Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensory Probes: An Exploratory Design Research Method for Human-Food Interaction

Designing interactions with food holds potential for rich multisensory experiences but their pervasiveness can challenge our understanding of them. This paper presents the design and evaluation of Sensory probes, a novel, exploratory design research method aimed to sensitize participants towards their food experiences. We report on workshops with 8 participants for co-designing the probes, followed by iterative revision through two-week diary studies with 18 participants. Findings indicate strong engagement with the sensory probes and how they brought forward the bodily and sensory aspects of these experiences, alongside emotional and social ones. We highlight the design rationale for the sensory probes which has been both empirically- and theoretically-grounded, provide reflections on the value of these probes for enabling novel perspectives on food experiences, and on probes’ ability to capture what we called sensory fragments of participants’ experience reflecting distinct sensory aspects form both internal and external senses.
Trees and

CART is an unstable classifier resulting in significant changes in tree structure for small changes in the learning set (Breiman, Friedman et al. 1984; Breiman 1994). To address this problem, research into combining classifiers has increased significantly in the last few years (Breiman 1992; Wolpert 1992; Breiman 1994). These methods are of two basic types: concatenation uses the output from one classifier as input to the next classifier; parallel classifiers work on the same input data with the output from each classifier combined using regression or vote-counting techniques (Schurman 1996). These strategies greatly improve the predictive power of unstable classifiers. However, when the goal of the statistical analysis is to learn about the relationship between outcome and predictors, these strategies for combining classifiers are unacceptable since they produce a large number of trees, making interpretation difficult. We present a new method for combining classification trees which results in a single, interpretable tree. We begin by defining a distance metric between two trees based on the amount of rearrangement needed so that the structure of the two trees is identical. Using this distance metric, we develop the concept of the central, or median, tree structure and estimate it using a consensus rule. This tree is seen to be more centrally located than the tree fit to all the data. We finish by discussing future work including alternative methods for estimating the median tree, probability models, uses in data mining and meta-analysis, and performance measurements of the median tree.
The weight of words: textual data versus sentiment analysis in stock returns prediction

The focus of this paper is to understand whether the words contained in a text corpus improves the explained variance of stock returns better than the use of the polarity of the same texts, obtained through a sentiment analysis using a generic ontological dictionary. The empirical analysis is based on the content of a weekly column in the most important Italian financial newspaper, which published past information and analysts’ recommendations on listed companies. The use of textual data clearly increases the explained variance of stock returns but, through comparisons between data mining techniques, we observed minor differences in terms of MSE, by adding a selection of specific terms as features. In this context, the text mining approach proved to be very useful to improve the explanatory power of forecasting models, while it emerged the limited explanatory power of an automatic sentiment analysis based on a generic lexicon. Abstract Il focus di questo contributo è capire se le parole contenute in un corpus di testi migliorano la varianza spiegata dei rendimenti azionari rispetto all’uso della polarità degli stessi testi, ottenuta mediante una sentiment analysis utilizzando un dizionario ontologico generico. L’analisi empirica si basa sul contenuto di una rubrica settimanale del più importante quotidiano finanziario italiano, che ha pubblicato informazioni già note e raccomandazioni di analisti per una selezione di aziende. L’utilizzo di dati testuali chiaramente migliora la varianza spiegata dei rendimenti azionari ma, mediante il confronto tra diverse tecniche di data mining, abbiamo osservato modeste differenze in termini di MSE, quando abbiamo aggiunto alle variabili esplicative una selezione di parole. In questo contesto, l’approccio del text mining ha mostrato la sua utilità per migliorare la potenza esplicativa in modelli di previsione, mentre è emersa la scarsa capacità esplicativa di una sentiment analysis automatica basata su un dizionario ontologico generico.
A globally trust-region LP-Newton method for nonsmooth functions under the Hölder metric subregularity

We describe and analyse a globally convergent algorithm to find a possible nonisolated zero of a piecewise smooth mapping over a polyhedral set, such formulation includes Karush-Kuhn-Tucker (KKT) systems, variational inequalities problems, and generalized Nash equilibrium problems. Our algorithm is based on a modification of the fast locally convergent Linear Programming (LP)-Newton method with a trust-region strategy for globalization that makes use of the natural merit function. The transition between the global to local convergence occurs naturally under mild assumption. Our local convergence analysis of the method is performed under a Hölder metric subregularity condition of the mapping defining the possibly nonsmooth equation and the Hölder continuity of its gradient mapping of the selection mapping. We present numerical results that show the feasibility of the approach.
Archival gesture and critical attitude as an analytical leitmotiv in educational research

