Maximum PCE exhibited by RGO/NMP/PVP CE based DSSC is 5.8%.