Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations

The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods. The promoters of GreenAI suggested the use of Floating Point Operations (FLOPs) as a measure of the computational cost of Neural Networks; however, that measure does not correlate well with the energy consumption of hardware equipped with massively parallel processing units like GPUs or TPUs. In this article, we propose a simple refinement of the formula used to compute floating point operations for convolutional layers, called alpha-FLOPs, explaining and correcting the traditional discrepancy with respect to different layers, and closer to reality. The notion of alpha-FLOPs relies on the crucial insight that, in case of inputs with multiple dimensions, there is no reason to believe that the speedup offered by parallelism will be uniform along all different axes

Image embedding for denoising generative models

Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of embedding an image into the latent space of Denoising Diffusion Models, that is finding a suitable “noisy” image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images

To be or not to be stable, that is the question: understanding neural networks for inverse problems

The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem, since the ill-conditioning amplifies the noise on the data. Recently introduced deep-learning based algorithms overwhelm the more traditional model-based approaches but they typically suffer from instability with respect to data perturbation. In this paper, we theoretically analyse the trade-off between neural networks stability and accuracy in the solution of linear inverse problems. Moreover, we propose different supervised and unsupervised solutions, to increase network stability by maintaining good accuracy, by inheriting, in the network training, regularization from a model-based iterative scheme. Extensive numerical experiments on image deblurring confirm the theoretical results and the effectiveness of the proposed networks in solving inverse problems with stability with respect to noise.Comment: 26 pages, 9 figures, divided in 4 blocks of figures in the LaTeX code. Paper will be sent for publication on a journal soon. This is a preliminary version, updated versions will be uploaded on ArXi

A Graph-based Optimization Framework for Hand-Eye Calibration for Multi-Camera Setups

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.Comment: This paper has been accepted for publication at the 2023 IEEE International Conference on Robotics and Automation (ICRA

Efficacy of modified atkins ketogenic diet in chronic cluster headache. An open-label, single-arm, clinical trial

Introduction: Drug-resistant cluster headache (CH) is still an open clinical challenge. Recently, our group observed the clinical efficacy of a ketogenic diet (KD), usually adopted to treat drug-resistant epilepsies, on migraine. Aim: Here, we aim to detect the effect of KD in a group of drug-resistant chronic CH (CCH) patients. Materials and methods: Eighteen drug-resistant CCH patients underwent a 12-week KD (Modified Atkins Diet, MAD), and the clinical response was evaluated in terms of response (>= 50% attack reduction). Results: Of the 18 CCH patients, 15 were considered responders to the diet (11 experienced a full resolution of headache, and 4 had a headache reduction of at least 50% in terms of mean monthly number of attacks during the diet). The mean monthly number of attacks for each patient at the baseline was 108.71 (SD = 81.71); at the end of the third month of diet, it was reduced to 31.44 (SD = 84.61). Conclusion: We observed for the first time that a 3-month ketogenesis ameliorates clinical features of CCH

Massive non-natural proteins structure prediction using grid technologies

Background The number of natural proteins represents a small fraction of all the possible protein sequences and there is an enormous number of pr oteins never sampled by nature, the so called "never born proteins" (NBPs). A fundamental question in this regard is if the ensemble of natural proteins possesses peculiar chemical and physical properties or if it is just the product of contingency coupled to functional selection. A key feature of natural proteins is thei r ability to form a well defined three-dimensional structure. T hus, the structural study of NBPs can help to understand if natural protein sequences were selecte d for their peculiar properties or if they are just one of the possible stable and functional ensembles. Methods The structural characterization of a huge number of random proteins cannot be approached experimentally, thus the problem has been tackled using a computational approach. A large random protein sequences library (2 × 10 ^4 sequences) was generated, discarding amino acid sequences with significant simi larity to natural proteins, and the corresponding structures were predicted using Rosetta. Given th e highly computational demanding problem, Rosetta was ported in grid and a user friendly job submission environment was developed within the GENIUS Grid Portal. Protein structures generated were analysed in terms of net charge, secondary structure content, surface/volume ratio, hydrophobic core composition, etc. Results The vast majority of NBPs, according to the Rosetta mode l, are characterized by a compact three-dimensional structure with a high secondary structure content. Structure compactness and surface polarity are comparable to those of natural proteins, suggesting similar stability and solubility. Deviations are observed in α helix- β strands relative content and inydrophobic core composition, as NBPs appear to be richer in helical structure and aromatic amino acids with respect to natural proteins. Conclusion The results obtained suggest that the abil ity to form a compact, ordered and water-soluble structure is an intrinsic property of polypeptides. The tendency of random sequences to adopt α helical folds indicate that all-α proteins may have emerged ea rly in pre-biotic evolution. Further, the lower percentage of aromatic residu es observed in natural proteins has important evolutionary implications as far as tolerance to mutati ons is concerned

Factors influencing choice of chemotherapy in metastatic colorectal cancer (mCRC)

Management of metastatic colorectal cancer requires a multimodal approach and must be performed by an experienced, multidisciplinary expert team. The optimal choice of the individual treatment modality, according to disease localization and extent, tumor biology, and patient clinical characteristics, will be one that can maintain quality of life and long-term survival, and even cure selected patients. This review is an overview of the different therapeutic approaches available in metastatic colorectal cancer, for the purpose of defining personalized therapeutic algorithms according to tumor biology and patient clinical features

