Miracle-dz/UniFAR-FTUs huggingface dataset:
Deconstructing Empathy and Creating a “We Space” in Group Therapy

Abstract:A long and rich history recognizes the important role empathy plays in both group and individual therapy. The discovery of mirror neurons and related systems in the human brain has initiated renewed interest in the subject. This article deconstructs empathy in an effort to provide a deeper understanding of its developmental value as the highest form of emotional intelligence and thus provide a rationale for greater emphasis on education and training for psychotherapists.
A control strategy based on the upper and lower's arms modulation functions of MMC in HVDC applications

A control strategy Is proposed In this paper based on considering the upper and lower's arms modulation functions of a MMC in HVDC applications. The designed modulation-functions-based controller is consisted of the modulation index and phase that are accurately evaluated according to ac-side voltage, MMC voltage and current components in a-b-c reference frame. Two main contributions of the proposed control strategy over the other existing control techniques are robust against the MMC parameter variations and simplicity operation that causes MMC to perform the ac/dc conversion for the HVDC applications. The simulation results verify the ability of the proposed control strategy at approaching to the stable performance of MMC under various operating conditions.
Environmental determinism, and not interspecific competition, drives morphological variability in Australasian warblers (Acanthizidae)

Abstract Interspecific competition is thought to play a key role in determining the coexistence of closely related species within adaptive radiations. Competition for ecological resources can lead to different outcomes from character displacement to, ultimately, competitive exclusion. Accordingly, divergent natural selection should disfavor those species that are the most similar to their competitor in resource use, thereby increasing morphological disparity. Here, we examined ecomorphological variability within an Australo‐Papuan bird radiation, the Acanthizidae, which include both allopatric and sympatric complexes. In addition, we investigated whether morphological similarities between species are related to environmental factors at fine scale (foraging niche) and/or large scale (climate). Contrary to that predicted by the competition hypothesis, we did not find a significant correlation between the morphological similarities found between species and their degree of range overlap. Comparative modeling based on both a priori and data‐driven identification of selective regimes suggested that foraging niche is a poor predictor of morphological variability in acanthizids. By contrast, our results indicate that climatic conditions were an important factor in the formation of morphological variation. We found a significant negative correlation between species scores for PC1 (positively associated to tarsus length and tail length) and both temperature and precipitation, whereas PC2 (positively associated to bill length and wing length) correlated positively with precipitation. In addition, we found that species inhabiting the same region are closer to each other in morphospace than to species outside that region regardless of genus to which they belong or its foraging strategy. Our results indicate that the conservative body form of acanthizids is one that can work under a wide variety of environments (an all‐purpose morphology), and the observed interspecific similarity is probably driven by the common response to environment.
A Non-parametric Multi-stage Learning Framework for Cognitive Spectrum Access in IoT Networks

Given the increasing number of devices that is going to get connected to wireless networks with the advent of Internet of Things, spectrum scarcity will present a major challenge. Application of opportunistic spectrum access mechanisms to IoT networks will become increasingly important to solve this. In this paper, we present a cognitive radio network architecture which uses multi-stage online learning techniques for spectrum assignment to devices, with the aim of improving the throughput and energy efficiency of the IoT devices. In the first stage, we use an AI technique to learn the quality of a user-channel pairing. The next stage utilizes a non-parametric Bayesian learning algorithm to estimate the Primary User OFF time in each channel. The third stage augments the Bayesian learner with implicit exploration to accelerate the learning procedure. The proposed method leads to significant improvement in throughput and energy efficiency of the IoT devices while keeping the interference to the primary users minimal. We provide comprehensive empirical validation of the method with other learning based approaches.
An Optimal Joint User Association and Power Allocation Algorithm for Secrecy Information Transmission in Heterogeneous Networks

In recent years, radio access technologies have experienced rapid development and gradually achieved effective coordination and integration, resulting in heterogeneous networks (HetNets). User equipments (UEs) located in the overlapping area of various networks of HetNets are capable of selecting the base station (BS) of one network for association and conduct information interaction. In this paper, we study user association and power allocation problem for HetNets with eavesdroppers. To achieve secrecy data transmission in a secret and energy-efficient manner, the concept of joint secrecy energy efficiency of the network is introduced and is defined as the ratio of secrecy transmission rate and the power consumption of the BSs. An optimization problem is formulated which maximizes the joint secrecy energy efficiency under the constraints of maximum power of the BSs and the minimum data rate requirement of the UEs, and the optimal user association and transmit power strategy is obtained through applying iterative algorithm and Lagrange dual method. Numerical results demonstrate the efficiency of the proposed algorithm.
Predicting student's psychomotor domain on the vocational senior high school using linear regression

