Miracle-dz/UniFAR-FTUs huggingface dataset:
Race, Four Farms, and a City: Color Blindness and the Austin, TX, Urban Farm Debate

Austin, TX, was the site of a three-year debate between urban farmers and their supporters and local community activists about how to rewrite an outdated farm code. There was tremendous animosity between the two groups and, despite efforts at mediation, the two sides were unable to reach compromise. To understand more about how these two groups came into conflict, we interviewed 26 local stakeholders about their views and experiences during the farm code debate and found that issues of race and racial inequality were a key factor in the continuing mistrust. We found that farmers and their supporters attempted to frame the debate and its racial undertones by highlighting their racial–ethnic minority supporters, describing their businesses as reclaiming East Austin’s agricultural past, and arguing that the only issue that should matter in redrawing the farm ordinance should be how to best help farmers provide healthy food to the community. We argue that the farmers’ responses draw from a discourse of whiteness and color blindness that can be harmful to People of Color and link their views to larger critiques that the alternative food movement and individual alternative food projects can be exclusionary.
Better Together: Enhancing Team Collaboration in Science (ETCSi) to Improve Well-being and Medicine Discovery

Public health is dependent on the discovery of new medicines, and due to the considerable investment already required, the discovery process needs to be made more efficient. One approach is to use positive psychology to enhance collaboration among scientific researchers, which would lead to the discovery of more medicines by enhancing innovation, problem solving, and decision making. Scientists are technical experts that are not trained in collaboration skills despite this being critical to their success in the workplace. A one-day training program has been developed here to enhance team collaboration in science (ETCSi). Building skills in trust, communication, and belonging will enhance scientist’s well-being. Additionally, the training will enhance collaboration among science discovery teams to improve the outcomes of their work: an increase in the number of new medicines to treat public health. This thesis will explain how investing in the well-being of scientists through enhancing collaboration will also improve their productivity, a win-win situation.
Design and Implementation of a Low Power RSA Processor for Smartcard

Power consumption limits the application of public key cryptosystem in portable devices. This paper proposes a low power design of 1,024-bit RSA. In algorithm, the Chinese Remainder Theorem (CRT) and an improved Montgomery algorithm are selected to decrease the computation of RSA. In architecture and circuit, the operand isolation technique is applied to avoid unnecessary flip-flops of the combinational logic, and the clock gating technique is used to reduce the power dissipation of the registers. The proposed design is functionally verified on Altera FPGA EP2C8Q208C8N device. With SMIC 0. 18μm CMOS process, the Synopsys synthesizing result shows that the area and the critical path are 7.1k gates and 5.3ns respectively, while the power is 2.56mW and the throughput can reach 49 kbps. Thus the proposed design requires lower power than previous designs.
On the extra mode and inconsistency of Hořava gravity

We address the consistency of Hořava's proposal for a theory of quantum gravity from the low-energy perspective. We uncover the additional scalar degree of freedom arising from the explicit breaking of the general covariance and study its properties. The analysis is performed both in the original formulation of the theory and in the Stuckelberg picture. A peculiarity of the new mode is that it satisfies an equation of motion that is of first order in time derivatives. At linear level the mode is manifest only around spatially inhomogeneous and time-dependent backgrounds. We find two serious problems associated with this mode. First, the mode develops very fast exponential instabilities at short distances. Second, it becomes strongly coupled at an extremely low cutoff scale. We also discuss the ``projectable'' version of Hořava's proposal and argue that this version can be understood as a certain limit of the ghost condensate model. The theory is still problematic since the additional field generically forms caustics and, again, has a very low strong coupling scale. We clarify some subtleties that arise in the application of the Stuckelberg formalism to Hořava's model due to its non-relativistic nature.
Rule-based Modeling and Simulation of Biochemical Systems with Molecular Finite

