Miracle-dz/UniFAR-FTUs huggingface dataset:
Tidal dissipation in rotating fluid bodies: a simplified model

We study the tidal forcing, propagation and dissipation of linear inertial waves in a rotating fluid body. The intentionally simplified model involves a perfectly rigid core surrounded by a deep ocean consisting of a homogeneous incompressible fluid. Centrifugal effects are neglected, but the Coriolis force is considered in full, and dissipation occurs through viscous or frictional forces. The dissipation rate exhibits a complicated dependence on the tidal frequency and generally increases with the size of the core. In certain intervals of frequency, efficient dissipation is found to occur even for very small values of the coefficient of viscosity or friction. We discuss the results with reference to wave attractors, critical latitudes and other features of the propagation of inertial waves within the fluid, and comment on their relevance for tidal dissipation in planets and stars.
Gait planning for a biped robot by a nonholonomic system with difference equation constraints

New gait planning using a nonholonomic model with difference equation constraints is proposed for biped robot walking. A model of a pivoting telescopic segment is used as the kinematic foothold selection model of a bipedal robot. The repetitive and discontinuous constraints of pivoting, expanding, and contracting make up the set of walking trajectory data. The k-step reachable region is defined as the set of the k-th state that the system can reach from the initial state, and the motion planning is solved using the Jacobian matrix of the state with regard to the input series. The difference equation constraints can be discussed as a digital control of continuous-time nonholonomic systems. The gait planning is modified based on the limiting condition for the HRP-2 humanoid robot. Energy consumption is evaluated based on the linear-pendulum model and the gait planning is optimized. The feasibility of the proposed walking planning is demonstrated through a numerical simulation and an experiment involving the HRP-2 humanoid robot.
Post-Truth Politics: The Effect of Reminders of Political Affiliation on Partisan Op-Ed Viewership

Conventional economic theory depicts human-decision making as logical and rational. However, recent research has demonstrated that humans act as an irrational agent more often than not, and will habitually prioritize attitudes, emotions, values, and beliefs over a marginal analysis in their decision-making calculus. As such, individuals will regularly undertake actions in order to avoid conflicts with their beliefs. In particular, information contradicting an individual’s beliefs may be avoided to preserve an individual’s identity (information avoidance). This paper investigates the phenomena of belief-based utility and information avoidance in the political realm, an area in which the literature regarding the aforementioned theories are relatively sparse. Specifically, we explored whether a reminder of political affiliation could influence subjects to avoid reading op-eds possessing headlines indicative of a position commonly held by an opposing political party. The hypothesis was tested through a survey distributed on Amazon Mechanical Turk, where half the participants received a reminder while the other half did not. The results suggest a statistically significant relationship between reminders and media access behavior — a reminder can have a demonstrable effect on media access behavior by causing individuals to avoid op-eds that advocate for the viewpoints of a conflicting political party. This has multiple implications (increasing political polarization, expanding influence of private interest groups, etc.) regarding media viewership habits for the individual undertaking decisions that may deprive them of useful information.
Distributed, Concurrent, and Independent Access to Encrypted Cloud Databases

Placing critical data in the hands of a cloud provider should come with the guarantee of security and availability for data at rest, in motion, and in use. Several alternatives exist for storage services, while data confidentiality solutions for the database as a service paradigm are still immature. We propose a novel architecture that integrates cloud database services with data confidentiality and the possibility of executing concurrent operations on encrypted data. This is the first solution supporting geographically distributed clients to connect directly to an encrypted cloud database, and to execute concurrent and independent operations including those modifying the database structure. The proposed architecture has the further advantage of eliminating intermediate proxies that limit the elasticity, availability, and scalability properties that are intrinsic in cloud-based solutions. The efficacy of the proposed architecture is evaluated through theoretical analyses and extensive experimental results based on a prototype implementation subject to the TPC-C standard benchmark for different numbers of clients and network latencies.
Governing Universities: Changing the Culture?