The educational data can be mined to produce the useful knowledge. This paper focuses on the educational data processing to predict student's psychomotor domain. Here, we apply linear regression method to do it. On process stage, we use 4 regularizations, namely: no regularization, ridge regression, lasso regression and elastic net regression. Furthermore, we exploit 2 sampling methods as the evaluation technique, for examples: cross-validation sampling and random sampling. The experimental result indicates that the best regularization on cross-validation and random sampling are an elastic net regression because this regularization achieves the lowest predicting error. On cross-validation, values of MSE, RMSE, and MAE are 40.079, 6.330 and 5.183, respectively. Additionally, for random sampling, respectively, values of MSE, RMSE, and MAE are 86.910, 8.428 and 6.511.
Data-driven Summarization of Scientific Articles

Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we propose to use scientific articles as a new milestone for text summarization: large-scale training data come almost for free with two types of high-quality summaries at different levels - the title and the abstract. We generate two novel multi-sentence summarization datasets from scientific articles and test the suitability of a wide range of existing extractive and abstractive neural network-based summarization approaches. Our analysis demonstrates that scientific papers are suitable for data-driven text summarization. Our results could serve as valuable benchmarks for scaling sequence-to-sequence models to very long sequences.
Biomarkers in disease and health

Biomarkers have considerable potential in aiding the understanding of the relationship between diet and disease or health. However, to assess the role, relevance and importance of biomarkers on a case by case basis it is essential to understand and prioritise the principal diet and health issues. In the majority of cases, dietary compounds are only weakly biologically active in the short term, have multiple targets and can be both beneficial and deleterious. This poses particular problems in determining the net effect of types of foods on health. In principle, a biomarker should be able to contribute to this debate by allowing the measurement of exposure and by acting as an indicator either of a deleterious or of an enhanced health effect prior to the final outcome. In this review, the examples chosen - cancer (stomach, colon/rectal, breast); coronary heart disease and osteoporosis - reflect three major diet-related disease issues. In each case the onset of the disease has a genetic determinant which may be exacerbated or delayed by diet. Perhaps the most important factor is that in each case the disease, once manifest, is difficult to influence in a positive way by diet alone. This then suggests that the emphasis for biomarker studies should focus on predictive biomarkers which can be used to help in the development of dietary strategies which will minimise the risk and be of greater benefit.
Axis-Aligned Height-Field Block Decomposition of 3D Shapes

We propose a novel algorithm for decomposing general three-dimensional geometries into a small set of overlap-free height-field blocks, volumes enclosed by a flat base and a height-field surface defined with respect to this base. This decomposition is useful for fabrication methodologies such as 3-axis CNC milling, where a single milling pass can only carve a single height-field surface defined with respect to the machine tray but can also benefit other fabrication settings. Computing our desired decomposition requires solving a highly constrained discrete optimization problem, variants of which are known to be NP-hard. We effectively compute a high-quality decomposition by using a two-step process that leverages the unique characteristics of our setup. Specifically, we notice that if the height-field directions are constrained to the major axes, then we can always produce a valid decomposition starting from a suitable surface segmentation. Our method first produces a compact set of large, possibly overlapping, height-field blocks that jointly cover the model surface by recasting this discrete constrained optimization problem as an unconstrained optimization of a continuous function, which allows for an efficient solution. We then cast the computation of an overlap-free, final decomposition as an ordering problem on a graph and solve it via a combination of cycle elimination and topological sorting. The combined algorithm produces a compact set of height-field blocks that jointly describe the input model within a user given tolerance. We demonstrate our method on a range of inputs and showcase a number of real life models manufactured using our technique.
Identifying natural substrates for chaperonins using a sequence‐based approach