This article, through a theoretical discussion, intends to point out the meaning and some of the possible potentialities of archival gesture and critical attitude — as advocated by Michel Foucault and some of his later interlocutors — when conceived as analytical leitmotiv for research in the field of education. It is argued that the articulation of such key notion from the Foucaultian legacy in the investigative work and the resulting modi operandi often give rise to a certain way of problematizing the educational present, asking about their conditions of existence, their meanings, their rules of action, their effects and, in view of that, perhaps outlining possible transformations.
Mn3+-rich oxide/persistent luminescence nanoparticles achieve light-free generation of singlet oxygen and hydroxyl radicals for responsive imaging and tumor treatment

X-ray excited persistent luminescence (XEPL) imaging has attracted increasing attention in biomedical imaging due to elimination of autofluorescence, high signal-to-noise ratio and repeatable activation with high penetration. However, optical imaging still suffers from limited for high spatial resolution. Methods: Herein, we report Mn3+-rich manganese oxide (MnOx)-coated chromium-doped zinc gallogermanate (ZGGO) nanoparticles (Mn-ZGGOs). Enhanced XEPL and magnetic resonance (MR) imaging were investigated by the decomposition of MnOx shell in the environment of tumors. We also evaluated the tumor cell-killing mechanism by detection of reactive oxygen (ROS), lipid peroxidation and mitochondrial membrane potential changes in vitro. Furthermore, the in vivo biodistribution, imaging and therapy were studied by U87MG tumor-bearing mice. Results: In the tumor region, the MnOx shell is quickly decomposed to produce Mn3+ and oxygen (O2) to directly generate singlet oxygen (1O2). The resulting Mn2+ transforms endogenous H2O2 into highly toxic hydroxyl radical (·OH) via a Fenton-like reaction. The Mn2+ ions and ZGGOs also exhibit excellent T1-weighted magnetic resonance (MR) imaging and ultrasensitive XEPL imaging in tumors. Conclusion: Both the responsive dual-mode imaging and simultaneous self-supplied O2 for the production of 1O2 and oxygen-independent ·OH in tumors allow for more accurate diagnosis of deep tumors and more efficient inhibition of tumor growth without external activation energy.
Does Organizational Health Enhance the Level of Commitment in Police Employees?

Present study endeavored to investigate the role of organizational health in enhancing the level of commitment in police employees. A 3x2 factorial design with three levels of job hierarchy (officers, sub-inspectors & constables) and two levels of job tenure [short job tenure (0-5 yrs.) & long job tenure (above 10 Yrs.)] were used in present study . A total of 240 police personnel from Gorakhpur Zone participated as respondents. Organizational Health Questionnaire [30] and Organizational Commitment Scale [3] were used to determine the perception of organizational health and level of commitment of the employee towards their organization. Correlation results evinced that organizational health was positively correlated with various domains of organizational commitment. Organizational health was positively linked with affective commitment, normative commitment and overall commitment. Results revealed that different group of employees varied on perception of organizational health and commitment level. More specifically, police officers perceived greater level of organizational health as compared to sub-inspectors and constables respectively. Similarly, organizational commitments i.e., affective and normative as well as overall commitment were also greater in police officers as compared to sub inspectors and constables. Findings have been discussed in the light of organizational and personal factors.
Dvé: Improving DRAM Reliability and Performance On-Demand via Coherent Replication

As technologies continue to shrink, memory system failure rates have increased, demanding support for stronger forms of reliability. In this work, we take inspiration from the two-tier approach that decouples correction from detection and explore a novel extrapolation. We propose Dvé, a hardware-driven replication mechanism where data blocks are replicated in 2 different sockets across a cache-coherent NUMA system. Each data block is also accompanied by a code with strong error detection capabilities so that when an error is detected, correction is performed using the replica. Such an organization has the advantage of offering two independent points of access to data which enables: (a) strong error correction that can recover from a range of faults affecting any of the components in the memory, upto and including the memory controller, and (b) higher performance by providing another nearer point of memory access. Dvé realizes both of these benefits via Coherent Replication, a technique that builds on top of existing cache coherence protocols for not only keeping the replicas in sync for reliability, but also to provide coherent access to the replicas during fault-free operation for performance. Dvé can flexibly provide these benefits on-demand by simply using the provisioned memory capacity which, as reported in recent studies, is often underutilized in today’s systems. Thus, Dvé introduces a unique design point that offers higher reliability and performance for workloads that do not require the entire memory capacity.
Twistronics in Graphene, from Transfer Assembly to Epitaxy

