Training neural networks to encode symbols enables combinatorial generalization

Combinatorial generalization - the ability to understand and produce novel combinations of already familiar elements - is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research suggests that conventional neural networks can't solve this problem unless they are endowed with mechanisms specifically engineered for the purpose of representing symbols. In this paper we introduce a novel way of representing symbolic structures in connectionist terms - the vectors approach to representing symbols (VARS), which allows training standard neural architectures to encode symbolic knowledge explicitly at their output layers. In two simulations, we show that neural networks not only can learn to produce VARS representations, but in doing so they achieve combinatorial generalization in their symbolic and non-symbolic output. This adds to other recent work that has shown improved combinatorial generalization under specific training conditions, and raises the question of whether specific mechanisms or training routines are needed to support symbolic processing

Grounding relations and analogy-making in action : A dissertation submitted in partial fulfillment of the requirements for the degree of Ph.D. in Cognitive Science

This thesis presents an attempt to ground relational concepts and relational reasoning in actually executed or mentally simulated interactions with the environment. It is suggested that relations are encoded by executing or mentally simulating actions which are constrained by the environment and the specifics of the human body. The embodiment view of relations is discussed in light of contemporary theoretical, computational and empirical research of relations and relational reasoning. Two computer simulations based on the AMBR (Associative Memory Based Reasoning) model of analogy-making are reported. The first simulation describes how spatial relations are grounded in actually executed actions. The second simulation shows how other classes of relations, such as functional relations, are encoded and compared by simulated perceptual-motor interactions. A series of experiments which test the predictions of the embodiment approach to relations are described. The results of the experiments indicate that people simulate actions when comparing relations and that the process of relational comparison is constrained by the characteristics of the human body. Finally, the results of the computation and the empirical studies are put together and it is discussed to what extent the predictions of the model are supported by the experimental results. The shortcomings and limitations of the proposed approach are outlined and directions for future studies are given

High Voltage System for the CMS Forward Calorimeter

The high voltage system for the CMS forward calorimeters (HF) is described and the measurements of the critical parameters are tabulated

The human visual system and CNNs can both support robust online translation tolerance following extreme displacements

Visual translation tolerance refers to our capacity to recognize objects over a wide range of different retinal locations. Although translation is perhaps the simplest spatial transform that the visual system needs to cope with, the extent to which the human visual system can identify objects at previously unseen locations is unclear, with some studies reporting near complete invariance over 10 degrees and other reporting zero invariance at 4 degrees of visual angle. Similarly, there is confusion regarding the extent of translation tolerance in computational models of vision, as well as the degree of match between human and model performance. Here, we report a series of eye-tracking studies (total N = 70) demonstrating that novel objects trained at one retinal location can be recognized at high accuracy rates following translations up to 18 degrees. We also show that standard deep convolutional neural networks (DCNNs) support our findings when pretrained to classify another set of stimuli across a range of locations, or when a global average pooling (GAP) layer is added to produce larger receptive fields. Our findings provide a strong constraint for theories of human vision and help explain inconsistent findings previously reported with convolutional neural networks (CNNs)

Design, Performance, and Calibration of CMS Hadron-Barrel Calorimeter Wedges

Extensive measurements have been made with pions, electrons and muons on four production wedges of the Compact Muon Solenoid (CMS) hadron barrel (HB) calorimeter in the H2 beam line at CERN with particle momenta varying from 20 to 300 GeV/c. Data were taken both with and without a prototype electromagnetic lead tungstate crystal calorimeter (EB) in front of the hadron calorimeter. The time structure of the events was measured with the full chain of preproduction front-end electronics running at 34 MHz. Moving-wire radioactive source data were also collected for all scintillator layers in the HB. These measurements set the absolute calibration of the HB prior to first pp collisions to approximately 4%

Energy Response and Longitudinal Shower Profiles Measured in CMS HCAL and Comparison With Geant4

The response of the CMS combined electromagnetic and hadron calorimeter to beams of pions with momenta in the range 5-300 GeV/c has been measured in the H2 test beam at CERN. The raw response with the electromagnetic compartment calibrated to electrons and the hadron compartment calibrated to 300 GeV pions may be represented by sigma = (1.2) sqrt{E} oplus (0.095) E. The fraction of energy visible in the calorimeter ranges from 0.72 at 5 GeV to 0.95 at 300 GeV, indicating a substantial nonlinearity. The intrinsic electron to hadron ratios are fit as a function of energy and found to be in the range 1.3-2.7 for the electromagnetic compartment and 1.4-1.8 for the hadronic compartment. The fits are used to correct the non-linearity of the e pi response to 5% over the entire measured range resulting in a substantially improved resolution at low energy. Longitudinal shower profile have been measured in detail and compared to Geant4 models, LHEP-3.7 and QGSP-2.8. At energies below 30 GeV, the data, LHEP and QGSP are in agreement. Above 30 GeV, LHEP gives a more accurate simulation of the longitudinal shower profile

Synchronization and Timing in CMS HCAL

The synchronization and timing of the hadron calorimeter (HCAL) for the Compact Muon Solenoid has been extensively studied with test beams at CERN during the period 2003-4, including runs with 40 MHz structured beam. The relative phases of the signals from different calorimeter segments are timed to 1 ns accuracy using a laser and equalized using programmable delay settings in the front-end electronics. The beam was used to verify the timing and to map out the entire range of pulse shapes over the 25 ns interval between beam crossings. These data were used to make detailed measurements of energy-dependent time slewing effects and to tune the electronics for optimal performance