We propose a theoretical formalism, molecular finite automata (MFA), to describe individual proteins as rule-based computing machines. The MFA formalism provides a framework for modeling individual protein behaviors and systems-level dynamics via construction of programmable and executable machines. Models specified within this formalism explicitly represent the context-sensitive dynamics of individual proteins driven by external inputs and represent protein-protein interactions as synchronized machine reconfigurations. Both deterministic and stochastic simulations can be applied to quantitatively compute the dynamics of MFA models. We apply the MFA formalism to model and simulate a simple example of a signal transduction system that involves a MAP kinase cascade and a scaffold protein.
A Cortical Attractor Network with Martinotti Cells Driven by Facilitating Synapses

The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.
Compensation Techniques for Speaker Variability in Continuous Emotion Prediction

Continuous time-varying prediction of emotions based on speech in terms of attributes (i.e., arousal) has received considerable attention in the past few years. However, the variability introduced by factors not related to emotion, such as speaker and phonetic variability, which in turn may lead to less reliable models and less accurate emotion predictions, has not been fully explored yet. In particular, even though speaker variability has been shown to be a significant confounding factor in continuous emotion prediction systems, there remains a paucity of analyses about how speaker variability affects continuous emotion prediction systems and which methods can be applied to compensate for this variability. This paper first formulates speaker variability systematically in terms of probability distributions in both feature and model spaces, and quantifies the effect of speaker variability by comparing inter- and intra-speaker variability between speaker-dependent models. Second, two compensation techniques based on partial least squares dimensional reduction and feature mapping are proposed. Finally, the effectiveness of the proposed techniques is validated on three databases, across which they show consistent improvement in arousal, valence and dominance prediction. Additional quantitative analyse reveals that the two proposed techniques compensate for speaker variability in both the feature and model spaces simultaneously.
Perception of Spectrally Shifted Speech: Implications for Cochlear Implants

Objective: Present study used the normal hearing subjects to check the effect of spectral shifts in monoaural and binaural cochlear implant simulations. Cochlear implant signal processing was simulated using eight channel sine wave vocoders. Materials and methods:Thespectrallyshiftedsentences(HINT)werecreatedtosimulate20mm&25mminsertiondepthof cochlear implants. Speech recognition scores were assessed in three mono aural conditions (No shift, 25 mm & 20 mm insertion) and two binaural conditions [asymmetrical spectral shift (20 & 25 mm insertions in each ear) & no spectral shift in both ears]. Greenwood (1990) function was used to decide the corner frequencies for the simulation of spectral shifts. Results:Itreveledthatpartialshift(20mm)resultedinpoorscoresinmonauralthan25mmshift.Spectrallyunshiftedspeech resulted in significantly higher scores when compared to spectrally shifted speech. In binaural asymmetrical condition, scores were equivalent to 25 mm shift in monaural condition. Even in symmetrical condition also binaural scores were equivalent to mono aural scores which implies that at least in initial stages there may not be binaural integration or interference may be present.
Anomaly Detection on Collective Moving Patterns: A Hidden Markov Model Based Solution

Trajectories of people provide rich information about the collective behaviors, where the term collective behavior means the behavior of a large number of people as a whole. Abnormal people trajectories that are rarely observed may correspond with some unusual events, for instance, natural disasters, terrorism attacks, or traffic accidents. To detect such abnormal people trajectories, namely outliers, this paper presents a solution based on Hidden Markov Model (HMM). The motivation to use HMM for outlier detection on people trajectories is that time-varying people distribution can be modeled by using HMM and HMM can provide the probability that a sequence appears. Here, the time-varying people distributions with low probabilities to appear are regarded as outliers. Experiments with an artificial data set simulating collective behaviors and a real-world traffic data set validate the proposed solution.
A REVEALED PREFERENCE APPROACH TO VALUATIONS OF RISK REDUCTIONS: A CONTRACTOR’S SAMPLE

Radon is a naturally occurring tasteless, odourless gas. It is found everywhere, usually at negligible concentrations. It can enter buildings from porous soil and rock and usually dissipates harmlessly into the atmosphere (1). However, in enclosed spaces, such as houses, it can accumulate and pose health hazards, specifically an increased risk of lung cancer (2,3). On average radon contributes 50% of the natural background radiation dose to the general public (4). It is therefore an important source of radiation dosage in the general population and has been recognised in many countries as an environmental and public health issue.
On Cheating in CSMA/CA Ad Hoc Networks