The Escherichia coli chaperonin machinery, GroEL, assists the folding of a number of proteins. We describe a sequence‐based approach to identify the natural substrate proteins (SPs) for GroEL. Our method is based on the hypothesis that natural SPs are those that contain patterns of residues similar to those found in either GroES mobile loop and/or strongly binding peptide in complex with GroEL. The method is validated by comparing the predicted results with experimentally determined natural SPs for GroEL. We have searched for such patterns in five genomes. In the E. coli genome, we identify 1422 (about one‐third) sequences that are putative natural SPs. In Saccharomyces cerevisiae, 2885 (32%) of sequences can be natural substrates for Hsp60, which is the analog of GroEL. The precise number of natural SPs is shown to be a function of the number of contacts an SP makes with the apical domain (NC) and the number of binding sites (NB) in the oligomer with which it interacts. For known SPs for GroEL, we find ∼4 < NC < 5 and 2 ≤ NB ≤ 4. A limited analysis of the predicted binding sequences shows that they do not adopt any preferred secondary structure. Our method also predicts the putative binding regions in the identified SPs. The results of our study show that a variety of SPs, associated with diverse functions, can interact with GroEL.
Next Generation Telecommunications Computing Architecture (NexTCA)

Many telecom services are deployed on proprietary and expensive equipment. Telecom operators must constantly invest in computing power and I/O capabilities to provide more services. However, it is not allowed to share resources among these dedicated systems, which leads to considerable waste of money and resources. A general-purpose platform should take care of virtualization, performance resource sharing and cost efficiency as a whole. This paper proposes NexTCA that embodies disaggregated rack architecture as well as network and storage MR-IOV (multi-root I/O virtualization) system based on PCIe technology by leveraging existing commodity hardware. Additionally, we develop an Ethernet over PCIe channel which fully uses PCIe link capability while providing transparent features to upper-layer applications. At last, OPNFV YardStick and FIO are deployed to verify the functionality and performance of NexTCA in comparison with a modern Ethernet-based system. The experiment results show that NexTCA remarkably outperforms a modern Ethernet-based system in both throughput and latency; even, PCIe is a superior technology for intra-rack and inter-rack communication. Reference from vendors and resellers also points out that the construction cost of PCIe is lower than that of 10Gbps Ethernet.
High-resolution mapping of DNA polymerase fidelity using nucleotide imbalances and next-generation sequencing

Abstract DNA polymerase fidelity is affected by both intrinsic properties and environmental conditions. Current strategies for measuring DNA polymerase error rate in vitro are constrained by low error subtype sensitivity, poor scalability, and lack of flexibility in types of sequence contexts that can be tested. We have developed the Magnification via Nucleotide Imbalance Fidelity (MagNIFi) assay, a scalable next-generation sequencing assay that uses a biased deoxynucleotide pool to quantitatively shift error rates into a range where errors are frequent and hence measurement is robust, while still allowing for accurate mapping to error rates under typical conditions. This assay is compatible with a wide range of fidelity-modulating conditions, and enables high-throughput analysis of sequence context effects on base substitution and single nucleotide deletion fidelity using a built-in template library. We validate this assay by comparing to previously established fidelity metrics, and use it to investigate neighboring sequence-mediated effects on fidelity for several DNA polymerases. Through these demonstrations, we establish the MagNIFi assay for robust, high-throughput analysis of DNA polymerase fidelity.
Restricted Max-Min Fair Allocation

The restricted max-min fair allocation problem seeks an allocation of resources to players that maximizes the minimum total value obtained by any player. It is NP-hard to approximate the problem to a ratio less than 2. Comparing the current best algorithm for estimating the optimal value with the current best for constructing an allocation, there is quite a gap between the ratios that can be achieved in polynomial time: roughly 4 for estimation and roughly $6 + 2\sqrt{10}$ for construction. We propose an algorithm that constructs an allocation with value within a factor of $6 + \delta$ from the optimum for any constant $\delta > 0$. The running time is polynomial in the input size for any constant $\delta$ chosen.
The environmental zero‐point problem in evolutionary reaction norm modeling

Abstract There is a potential problem in present quantitative genetics evolutionary modeling based on reaction norms. Such models are state‐space models, where the multivariate breeder's equation in some form is used as the state equation that propagates the population state forward in time. These models use the implicit assumption of a constant reference environment, in many cases set to zero. This zero‐point is often the environment a population is adapted to, that is, where the expected geometric mean fitness is maximized. Such environmental reference values follow from the state of the population system, and they are thus population properties. The environment the population is adapted to, is, in other words, an internal population property, independent of the external environment. It is only when the external environment coincides with the internal reference environment, or vice versa, that the population is adapted to the current environment. This is formally a result of state‐space modeling theory, which is an important theoretical basis for evolutionary modeling. The potential zero‐point problem is present in all types of reaction norm models, parametrized as well as function‐valued, and the problem does not disappear when the reference environment is set to zero. As the environmental reference values are population characteristics, they ought to be modeled as such. Whether such characteristics are evolvable is an open question, but considering the complexity of evolutionary processes, such evolvability cannot be excluded without good arguments. As a straightforward solution, I propose to model the reference values as evolvable mean traits in their own right, in addition to other reaction norm traits. However, solutions based on an evolvable G matrix are also possible.
Streaming Rewriting Games: Winning Strategies and Complexity

