Runaway Feedback Loops in Predictive Policing

Predictive policing systems are increasingly used to determine how to allocate police across a city in order to best prevent crime. Discovered crime data (e.g., arrest counts) are used to help update the model, and the process is repeated. Such systems have been empirically shown to be susceptible to runaway feedback loops, where police are repeatedly sent back to the same neighborhoods regardless of the true crime rate. In response, we develop a mathematical model of predictive policing that proves why this feedback loop occurs, show empirically that this model exhibits such problems, and demonstrate how to change the inputs to a predictive policing system (in a black-box manner) so the runaway feedback loop does not occur, allowing the true crime rate to be learned. Our results are quantitative: we can establish a link (in our model) between the degree to which runaway feedback causes problems and the disparity in crime rates between areas. Moreover, we can also demonstrate the way in which \emph{reported} incidents of crime (those reported by residents) and \emph{discovered} incidents of crime (i.e. those directly observed by police officers dispatched as a result of the predictive policing algorithm) interact: in brief, while reported incidents can attenuate the degree of runaway feedback, they cannot entirely remove it without the interventions we suggest.Comment: Extended version accepted to the 1st Conference on Fairness, Accountability and Transparency, 2018. Adds further treatment of reported as well as discovered incident

Sensor network localization for moving sensors

pre-printSensor network localization (SNL) is the problem of determining the locations of the sensors given sparse and usually noisy inter-communication distances among them. In this work we propose an iterative algorithm named PLACEMENT to solve the SNL problem. This iterative algorithm requires an initial estimation of the locations and in each iteration, is guaranteed to reduce the cost function. The proposed algorithm is able to take advantage of the good initial estimation of sensor locations making it suitable for localizing moving sensors, and also suitable for the refinement of the results produced by other algorithms. Our algorithm is very scalable. We have experimented with a variety of sensor networks and have shown that the proposed algorithm outperforms existing algorithms both in terms of speed and accuracy in almost all experiments. Our algorithm can embed 120,000 sensors in less than 20 minutes

Radiation resistance of Ge, Ge0.93Si0.07, GaAs and Al0.08Ga0.92 as solar cells

Solar cells made of Ge, Ge(0.93)Si(0.07) alloys, GaAs and Al(0.08)Ga(0.92)As were irradiated in two experiments with 1-meV electrons at fluences as great as 1 x 10(exp 16) cm(exp-2). Several general trends have emerged. Low-band-gap Ge and Ge(0.93)Si(0.07) cells show substantial resistance to radiation-induced damage. The two experiments showed that degradation is less for Al(0.08)Ga(0.92)As cells than for similarly irradiated GaAs cells. Compared to homojunctions, cells with graded-band-gap emitters did not show the additional resistance to damage in the second experiment that had been seen in the first. The thickness of the emitter is a key parameter to limit the degradation in GaAs devices

Universal Chemomechanical Design Rules for Solid-Ion Conductors to Prevent Dendrite Formation in Lithium Metal Batteries

Dendrite formation during electrodeposition while charging lithium metal batteries compromises their safety. While high shear modulus solid-ion conductors (SICs) have been prioritized to resolve pressure-driven instabilities that lead to dendrite propagation and cell shorting, it is unclear whether these or alternatives are needed to guide uniform lithium electrodeposition, which is intrinsically density-driven. Here, we show that SICs can be designed within a universal chemomechanical paradigm to access either pressure-driven dendrite-blocking or density-driven dendrite-suppressing properties, but not both. This dichotomy reflects the competing influence of the SICs mechanical properties and partial molar volume of Li+ relative to those of the lithium anode on plating outcomes. Within this paradigm, we explore SICs in a previously unrecognized dendrite-suppressing regime that are concomitantly soft, as is typical of polymer electrolytes, but feature atypically low Li+ partial molar volume, more reminiscent of hard ceramics. Li plating mediated by these SICs is uniform, as revealed using synchrotron hard x-ray microtomography. As a result, cell cycle-life is extended, even when assembled with thin Li anodes and high-voltage NMC-622 cathodes, where 20 percent of the Li inventory is reversibly cycled

Graded-bandgap AlGaAs solar cells for AlGaAs/Ge cascade cells

Some p/n graded-bandgap Al(x)Ga(1-x)As solar cells were fabricated and show AMO conversion efficiencies in excess of 15 percent without antireflection (AR) coatings. The emitters of these cells are graded between 0.008 is less than or equal to x is less than or equal to 0.02 during growth of 0.25 to 0.30 micron thick layers. The keys to achieving this performance were careful selection of organometallic sources and scrubbing oxygen and water vapor from the AsH3 source. Source selection and growth were optimized using time-resolved photoluminescence. Preliminary radiation-resistance measurements show AlGaAs cells degraded less than GaAs cells at high 1 MeV electron fluences, and AlGaAs cells grown on GaAs and Ge substrates degrade comparably

Fairness in representation: quantifying stereotyping as a representational harm

While harms of allocation have been increasingly studied as part of the subfield of algorithmic fairness, harms of representation have received considerably less attention. In this paper, we formalize two notions of stereotyping and show how they manifest in later allocative harms within the machine learning pipeline. We also propose mitigation strategies and demonstrate their effectiveness on synthetic datasets.Comment: 9 pages, 6 figures, Siam International Conference on Data Minin

A structural decomposition-based diagnosis method for dynamic process systems using HAZID information

A novel diagnosis method is proposed in this paper that uses the results of the blended HAZID analysis extended to the dynamic case of process systems controlled by operational procedures. The algorithm is capable of finding fault root causes in process systems using nominal and observed possible faulty operational procedure execution traces. The algorithm uses the structural decomposition of the process system and its component-level dynamic HAZID (P-HAZID) tables and executes the diagnosis component-wise by first decomposing the observed execution traces, and then assembling the diagnosis results. The exact structure of the algorithm is also discussed, followed by two case studies on which its operation is demonstrated. © 2014 Elsevier Ltd