The twistronics, which is arising from the moiré superlattice of the small angle between twisted bilayers of 2D materials like graphene, has attracted much attention in the field of 2D materials and condensed matter physics. The novel physical properties in such systems, like unconventional superconductivity, come from the dispersionless flat band that appears when the twist reaches some magic angles. By tuning the filling of the fourfold degeneracy flat bands, the desired effects are induced due to the strong correlation of the degenerated Bloch electrons. In this article, we review the twistronics in twisted biand multi-layer graphene (TBG and TMG), which is formed both by transfer assembly of exfoliated monolayer graphene and epitaxial growth of multilayer graphene on SiC substrates. Starting from a brief history, we then introduce the theory of flat band in TBG. In the following, we focus on the major achievements in this field: (a) van Hove singularities and charge order; (b) superconductivity and Mott insulator in TBG and (c) transport properties in TBG. In the end, we give the perspective of the rising materials system of twistronics, epitaxial multilayer graphene on the SiC.
Diﬀerentiating between in situ and invasive malignant breast lesions using natural language processing technology on radiological reports

The radiological reports contain a human assessment of the important features of breast lesions as visible on radiological images, and therefore, contain a lot of information for the characterization of breast lesions. Here we applied natural language processing (NLP) model to extract features of breast lesions from reports, and developed an end-to-end technology using NLP to predict whether malignant breast lesions are in situ or invasive. The results show that our NLP models can potentially be used to extract relevant features of breast lesions (F1 score = 0.915) and can be used to predict pathological results (areas under the receiver operating characteristic curve, AUC = 0.929) from radiological reports
Ethical leadership and employee ethical behaviour: exploring dual-mediation paths of ethical climate and organisational justice: empirical study on Iraqi organisations

Due to ethical lapses of leaders, interest in ethical leadership has grown, raising important questions about the responsibility of leaders in ensuring moral and ethical conduct. Research conducted on ethical leadership failed to investigate the active role that the characteristics of ethical climate and organisational justice have an increasing or decreasing influence on the ethical leadership in the organisation’s outcomes of employees’ ethical behaviour. Thus, this study examined the dual-mediations of work ethical climate and organisational justice on the relation of ethical leadership and ethical behaviour of employees. A total of 620 full-time employees from five Iraqi provinces working at 33 Iraqi organisations in the field of manufacturing, retailing, medical, insurance, information technology, legal, finance, and telecommunication responded to the questionnaire survey. Structural equation modelling (SEM) was used to test the model and data analysis was carried out using structural equation modelling-partial least square (SEM-PLS). The results revealed that there is a significant relationship between ethical leadership behaviour and the ethical behaviour of employees. Primarily, the study also found that ethical climate and organisational justice play a very significant mediating role between ethical leadership and employees’ ethical behaviour.
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator

Recent work demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication—the intensive and key computation in deep neural networks (DNNs). One key problem is the weights that are signed values. However, in a ReRAM crossbar, weights are stored as conductance of the crossbar cells, and the in-situ computation assumes all cells on each crossbar column are of the same sign. The current architectures either use two ReRAM crossbars for positive and negative weights (PRIME), or add an offset to weights so that all values become positive (ISAAC). Neither solution is ideal: they either double the cost of crossbars, or incur extra offset circuity. To better address this problem, we propose FORMS, a fine-grained ReRAM-based DNN accelerator with algorithm/hardware co-design. Instead of trying to represent the positive/negative weights, our key design principle is to enforce exactly what is assumed in the in-situ computation— ensuring that all weights in the same column of a crossbar have the same sign. It naturally avoids the cost of an additional crossbar. Such polarized weights can be nicely generated using alternating direction method of multipliers (ADMM) regularized optimization during the DNN training, which can exactly enforce certain patterns in DNN weights. To achieve high accuracy, we divide the crossbar into logical sub-arrays and only enforce this property within the fine-grained sub-array columns. Crucially, the small sub-arrays provides a unique opportunity for input zero-skipping, which can significantly avoid unnecessary computations and reduce computation time. At the same time, it also makes the hardware much easier to implement and is less susceptible to non-idealities and noise than coarse-grained architectures. Putting all together, with the same optimized DNN models, FORMS achieves 1.50× and 1.93× throughput improvement in terms of $\frac{{GOPs}}{{s \times m{m^2}}}$ and $\frac{{GOPs}}{W}$ compared to ISAAC, and 1.12× ~2.4 × speed up in terms of frame per second over optimized ISAAC with almost the same power/area cost. Interestingly, FORMS optimization framework can even speed up the original ISAAC from 10.7 × up to 377.9×, reflecting the importance of software/hardware co-design optimizations.
A clinical prediction model identifies a subgroup with inferior survival within intermediate risk acute myeloid leukemia