CSMA/CA protocols rely on the random deferment of packet transmissions. Like most other protocols, CSMA/CA was designed with the assumption that the nodes would play by the rules. This is important, since the nodes themselves control their random deferment. However, with the higher programmability of the network adapters, the temptation to tamper with the software or firmware is likely to grow; in this way, a user could obtain a much larger share of the available bandwidth at the expense of other users. We use a game-theoretic approach to investigate the problem of the selfish behavior of nodes, specifically geared towards the most widely accepted protocol in this class of protocols, IEEE 802.11. We show that a selfish and non-cooperative behavior by a small population of (two or more) cheaters leads to a network collapse. We argue that this provides an incentive for cheaters to cooperate with each other. However, explicit cooperation among nodes is clearly impractical. By applying the model of dynamic games borrowed from game theory, we derive the conditions for the stable and optimal functioning of a population of cheaters. We use this insight to develop a simple, localized and distributed cheating protocol that needs no explicit cooperation between cheaters or \textit{a priori} knowledge about the total number of nodes/cheaters in the system. We show that the scheme successfully guides multiple selfish nodes to a Pareto-optimal Nash equilibrium.
Research of chaotic PSO coverage control algorithm based on energy balance

Network coverage is a key research problem in wireless sensor network field. Sensor node energy is generally supplied by batteries, and the energy is limited, so an effective energy balance strategy can extend the networks' life in the process of the coverage. A chaotic particle swarm optimization coverage control algorithm is proposed that based on the concept of energy balance. It utilizes chaotic algorithm to improve particle swarm optimization function, effectively avoiding the shortcomings of ”premature” in the process of the particle swarm optimization. It also could realize energy balance among the nodes in the process of network coverage. The simulation results show that the proposed algorithm has good performances for network coverage quality and energy balance.
Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix

In this paper, a multi-microphone noise reduction system based on the generalized sidelobe canceller (GSC) structure is investigated. The system consists of a fixed beamformer providing an enhanced speech reference, a blocking matrix providing a noise reference by suppressing the target speech, and a single-channel spectral post-filter. The spectral post-filter requires the power spectral density (PSD) of the residual noise in the speech reference, which can in principle be estimated from the PSD of the noise reference. However, due to speech leakage in the noise reference, the noise PSD is overestimated, leading to target speech distortion. To minimize the influence of the speech leakage, a maximum noise-to-speech ratio (MaxNSR) blocking matrix is proposed, which maximizes the ratio between the noise and the speech leakage in the noise reference. The proposed blocking matrix can be computed from the generalized eigenvalue decomposition of the correlation matrix of the microphone signals and the noise coherence matrix, which is assumed to be time-invariant. Experimental results in both stationary and nonstationary diffuse noise fields show that the proposed algorithm outperforms existing blocking matrices in terms of target speech blocking ability, noise estimation and noise reduction performance.
Binning Without a Model for Cone-beam CT

We present the first results of a new technique to bin Cone Beam projections without imposing any motion model. Such a technique is required for studying motion in regions of the body, such as the pelvis, where motion exists and is unpredictable. All motion information is obtained directly from the projections and the binning is performed through a type of best first search through the graph of possible complete assignments. Simplifying assumptions coupled with loss-less dimensional reduction using Principal Component Analysis, make the method tractable.
Adaptive SDF filters for recognition of partially occluded objects

One of the main problems in visual image processing is incomplete information owing an occlusion of objects by other objects. Since correlation filters mainly use contour information of objects to carry out pattern recognition then conventional correlation filters without training often yield a poor performance to recognize partially occluded objects. Adaptive correlation filters based on synthetic discriminant functions for recognition of partially occluded objects imbedded into a cluttered background are proposed. The designed correlation filters are adaptive to an input test scene, which is constructed with fragments of the target, false objects, and background to be rejected. These filters are able to suppress sidelobes of the given background as well as false objects. The performances of the adaptive filters in real scenes are compared with those of various correlation filters in terms of discrimination capability and robustness to noise.
Where Do You "Tube"? Uncovering YouTube Server Selection Strategy