Different Effects of Angiotensin Converting Enzyme Inhibitors on Endothelin-1 and Nitric Oxide Balance in Human Vascular Endothelial Cells: Evidence of an Oxidant-Sensitive Pathway

Angiotensin converting enzyme inhibitors (ACE-I) are able to reduce the formation of the potent vasoconstrictor endothelin-1 and increase nitric oxide bioavailability in human vascular endothelial cells (HUVECs). We tested the effects of two sulfhydryl-containing ACE-I, zofenoprilat, and captopril, and two nonsulfhydryl containing ACE-I, enalaprilat and lisinopril, on endothelin-1/nitric oxide balance and oxidative stress in HUVECs. All the four tested ACE-I reduced endothelin-1 secretion and increased nitric oxide metabolite production by HUVECs. However, zofenoprilat (−42% after 8 hours of incubation) was more effective (P < .05) than enalaprilat (−25%), lisinopril (−21%), and captopril (−30%) in reducing endothelin-1 secretion. Similarly, zofenoprilat (+110% after 8 hours of incubation) was more effective (P < .05) than enalaprilat (+64%), lisinopril (+63%), and captopril (+65%) in increasing nitric oxide metabolite production. The effect of ACE-I on endothelin-1 and nitric oxide metabolite production is mediated by the activation of bradykinin B2 receptor being counteracted, at least in part, by a specific antagonist. Zofenoprilat and, to a lesser extent, captopril also reduced oxidative stress in HUVECs. In conclusion, among the four tested ACE-I, zofenoprilat was more effective in improving endothelin-1/nitric oxide balance in HUVECs likely because of its greater antioxidant properties

Correlation between NK function and response to trastuzumab in metastatic breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Trastuzumab is a monoclonal antibody selectively directed against Her2 and approved for the treatment of Her2 overexpressing breast cancer patients. Its proposed mechanisms of action include mediation of antibody-dependent cellular cytotoxicity (ADCC) by triggering FcγRIII on natural killer (NK) cells. This study addresses the correlation between overall NK function and trastuzumab's clinical activity.</p> <p>Subjects and methods</p> <p>Clinical and immunological responses were assessed in 26 patients receiving trastuzumab monotherapy as maintenance management after chemotherapy (8 mg/kg load and then standard doses of 6 mg/kg every 3 weeks). Cytotoxic activity against the MHC class I-negative standard NK target K562 cell line and HER2-specific ADCC against a trastuzumab-coated Her2-positive SKBR3 cell line were assessed in peripheral blood mononuclear cells (PBMC) harvested after the first standard dose. After six months, seventeen patients were scored as responders and nine as non-responders according to the RECIST criteria, while Progression-Free Survival (PFS) was calculated during a 12 months follow-up.</p> <p>Results</p> <p>The responders had significantly higher levels of both NK and ADCC activities (p < 0.05) that were not different from those of eleven normal controls. The NK activity of the non-responders was significantly (p < 0.05) lower than that of the normal controls. At twelve months, there was a marked correlation between PFS and NK activity only. PFS was significantly longer in patients with high levels of NK activity, whereas its pattern was unrelated to high or low ADCC activity.</p> <p>Conclusion</p> <p>One of the mechanisms of action of trastuzumab is NK cell-mediated ADCC lysis of the Her2-positve target cell. We show here that its potency is correlated with the short-term response to treatment, whereas longer protection against tumor expansion seems to be mediated by pure NK activity.</p

Short-latency afferent inhibition and somato-sensory evoked potentials during the migraine cycle: surrogate markers of a cycling cholinergic thalamo-cortical drive?

peer reviewed[en] BACKGROUND: Short-latency afferent inhibition (SAI) consists of motor cortex inhibition induced by sensory afferents and depends on the excitatory effect of cholinergic thalamocortical projections on inhibitory GABAergic cortical networks. Given the electrophysiological evidence for thalamo-cortical dysrhythmia in migraine, we studied SAI in migraineurs during and between attacks and searched for correlations with somatosensory habituation, thalamocortical activation, and clinical features. METHODS: SAI was obtained by conditioning the transcranial magnetic stimulation-induced motor evoked potential (MEP) with an electric stimulus on the median nerve at the wrist with random stimulus intervals corresponding to the latency of individual somatosensory evoked potentials (SSEP) N20 plus 2, 4, 6, or 8 ms. We recruited 30 migraine without aura patients, 16 between (MO), 14 during an attack (MI), and 16 healthy volunteers (HV). We calculated the slope of the linear regression between the unconditioned MEP amplitude and the 4-conditioned MEPs as a measure of SAI. We also measured SSEP amplitude habituation, and high-frequency oscillations (HFO) as an index of thalamo-cortical activation. RESULTS: Compared to HV, SAI, SSEP habituation and early SSEP HFOs were significantly reduced in MO patients between attacks, but enhanced during an attack. There was a positive correlation between degree of SAI and amplitude of early HFOs in HV, but not in MO or MI. CONCLUSIONS: The migraine cycle-dependent variations of SAI and SSEP HFOs are further evidence that facilitatory thalamocortical activation (of GABAergic networks in the motor cortex for SAI), likely to be cholinergic, is reduced in migraine between attacks, but increased ictally