A Multi-Country Study of Inter-Generational Educational Mobility

This paper analyses intergenerational educational mobility using survey data for twenty countries. We find that a number of interesting patterns emerge. Estimating a measure of mobility as movement and an index of mobility as equality of opportunity we find that while these two measures are positively correlated, the correlation is far from perfect. Examining the link with educational inequality we find evidence which suggests an inverse relationship between mobility and inequality consistent with egalitarian theory. The relationship between mobility appears to be weak, high returns to education do not depress mobility, as some human capital theories would suggest. Mobility appears to be somewhat higher for men whereas equality is much the same for both sexes. There is evidence that mobility as equality of opportunity has risen consistent with modernization theory. There is no evidence that expansion of third level education has led to a fall in the penalty associated with having a low educated parent. Estimates of marginal mobility are quite different from average mobility.

Psychedelic-Assisted Psychotherapy for Existential Suffering: Facilitating Self-Transcendence at the End-of-Life

Although existential suffering is amongst the most devastating forms of distress experienced by many patients nearing the end-of-life, it is often unsatisfactorily addressed due to a paucity of effective interventions. However, both historic and recent studies of psychedelic-assisted psychotherapy have reported marked alleviation of this suffering. As such, this article seeks to advance the rationale for the use of psychedelic substances in the provision of psychedelic-assisted psychotherapy for patients nearing the end-of-life. It begins with an overview of the classic psychedelics and their application in psychotherapy, highlighting recent studies. This is followed with a conceptual overview of existential suffering at the end-of-life and the process of selftranscendence. These sections are then integrated in a theoretical rationale for psychedelic-assisted mystical states as a means of facilitating the development of self-transcendence and, through it, the remediation of existential suffering. The paper concludes with a discussion of practical and philosophical considerations germane to the safe and ethical application of psychedelics in healthcare. In particular, developmental considerations for assessing both therapist and patient applicability in utilizing this modality are proposed

Psychedelic-Assisted Psychotherapy for Existential Suffering: Facilitating Self-Transcendence at the End-of-Life

Although existential suffering is amongst the most devastating forms of distress experienced by many patients nearing the end-of-life, it is often unsatisfactorily addressed due to a paucity of effective interventions. However, both historic and recent studies of psychedelic-assisted psychotherapy have reported marked alleviation of this suffering. As such, this article seeks to advance the rationale for the use of psychedelic substances in the provision of psychedelic-assisted psychotherapy for patients nearing the end-of-life. It begins with an overview of the classic psychedelics and their application in psychotherapy, highlighting recent studies. This is followed with a conceptual overview of existential suffering at the end-of-life and the process of selftranscendence. These sections are then integrated in a theoretical rationale for psychedelic-assisted mystical states as a means of facilitating the development of self-transcendence and, through it, the remediation of existential suffering. The paper concludes with a discussion of practical and philosophical considerations germane to the safe and ethical application of psychedelics in healthcare. In particular, developmental considerations for assessing both therapist and patient applicability in utilizing this modality are proposed

MultIOD: Rehearsal-free Multihead Incremental Object Detector

Class-Incremental learning (CIL) is the ability of artificial agents to accommodate new classes as they appear in a stream. It is particularly interesting in evolving environments where agents have limited access to memory and computational resources. The main challenge of class-incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one. Unfortunately, most existing class-incremental object detectors are applied to two-stage algorithms such as Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe that the current benchmarks are not realistic, and more effort should be dedicated to anchor-free and rehearsal-free object detection. In this context, we propose MultIOD, a class-incremental object detector based on CenterNet. Our main contributions are: (1) we propose a multihead feature pyramid and multihead detection architecture to efficiently separate class representations, (2) we employ transfer learning between classes learned initially and those learned incrementally to tackle catastrophic forgetting, and (3) we use a class-wise non-max-suppression as a post-processing technique to remove redundant boxes. Without bells and whistles, our method outperforms a range of state-of-the-art methods on two Pascal VOC datasets.Comment: Under review at the WACV 2024 conferenc

PIPE : Parallelized Inference Through Post-Training Quantization Ensembling of Residual Expansions

Deep neural networks (DNNs) are ubiquitous in computer vision and natural language processing, but suffer from high inference cost. This problem can be addressed by quantization, which consists in converting floating point perations into a lower bit-width format. With the growing concerns on privacy rights, we focus our efforts on data-free methods. However, such techniques suffer from their lack of adaptability to the target devices, as a hardware typically only support specific bit widths. Thus, to adapt to a variety of devices, a quantization method shall be flexible enough to find good accuracy v.s. speed trade-offs for every bit width and target device. To achieve this, we propose PIPE, a quantization method that leverages residual error expansion, along with group sparsity and an ensemble approximation for better parallelization. PIPE is backed off by strong theoretical guarantees and achieves superior performance on every benchmarked application (from vision to NLP tasks), architecture (ConvNets, transformers) and bit-width (from int8 to ternary quantization).Comment: arXiv admin note: substantial text overlap with arXiv:2203.1464

Gradient-Based Post-Training Quantization: Challenging the Status Quo

Quantization has become a crucial step for the efficient deployment of deep neural networks, where floating point operations are converted to simpler fixed point operations. In its most naive form, it simply consists in a combination of scaling and rounding transformations, leading to either a limited compression rate or a significant accuracy drop. Recently, Gradient-based post-training quantization (GPTQ) methods appears to be constitute a suitable trade-off between such simple methods and more powerful, yet expensive Quantization-Aware Training (QAT) approaches, particularly when attempting to quantize LLMs, where scalability of the quantization process is of paramount importance. GPTQ essentially consists in learning the rounding operation using a small calibration set. In this work, we challenge common choices in GPTQ methods. In particular, we show that the process is, to a certain extent, robust to a number of variables (weight selection, feature augmentation, choice of calibration set). More importantly, we derive a number of best practices for designing more efficient and scalable GPTQ methods, regarding the problem formulation (loss, degrees of freedom, use of non-uniform quantization schemes) or optimization process (choice of variable and optimizer). Lastly, we propose a novel importance-based mixed-precision technique. Those guidelines lead to significant performance improvements on all the tested state-of-the-art GPTQ methods and networks (e.g. +6.819 points on ViT for 4-bit quantization), paving the way for the design of scalable, yet effective quantization methods

Chirped Pulse Spectrometer Operating at 200 GHz

The combination of electronic sources operating at high frequencies and modern microwave instrumentation has enabled the recent development of chirped-pulse spectrometers for the millimetre and THz bands. This type of instrument can operate at high resolution which is particularly suited to gas phase rotational spectroscopy. The construction of a chirped pulse spectrometer operating at 200 GHz is described in detail while attention is paid to the phase stability and the data accumulation over many cycles. Validation using carbonyl sulphide has allowed the detection limit of the instrument to be established as function of the accumulation. A large number of OCS transitions were identified using a 10 GHz chirped pulse and include the 6 most abundant isotopologues, the weakest line corresponding to the fundamental R(17) transition of 16 O 13 C 33 S with a line strength of 4.3 x 10-26 cm-1 /(molec.cm-2). The linearity of the system response for different degrees of data accumulation and transition line strength was confirmed over 4 orders of magnitudes. A simple analysis of the time domain data was demonstrated to provide the line broadening coefficient without the need for conversion by a Fourier transform. Finally, the pulse duration is discussed and optimal values are given for both Doppler limited and collisional regimes