Context-free games on strings are two-player rewriting games based on a set of production rules and a regular target language. In each round, the first player selects a position of the current string; then the second player replaces the symbol at that position according to one of the production rules. The first player wins as soon as the current string belongs to the target language. In this paper the one-pass setting for context-free games is studied, where the knowledge of the first player is incomplete, she selects positions in a left-to-right fashion and only sees the current symbol and the symbols from previous rounds. The paper studies conditions under which dominant or undominated strategies for the first player exist and investigates the complexity of some related algorithmic problems.
Role of allogeneic stem cell transplantation in mantle cell lymphoma

Despite a wide spectrum of treatment options, mantle cell lymphoma (MCL) remains a challenging hematologic malignancy to manage. Advances in front‐line therapy, including the monoclonal antibody rituximab and increasing use of cytarabine, have improved remission rates. Autologous hematopoietic cell transplantation (HCT) can effectively consolidate remission of MCL, leading to encouraging survival beyond 5 yr. However, nearly all patients with MCL will relapse and require salvage therapy. Novel agents such as ibrutinib, bortezomib, and lenalidomide have dramatically expanded the options for treating relapsed MCL. In this review, we summarize the clinical evidence supporting the use of allogeneic donor HCT in MCL and make recommendations on indications for its use. Data suggest that allogeneic donor HCT is the only curative therapy for patients with poor prognosis or aggressive MCL. Patient selection, timing, and optimal use remain a matter of scientific debate and given the rapidly changing therapeutic landscape of MCL, the outcomes of allogeneic HCT should be interpreted in the context of novel therapeutics.
The neural system of metacognition accompanying decision-making in the prefrontal cortex

Decision-making is usually accompanied by metacognition, through which a decision maker monitors the decision uncertainty and consequently revises the decision, even prior to feedback. However, the neural mechanisms of metacognition remain controversial: one theory proposes that metacognition coincides the decision-making process; and another addresses that it entails an independent neural system in the prefrontal cortex (PFC). Here we devised a novel paradigm of “decision-redecision” to investigate the metacognition process in redecision, in comparison with the decision process. We here found that the anterior PFC, including dorsal anterior cingulate cortex (dACC) and lateral frontopolar cortex (lFPC), were exclusively activated after the initial decisions. dACC was involved in decision uncertainty monitoring, whereas lFPC was involved in decision adjustment controlling, subject to control demands of the tasks. Our findings support that the PFC is essentially involved in metacognition and further suggest that functions of the PFC in metacognition are dissociable.
DATS — Programmability and Security by Design for Web Applications

Using data-centric containers for isolation is a very effective way to give users control of their data and avoid information leaks from untrusted applications. However, they are hard to program for and lead to inefficiencies, since they require replicating an application inside each container. We propose DATS — a system to run web applications that retains application usability and efficiency through a mix of hardware capability enhanced containers and the introduction of two new primitives modeled after the popular model-view-controller (MVC) pattern. (1) DATS introduces a templating language to create views that compose data across data containers. (2) DATS uses authenticated storage and confinement to enable an untrusted storage service such as memcached and deduplication to operate on plain-text data across containers. These two primitives act as robust declassifiers that allow DATS to enforce non-interference across containers, taking large applications out of the trusted computing base (TCB). We showcase various web applications including gitlab and a Slack-like chat (eight in total), significantly improve the worst-case overheads due to application replication, and demonstrate usable performance for common-case usage. CCS Concepts •Security and privacy → Authentication; Access control; Authorization; Web application security; Operating systems security; Information flow control 1 Equal contributors. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. ASPLOS ’18, March 24–28, 2018, Williamsburg, VA, USA c © 2018 Copyright held by the owner/author(s). Publication rights licensed to ACM. ISBN 978-1-4503-4911-6/18/03. $15.00 DOI: https://doi.org/10.1145/3173162.3173213