University governance - the historical and policy context governance and the management of higher education the governors - backgrounds and perceptions of higher education people like us? - the selection and appointment of university governors governing bodies - roles and organizational situations processes of governance - roles and decision-making key agents and relationships - vice-chancellors and chair of governing bodies clashes of culture - comparative perspectives on university governance changing patterns of governance?
Occupational Heat Stress Profiles in Selected Workplaces in India

Health and productivity impacts from occupational heat stress have significant ramifications for the large workforce of India. This study profiled occupational heat stress impacts on the health and productivity of workers in select organized and unorganized Indian work sectors. During hotter and cooler seasons, Wet Bulb Globe Temperatures (WBGT) were used to quantify the risk of heat stress, according to International workplace guidelines. Questionnaires assessed workers’ perceived health and productivity impacts from heat stress. A total of 442 workers from 18 Indian workplaces participated (22% and 78% from the organized and unorganized sector, respectively). Overall 82% and 42% of workers were exposed to higher than recommended WBGT during hotter and cooler periods, respectively. Workers with heavy workloads reported more heat-related health issues (chi square = 23.67, p ≤ 0.001) and reduced productivity (chi square = 15.82, p ≤ 0.001), especially the outdoor workers. Heat-rashes, dehydration, heat-syncope and urinogenital symptoms were self-reported health issues. Cited reasons for productivity losses were: extended-work hours due to fatigue/exhaustion, sickness/hospitalization and wages lost. Reducing workplace heat stress will benefit industries and workers via improving worker health and productivity. Adaptation and mitigation measures to tackle heat stress are imperative to protect the present and future workforce as climate change progresses.
Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.
Toward a feature classification scheme for network intrusion detection

One of the most important phases of the IDS/IPS design identifies the set of features that the system will use. This decision may influence both the performance of the system, and the types of attacks that it will detect. We present a feature classification schema for network intrusion detection, along with the implementation design, intended to provide a better understanding regarding the features that can be extracted from network packets. Our preliminary results show that the proposed schema has the potential to map each of the proposed feature groups into specific types of attacks
Succeeding by Specializing? Explaining Gendered Academic Career Trajectories

In this paper we take a longitudinal perspective to analyze gender differences in academic career attainment. We improve upon prior research both theoretically and methodologically. Theoretically, we introduce and conceptualize the influence of a heretofore-neglected factor in career attainment: the extent of research specialization. We conceive of the extent of research specialization as a form of professional capital that improves productivity and visibility, especially for men. Methodologically, we introduce a measure of research specialization and examine how men’s and women’s productivity and visibility unfold over time rather than examining discrete periods of scholars’ careers. We find that specializing is most beneficial with regard to productivity – that is, it helps scholars publish more. Specialization’s effects on visibility are less general: surprisingly, only women’s visibility seems to benefit from specialization. We discuss these findings, their implications, and suggest avenues for future research on this important and newly recognized form of capital. * We gratefully acknowledge research funding from the American Sociological Association’s Funds for the Advancement of the Disicpline (FAD) grant. Address all communication to Erin Leahey, Department of Sociology, University of Arizona, P.O. Box 210027, Tucson, AZ 857210027 (leahey@arizona.edu). The second and third authors are listed in alphabetical order to denote equal contribution.
Cognitive Deep Machine Can Train Itself

Machine learning is making substantial progress in diverse applications. The success is mostly due to advances in deep learning. However, deep learning can make mistakes and its generalization abilities to new tasks are questionable. We ask when and how one can combine network outputs, when (i) details of the observations are evaluated by learned deep components and (ii) facts and confirmation rules are available in knowledge based systems. We show that in limited contexts the required number of training samples can be low and self-improvement of pre-trained networks in more general context is possible. We argue that the combination of sparse outlier detection with deep components that can support each other diminish the fragility of deep methods, an important requirement for engineering applications. We argue that supervised learning of labels may be fully eliminated under certain conditions: a component based architecture together with a knowledge based system can train itself and provide high quality answers. We demonstrate these concepts on the State Farm Distracted Driver Detection benchmark. We argue that the view of the Study Panel (2016) may overestimate the requirements on `years of focused research' and `careful, unique construction' for `AI systems'.
Evolving a Neural Net-Based Decision and Search Heuristic for DPLL SAT Solvers

Solvers for the Boolean satisfiability problem are an important base technology for many applications. The most efficient SAT solvers for industrial applications are based on the DPLL algorithm with clause learning and conflict analysis dependent decision heuristics. The solver MINISAT V1.14 was modified to use a neural-net-based decision heuristic and search strategy. The weights and biases of the multilayer feedforward neural net are generated by an evolution strategy which is trained on a sample set of SAT problems. Problems solved with the evolved solutions encounter a similar number of conflicts as the original program, but require a higher number of decisions.
Consciousness is Pattern Recognition