Intermediate risk acute myeloid leukemia (AML) comprises around 50% of AML patients and is featured with heterogeneous clinical outcomes. The study aimed to generate a prediction model to identify intermediate risk AML patients with an inferior survival. We performed targeted next generation sequencing analysis for 121 patients with 2017 European LeukemiaNet-defined intermediate risk AML, revealing 122 mutated genes, with 24 genes mutated in > 10% of patients. A prognostic nomogram characterized by white blood cell count ≥10×109/L at diagnosis, mutated DNMT3A and genes involved in signaling pathways was developed for 110 patients who were with clinical outcomes. Two subgroups were identified: intermediate low risk (ILR; 43.6%, 48/110) and intermediate high risk (IHR; 56.4%, 62/110). The model was prognostic of overall survival (OS) and relapse-free survival (RFS) (OS: Concordance index [C-index]: 0.703, 95%CI: 0.643-0.763; RFS: C-index: 0.681, 95%CI 0.620-0.741), and was successfully validated with two independent cohorts. Allogeneic hematopoietic stem cell transplantation (alloHSCT) reduced the relapse risk of IHR patients (3-year RFS: alloHSCT: 40.0±12.8% vs. chemotherapy: 8.6±5.8%, P= 0.010). The prediction model can help identify patients with an unfavorable prognosis and refine risk-adapted therapy for intermediate risk AML patients.
Amplifying the Wisdom of the Crowd, Building and Measuring for Expert and Moral Consensus

— Scientiﬁc and cultural progress mostly takes place in the marketplace of ideas. The internet has largely changed how this marketplace functions. Although it has brought many improvements, it has also created a new set of problems. We propose a web service that can be used to crowdsource the process of knowledge acquisition and of knowledge summary, mediate disagreements, and improve discourse on controversial issues, thereby improving the efﬁciency of the marketplace of ideas. Our approach involves a speciﬁc technique for integrating: a forum; a wiki organized into separate camps, with camps organized into a hierarchical structure; a survey system based upon camp support; a mechanism for dynamically reorganizing the structure of the camp hierarchy while mediating user disagreements; and a customizable mechanism for determining how votes are weighted based on credentials or expert assignment. A version of this approach has been implemented, released as open source, and a beta test has gone live. Initial use seems to validate the merits of this approach.
Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia

Changes in sea level and the formation of islands impact the distributions and abundances of local flora and fauna, with palaeo-environmental investigations providing a context for biological conservation. The palaeo-environmental knowledge of the north-west of Australia during the late Quaternary is sparse, particularly the impact of island formation on local faunas. In 1991 and 1993 Peter Veth and colleagues conducted archaeological surveys of the Montebello Islands, an archipelago situated 70 – 90 km from the present-day coastline of north-west Australia. A group of three caves were found during this survey on the eastern side of Campbell Island. Two of the caves, Noala and Hayne’s Caves, were analysed by Veth and colleagues in the early 1990s; the last cave, Morgan’s Cave, remained unanalysed because it contained negligible archaeological material. It provides an opportunity to refine the interpretation of palaeo-environmental conditions, further information on the original pre-European fauna of the north-west shelf, the formation of the islands due to sea level rise, and the impact of sea level rise on local faunas. The fossil fauna assemblage of Morgan’s Cave was sorted, identified to the lowest taxonomic level possible, and counted for analysis on relative abundance for paleoenvironmental interpretation. There are marked patterns of species loss and changing relative abundances in certain species, consistent with island formation due to sea level rise. This palaeoecological interpretation can be used in conservation efforts on nearby Barrow Island, a Class A ecological reserve, and management of faunas in conservation reserves and on islands. Cassia J. Piper. Department of Earth and Planetary Sciences, Western Australian Museum, 49 Kew Street, Welshpool, 6101. cassia.piper@gmail.com Peter M. Veth. Indian Oceans Institute, University of Western Australia, Perth, WA 6009. Peter.Veth@uwa.edu. au PIPER & VETH: PALAEOECOLOGY AND RISING SEAS 2