YouTube is one of the most popular video sharing websites in the world. In order to serve its globally distributed users, it requires a massive-scale video delivery system. A major part of the whole system is to decide exactly what server machine is going to serve a client request at any given time. In this paper, we analyze DNS resolutions and video playback traces collected by playing half a million YouTube videos from geographically distributed PlanetLab nodes to uncover load- balancing and server selection strategies used by YouTube. Our results indicate that YouTube is aggressively deploying cache servers of widely varying sizes at many different locations around the world with several of them located inside other ISPs to reduce cost and improve the end-user performance. We also find that YouTube tries to use local "per-cache" load-sharing before resorting to redirecting a user to bigger/central cache locations.
Identification of motor imagery movements from EEG signals using Dual Tree Complex Wavelet Transform

In this paper, Dual Tree Complex Wavelet Transform (DTCWT) domain based feature extraction method has been proposed to identify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to enhance the weak brain signals and reduce noise, the EEG signals are decomposed into several bands of real and imaginary coefficients using DTCWT. The energy of the coefficients from relevant bands have been extracted as features and from the one way ANOVA analysis, scatter plots, box plots and histograms, this features are shown to be promising to distinguish various kinds of EEG signals. Publicly available benchmark BCI-competition 2003 Graz motor imagery dataset is used for this experiment. Among different types of classifiers developed such as support vector machine (SVM), probabilistic neural network (PNN), adaptive neuro fuzzy inference system (ANFIS) and K-nearest neighbor (KNN), KNN classifiers have been shown to provide a good mean accuracy of 91.07% which is better than several existing techniques.
Algorithm with Literal Renaming Strategy

Restricted on the proof of unsatisfiability of unsatisfiable formulas, a modified DPLL — RSMLS algorithm is presented. The new algorithm has a symmetry rule (Literal renaming ) and three simplified rule ((1,*)-Resolution, Subformula, and Multiple ). As an example, RSMLS algorithm is applied for the proof of unsatisfiability of pigeon-hole formula . We show with respect to RSMLS algorithm that has a refutation tree with at most O(n n n P 1 − n n P 1 − ) nodes.
Adaptive Harris Corner Detection Algorithm Based on B-Spline Function

Harris corner detection is a classic corner detection algorithm, but using Gaussian smoothing filter, there is phenomenon of corner information missing and corner migration. In this paper, to have a data-fitting and low-pass characteristics of B-spline function introduced into the algorithm, construct a new filter, while introducing the idea of image sub-block and the excluding neighboring points to build a B-spline-based adaptive Harris corner detection algorithm. The new corner detection algorithm to extract corner uniformity, positioning accuracy, and noise suppression is good. Through the experiment, the new algorithm significantly improves the image corner detection performance.
The UC Berkeley System for Interactive Visualization of Large Architectural Models

Realistic-looking architectural models with furniture may consist of millions of polygons and require gigabytes of data—far than today's workstations can render at interactive frame rates or store in physical memory. We have developed data structures and algorithms for identifying a small portion of a large model to load into memory and render during each frame of an interactive walkthrough. Our algorithms rely upon an efficient display database that represents a building model as a set of objects, each of which can be described at multiple levels of detail, and contains an index of spatial cells with precomputed cell-to-cell and cell-to-object visibility information. As the observer moves through the model interactively, a real-time visibility algorithm traces sightline beams through transparent cell boundaries to determine a small set of objects potentially visible to the observer. An optimization algorithm dynamically selects a level of detail and rendering algorithm with which to display each potentially visible object to meet a userspecified target frame time. Throughout, memory management algorithms predict observer motion and prefetch objects from disk that may become visible during imminent frames. This paper describes an interactive building walkthrough system that uses these data structures and algorithms to maintain interactive frame rates during visualization of very large models. So far, the implementation supports models whose major occluding surfaces are axis-aligned rectangles (e.g., typical buildings). This system is able to maintain over twenty frames per second with little noticeable detail elision during interactive walkthroughs of a building model containing over one million polygons.