This is a proof of the strong AI hypothesis, i.e. that machines can be conscious. It is a phenomenological proof that pattern-recognition and subjective consciousness are the same activity in different terms. Therefore, it proves that essential subjective processes of consciousness are computable, and identifies significant traits and requirements of a conscious system. Since Husserl, many philosophers have accepted that consciousness consists of memories of logical connections between an ego and external objects. These connections are called "intentions. " Pattern recognition systems are achievable technical artifacts. The proof links this respected introspective philosophical theory of consciousness with technical art. The proof therefore endorses the strong AI hypothesis and may therefore also enable a theoretically-grounded form of artificial intelligence called a "synthetic intentionality," able to synthesize, generalize, select and repeat intentions. If the pattern recognition is reflexive, able to operate on the set of intentions, and flexible, with several methods of synthesizing intentions, an SI may be a particularly strong form of AI. Similarities and possible applications to several AI paradigms are discussed. The article then addresses some problems: The proof's limitations, reflexive cognition, Searles' Chinese room, and how an SI could "understand" "meanings" and "be creative. "
A 23/3-dB dual-gain low-noise amplifier for 5-GHz-band wireless applications

We have developed a wide-dynamic-range low-noise amplifier (LNA) based on InGaP/GaAs hetero bipolar transistors (HBTs) for 5-GHz-band wireless applications. With no external matching components, this dual-gain LNA (with a 20-dB gain attenuation function) has a very high performance; namely, a noise figure (NF) of 2.3 dB, a gain of 23 dB, an output 1-dB compression point(P/sub 1dB/) of 9.8 dBm, and a DC power consumption (P/sub dc/) of 28 mW at 3.0 V. Its figure of merit, defined as (gain/NF)/spl middot/(P/sub 1dB//Pd/sub dc/), is 3.3, which is the highest value in the C-band to date.
Active SLAM in structured environments

This paper considers the trajectory planning problem for line-feature based SLAM in structured indoor environments. The robot poses and line features are estimated using smooth and mapping (SAM) which is found to provide more consistent estimates than the extended Kalman filter (EKF) The objective of trajectory planning is to minimise the uncertainty of the estimates and to maximise coverage. Trajectory planning is performed using model predictive control (MPC) with an attractor incorporating long term goals. This planning is demonstrated both in simulation and in a real-time experiment with a Pioneer2DX robot.
An Effective Clustering Algorithm for Auto-Detecting Well-Separated Clusters

Clustering is an important analysis method commonly used in many areas, including data mining, image processing, statistics, biology, and machine learning. In this paper, we introduce a novel effective clustering method based on Euclidean Distance called Self-Increase Clustering (SIC) for detecting well-separated clusters that can be either convex or non convex sets. Unlike most of the prevalent clustering algorithms, SIC does not require any initial parameters such as the number of clusters produced. Instead, SIC can discover the clusters number automatically based on the distribution of input data and separate these clusters effectively. In each iteration of SIC, a new cluster containing one randomly selected object is created and then this cluster increases by merging itself with the other objects or clusters near-by if certain criterion is satisfied. We evaluate SIC both from theoretical as well as practical points of view, and the experimental results show that SIC works effectively and efficiently on different data sets.
Implications of MMP9 for Blood Brain Barrier Disruption and Hemorrhagic Transformation Following Ischemic Stroke

Numerous studies have documented increases in matrix metalloproteinases (MMPs), specifically MMP-9 levels following stroke, with such perturbations associated with disruption of the blood brain barrier (BBB), increased risk of hemorrhagic complications, and worsened outcome. Despite this, controversy remains as to which cells release MMP-9 at the normal and pathological BBB, with even less clarity in the context of stroke. This may be further complicated by the influence of tissue plasminogen activator (tPA) treatment. The aim of the present review is to examine the relationship between neutrophils, MMP-9 and tPA following ischemic stroke to elucidate which cells are responsible for the increases in MMP-9 and resultant barrier changes and hemorrhage observed following stroke.
Cocoa Polyphenols and Their Potential Benefits for Human Health

This paper compiles the beneficial effects of cocoa polyphenols on human health, especially with regard to cardiovascular and inflammatory diseases, metabolic disorders, and cancer prevention. Their antioxidant properties may be responsible for many of their pharmacological effects, including the inhibition of lipid peroxidation and the protection of LDL-cholesterol against oxidation, and increase resistance to oxidative stress. The phenolics from cocoa also modify the glycemic response and the lipid profile, decreasing platelet function and inflammation along with diastolic and systolic arterial pressures, which, taken together, may reduce the risk of cardiovascular mortality. Cocoa polyphenols can also modulate intestinal inflammation through the reduction of neutrophil infiltration and expression of different transcription factors, which leads to decreases in the production of proinflammatory enzymes and cytokines. The phenolics from cocoa may thus protect against diseases in which oxidative stress is implicated as a causal or contributing factor, such as cancer. They also have antiproliferative, antimutagenic, and chemoprotective effects, in addition to their anticariogenic effects.
Downlink Power Control in CDMA Using Closed Loop Distance Based Power Allocation ( DBPA ) Algorithm

For wireless communication systems, power control is an effective technique to reduce cochannel interference and increase capacity for cellular radio systems. Iterative power control algorithms have been proposed to minimize transmitter powers while maintaining reliable communication between mobiles and base stations. To derive deterministic convergence results, these algorithms require perfect measurements of one or more of the following parameters: 1) the mobile’s signal-tointerference ratio (SIR) at the receiver; 2) the interference experienced by the mobile; and 3) the bit-error rate. The proposed work considers the power control schemes which are of prime importance for the CDMA wireless system capacity. Since the same radio channel is shared by several system users, each using a specific spread-spectrum pseudo-noise (PN) code, the capacity of the system is maximized if each mobile transmitter’s power controlled so that its signal arrives at the receiver with minimum signal-to-interference ratio required for an acceptable performance.In our work we use closed loop distance based power control algorithm.
Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups

With the astonishing rate that genomic and metagenomic sequence data sets are accumulating, there are many reasons to constrain the data analyses. One approach to such constrained analyses is to focus on select subsets of gene families that are particularly well suited for the tasks at hand. Such gene families have generally been referred to as “marker” genes. We are particularly interested in identifying and using such marker genes for phylogenetic and phylogeny-driven ecological studies of microbes and their communities (e.g., construction of species trees, phylogenetic based assignment of metagenomic sequence reads to taxonomic groups, phylogeny-based assessment of alpha- and beta-diversity of microbial communities from metagenomic data). We therefore refer to these as PhyEco (for phylogenetic and phylogenetic ecology) markers. The dual use of these PhyEco markers means that we needed to develop and apply a set of somewhat novel criteria for identification of the best candidates for such markers. The criteria we focused on included universality across the taxa of interest, ability to be used to produce robust phylogenetic trees that reflect as much as possible the evolution of the species from which the genes come, and low variation in copy number across taxa. We describe here an automated protocol for identifying potential PhyEco markers from a set of complete genome sequences. The protocol combines rapid searching, clustering and phylogenetic tree building algorithms to generate protein families that meet the criteria listed above. We report here the identification of PhyEco markers for different taxonomic levels including 40 for “all bacteria and archaea”, 114 for “all bacteria (greatly expanding on the ∼30 commonly used), and 100 s to 1000 s for some of the individual phyla of bacteria. This new list of PhyEco markers should allow much more detailed automated phylogenetic and phylogenetic ecology analyses of these groups than possible previously.
Fine-grained I/O access control of the mobile devices based on the Xen architecture

System virtualization is now available for mobile devices allowing for many advantages. Two of the major benefits from virtualization are system fault isolation and security. The isolated driver domain (IDD) model, a widely adopted architecture, enables strong system fault isolation by limiting the impact of driver faults to the driver domain itself. However, excessive I/O requests from a malicious domain to an IDD can cause CPU overuse of the IDD and performance degradation of applications in the IDD and other domains that share the same I/O device with the malicious do-main. If the IDD model is applied to mobile devices, this failure of performance isolation could also lead to battery drain, and thus it introduces a new severe threat to mobile devices. In order to solve this problem, we propose a fine-grained I/O access control mechanism in an IDD. Requests from guest domains are managed by an accounting module in terms of CPU usage, with the calcula-tion of estimated CPU consumption using regression equations. The requests are scheduled by an I/O access control enforcer ac-cording to security policies. As a result, our mechanism provides precise control on the CPU usage of a guest domain due to I/O device access, and prevents malicious guest domains from CPU overuse, performance degradation, and battery drain. We have implemented a prototype of our approach considering both network and storage devices with a real smart phone (SGH-i780) that runs two para-virtualized Linux kernels on top of Secure Xen on ARM. The evaluation shows our approach effectively protects a smart phone against excessive I/O attacks and guarantees availability.